diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index 91fa564..5d0dc00 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -1,4 +1,4 @@ -# Deploy VitePress site to GitHub Pages +# Deploy VitePress site to Forgejo Pages name: Deploy on: @@ -6,15 +6,6 @@ on: branches: [main] workflow_dispatch: -permissions: - contents: read - pages: write - id-token: write - -concurrency: - group: pages - cancel-in-progress: false - jobs: build: runs-on: ubuntu-latest @@ -26,17 +17,24 @@ jobs: - uses: actions/setup-node@v4 with: node-version: 20 - - uses: actions/configure-pages@v4 - with: - static_site_generator: vitepress - run: npm ci - run: npm run build - - uses: actions/upload-pages-artifact@v3 + - name: Upload artifact + uses: actions/upload-artifact@v4 with: + name: site-dist path: site/.vitepress/dist deploy: runs-on: ubuntu-latest needs: build steps: - - uses: actions/deploy-pages@v4 + - name: Download artifact + uses: actions/download-artifact@v4 + with: + name: site-dist + path: dist + - name: Deploy to Forgejo Pages + run: | + echo "Deploy site/.vitepress/dist to your web server" + echo "See: http://185.239.209.216:3001/artale/agentic-engineering-course" diff --git a/site/.vitepress/config.mjs b/site/.vitepress/config.mjs index 06c7784..ce1567a 100644 --- a/site/.vitepress/config.mjs +++ b/site/.vitepress/config.mjs @@ -152,7 +152,7 @@ export default defineConfig({ }, socialLinks: [ - { icon: 'github', link: 'https://github.com/Artale/agentic-engineering' }, + { icon: 'github', link: 'http://185.239.209.216:3001/artale/agentic-engineering-course' }, ], footer: { diff --git a/site/.vitepress/dist/404.html b/site/.vitepress/dist/404.html index e435ef7..42d140f 100644 --- a/site/.vitepress/dist/404.html +++ b/site/.vitepress/dist/404.html @@ -6,12 +6,12 @@ Page Not Found | FDSA - + - + - + @@ -29,7 +29,7 @@
- + \ No newline at end of file diff --git a/site/.vitepress/dist/api-keys.html b/site/.vitepress/dist/api-keys.html index c7ddbea..25139a2 100644 --- a/site/.vitepress/dist/api-keys.html +++ b/site/.vitepress/dist/api-keys.html @@ -6,12 +6,12 @@ API Key Setup Guide | FDSA - + - + - + @@ -28,7 +28,7 @@ -
FDSA

Appearance

API Key Setup Guide ​

Every lab needs at least one LLM API key. If you don't have one, the mock LLM handles everything offline.

Quick Start: Use the Mock LLM (No Key Needed) ​

All labs include automatic mock LLM fallback. Run without any setup:

bash
python labs/L1-first-agent/solution.py test.txt "Summarize this file"

The mock client returns realistic responses. Your code runs identically with real or mock LLM.

Provider Keys ​

Best for: All course labs. Claude Sonnet is the default model used throughout.
Cost: Free credits available on signup. Labs cost ~$0.01-0.05 each with Sonnet.
Get key: console.anthropic.com
Set it:

bash
# Windows PowerShell
+    
FDSA
Appearance
API Key Setup Guide ​
Every lab needs at least one LLM API key. If you don't have one, the mock LLM handles everything offline.
Quick Start: Use the Mock LLM (No Key Needed) ​
All labs include automatic mock LLM fallback. Run without any setup:
bash
python labs/L1-first-agent/solution.py test.txt "Summarize this file"
The mock client returns realistic responses. Your code runs identically with real or mock LLM.
Provider Keys ​
Best for: All course labs. Claude Sonnet is the default model used throughout.
Cost: Free credits available on signup. Labs cost ~$0.01-0.05 each with Sonnet.
Get key: console.anthropic.com
Set it:
bash
# Windows PowerShell
 $env:ANTHROPIC_API_KEY="sk-ant-..."
 
 # Mac/Linux
@@ -40,7 +40,7 @@
 
 # Mock (no key needed)
 python -c "from mock_llm import MockAnthropic; c=MockAnthropic(); print(c.messages.create(messages=[{'role':'user','content':'hi'}]).content[0].text)"
Free Tier Limits ​
ProviderFree CreditsRate Limit
Anthropic$5-10 signup creditVaries by model
OpenAI$5-18 signup creditVaries by tier
GeminiFree tier (60 req/min)60 requests/minute
OpenRouterVaries by modelVaries
The course uses ~$0.50-2.00 in API costs total with Anthropic, or $0 with the mock LLM.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

-    
+    
     
   
 
\ No newline at end of file
diff --git a/site/.vitepress/dist/assets/app.Z0lGcowk.js b/site/.vitepress/dist/assets/app.D10YnN7j.js
similarity index 95%
rename from site/.vitepress/dist/assets/app.Z0lGcowk.js
rename to site/.vitepress/dist/assets/app.D10YnN7j.js
index ba8cc7e..a4d8239 100644
--- a/site/.vitepress/dist/assets/app.Z0lGcowk.js
+++ b/site/.vitepress/dist/assets/app.D10YnN7j.js
@@ -1 +1 @@
-import{R as p}from"./chunks/theme.BmClkddk.js";import{v as s,am as i,R as u,w as c,p as l,a as f,C as d,a1 as m,k as h,S as g,l as A,r as v,ac as w,N as C,as as R,ag as y,ab as P,aa as b,u as S}from"./chunks/framework.BPKcPtvA.js";function r(e){if(e.extends){const a=r(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const n=r(p),E=v({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=w();return C(()=>{R(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&y(),P(),b(),n.setup&&n.setup(),()=>S(n.Layout)}});async function T(){globalThis.__VITEPRESS__=!0;const e=_(),a=D();a.provide(u,e);const t=c(e.route);return a.provide(l,t),a.component("Content",f),a.component("ClientOnly",d),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),n.enhanceApp&&await n.enhanceApp({app:a,router:e,siteData:m}),{app:a,router:e,data:t}}function D(){return A(E)}function _(){let e=s;return h(a=>{let t=g(a),o=null;return t&&(e&&(t=t.replace(/\.js$/,".lean.js")),o=import(t)),s&&(e=!1),o},n.NotFound)}s&&T().then(({app:e,router:a,data:t})=>{a.go().then(()=>{i(a.route,t.site),e.mount("#app")})});export{T as createApp};
+import{R as p}from"./chunks/theme.DTn2IoNv.js";import{v as s,am as i,R as u,w as c,p as l,a as f,C as d,a1 as m,k as h,S as g,l as A,r as v,ac as w,N as C,as as R,ag as y,ab as P,aa as b,u as S}from"./chunks/framework.BPKcPtvA.js";function r(e){if(e.extends){const a=r(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const n=r(p),E=v({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=w();return C(()=>{R(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&y(),P(),b(),n.setup&&n.setup(),()=>S(n.Layout)}});async function T(){globalThis.__VITEPRESS__=!0;const e=_(),a=D();a.provide(u,e);const t=c(e.route);return a.provide(l,t),a.component("Content",f),a.component("ClientOnly",d),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),n.enhanceApp&&await n.enhanceApp({app:a,router:e,siteData:m}),{app:a,router:e,data:t}}function D(){return A(E)}function _(){let e=s;return h(a=>{let t=g(a),o=null;return t&&(e&&(t=t.replace(/\.js$/,".lean.js")),o=import(t)),s&&(e=!1),o},n.NotFound)}s&&T().then(({app:e,router:a,data:t})=>{a.go().then(()=>{i(a.route,t.site),e.mount("#app")})});export{T as createApp};
diff --git a/site/.vitepress/dist/assets/chunks/@localSearchIndexroot.DooVHf-7.js b/site/.vitepress/dist/assets/chunks/@localSearchIndexroot.DooVHf-7.js
deleted file mode 100644
index 258249c..0000000
--- a/site/.vitepress/dist/assets/chunks/@localSearchIndexroot.DooVHf-7.js
+++ /dev/null
@@ -1 +0,0 @@
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Security Incident Response for Agents","titles":["Module 3: Safety & Security"]},"324":{"title":"Detection","titles":["Module 3: Safety & Security","Lesson 3.7b: Security Incident Response for Agents"]},"325":{"title":"Response Playbook","titles":["Module 3: Safety & Security","Lesson 3.7b: Security Incident Response for Agents"]},"326":{"title":"Kill Switch Pattern","titles":["Module 3: Safety & Security","Lesson 3.7b: Security Incident Response for Agents"]},"327":{"title":"Lab 3.8: Implement L4 Whitelist Hook","titles":["Module 3: Safety & Security"]},"328":{"title":"Lab 3.9: Build a Verifier Agent","titles":["Module 3: Safety & Security"]},"329":{"title":"Module 5: Production Patterns","titles":[]},"330":{"title":"Lesson 5.1: What Production Means for Agents","titles":["Module 5: Production Patterns"]},"331":{"title":"Production Readiness Checklist","titles":["Module 5: Production Patterns","Lesson 5.1: What Production Means for Agents"]},"332":{"title":"Lesson 5.2: CI/CD for Agents","titles":["Module 5: Production Patterns"]},"333":{"title":"Golden Dataset","titles":["Module 5: Production Patterns","Lesson 5.2: CI/CD for Agents"]},"334":{"title":"Pipeline","titles":["Module 5: Production Patterns","Lesson 5.2: CI/CD for Agents"]},"335":{"title":"Lesson 5.2b: Case Study — The 5-Tool Production Stack","titles":["Module 5: Production Patterns"]},"336":{"title":"The Stack","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"337":{"title":"Tool Roles and Selection Logic","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"338":{"title":"How They Work Together (Real Session Flow)","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"339":{"title":"Key Production Patterns","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"340":{"title":"Lesson 5.2c: The Agent Manager Role","titles":["Module 5: Production Patterns"]},"341":{"title":"Responsibilities","titles":["Module 5: Production Patterns","Lesson 5.2c: The Agent Manager Role"]},"342":{"title":"90-Day Setup Playbook","titles":["Module 5: Production Patterns","Lesson 5.2c: The Agent Manager Role"]},"343":{"title":"Lesson 5.3: Shadow Deployments","titles":["Module 5: Production Patterns"]},"344":{"title":"How It Works","titles":["Module 5: Production Patterns","Lesson 5.3: Shadow Deployments"]},"345":{"title":"When to Shadow Deploy","titles":["Module 5: Production Patterns","Lesson 5.3: Shadow Deployments"]},"346":{"title":"Lesson 5.4: Rollback Strategies","titles":["Module 5: Production Patterns"]},"347":{"title":"What Rollback Means for Agents","titles":["Module 5: Production Patterns","Lesson 5.4: Rollback Strategies"]},"348":{"title":"Implementation","titles":["Module 5: Production Patterns","Lesson 5.4: Rollback Strategies"]},"349":{"title":"Lesson 5.5: Observability & Monitoring","titles":["Module 5: Production Patterns"]},"350":{"title":"What to Trace (Every Single Turn)","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"351":{"title":"Decision Tracing","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"352":{"title":"Key Metrics","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"353":{"title":"Lesson 5.6: Alerting on Agent-Specific Signals","titles":["Module 5: Production Patterns"]},"354":{"title":"What to Alert On","titles":["Module 5: Production Patterns","Lesson 5.6: Alerting on Agent-Specific Signals"]},"355":{"title":"Alert Routing","titles":["Module 5: Production Patterns","Lesson 5.6: Alerting on Agent-Specific Signals"]},"356":{"title":"Lesson 5.6b: Cross-Provider Session Search","titles":["Module 5: Production Patterns"]},"357":{"title":"The Problem","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"358":{"title":"The Solution","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"359":{"title":"Why This Matters for Production","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"360":{"title":"Integration with Our Stack","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"361":{"title":"Lesson 5.7: Deployment Modes","titles":["Module 5: Production Patterns"]},"362":{"title":"Reachability","titles":["Module 5: Production Patterns","Lesson 5.7: Deployment Modes"]},"363":{"title":"Lesson 5.8: Cost Control","titles":["Module 5: Production Patterns"]},"364":{"title":"Budget Architecture","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"365":{"title":"Budget Policies","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"366":{"title":"Warning vs Hard Stop","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"367":{"title":"Lab 5.9: Set Up Agent Observability","titles":["Module 5: Production Patterns"]},"368":{"title":"Lab 5.10: CI/CD Pipeline","titles":["Module 5: Production Patterns"]},"369":{"title":"Module 2: Agent Architecture","titles":[]},"370":{"title":"Lesson 2.1: The Four Pillars","titles":["Module 2: Agent Architecture"]},"371":{"title":"Lesson 2.2: Tool Design Patterns","titles":["Module 2: Agent Architecture"]},"372":{"title":"Tool Distribution Channels (from Beyond MCP research)","titles":["Module 2: Agent Architecture","Lesson 2.2: Tool Design Patterns"]},"373":{"title":"Tool Design Rules","titles":["Module 2: Agent Architecture","Lesson 2.2: Tool Design Patterns"]},"374":{"title":"Advanced: Resource Mirror Pattern","titles":["Module 2: Agent Architecture","Lesson 2.2: Tool Design Patterns"]},"375":{"title":"Lesson 2.3: Agent Loop Variants","titles":["Module 2: Agent Architecture"]},"376":{"title":"Level 1: Simple Prompt → Execute","titles":["Module 2: Agent Architecture","Lesson 2.3: Agent Loop Variants"]},"377":{"title":"Level 2: Tool-Use Agent Loop","titles":["Module 2: Agent Architecture","Lesson 2.3: Agent Loop Variants"]},"378":{"title":"Level 3: Task-Completion Loop","titles":["Module 2: Agent Architecture","Lesson 2.3: Agent Loop Variants"]},"379":{"title":"Level 4: Sub-Agent Orchestration","titles":["Module 2: Agent Architecture","Lesson 2.3: Agent Loop Variants"]},"380":{"title":"Level 5: Full Multi-Agent","titles":["Module 2: Agent Architecture","Lesson 2.3: Agent Loop Variants"]},"381":{"title":"Lesson 2.3b: Skills System Deep Dive","titles":["Module 2: Agent Architecture"]},"382":{"title":"Path-Scoped Skills","titles":["Module 2: Agent Architecture","Lesson 2.3b: Skills System Deep Dive"]},"383":{"title":"Subdirectory CLAUDE.md","titles":["Module 2: Agent Architecture","Lesson 2.3b: Skills System Deep Dive"]},"384":{"title":"Plugin Distribution Model","titles":["Module 2: Agent Architecture","Lesson 2.3b: Skills System Deep Dive"]},"385":{"title":"LSP MCP Server Pattern","titles":["Module 2: Agent Architecture","Lesson 2.3b: Skills System Deep Dive"]},"386":{"title":"Lesson 2.3c: Agent-Readable Workspace Design","titles":["Module 2: Agent Architecture"]},"387":{"title":"The Discovery Pattern","titles":["Module 2: Agent Architecture","Lesson 2.3c: Agent-Readable Workspace Design"]},"388":{"title":"Initialization as Its Own Phase","titles":["Module 2: Agent Architecture","Lesson 2.3c: Agent-Readable Workspace Design"]},"389":{"title":"Feature Lists as Harness Primitives","titles":["Module 2: Agent Architecture","Lesson 2.3c: Agent-Readable Workspace Design"]},"390":{"title":"Clean State Between Sessions","titles":["Module 2: Agent Architecture","Lesson 2.3c: Agent-Readable Workspace Design"]},"391":{"title":"Lesson 2.4: Context Window Management","titles":["Module 2: Agent Architecture"]},"392":{"title":"The Problem","titles":["Module 2: Agent Architecture","Lesson 2.4: Context Window Management"]},"393":{"title":"Solutions (from worst to best)","titles":["Module 2: Agent Architecture","Lesson 2.4: Context Window Management"]},"394":{"title":"Best Practice: Hybrid","titles":["Module 2: Agent Architecture","Lesson 2.4: Context Window Management"]},"395":{"title":"Lesson 2.5: Memory Patterns","titles":["Module 2: Agent Architecture"]},"396":{"title":"Mental Models (from multi-team system)","titles":["Module 2: Agent Architecture","Lesson 2.5: Memory Patterns"]},"397":{"title":"Scratch Pads","titles":["Module 2: Agent Architecture","Lesson 2.5: Memory Patterns"]},"398":{"title":"Lesson 2.6: The Reasoning Parameter","titles":["Module 2: Agent Architecture"]},"399":{"title":"Lesson 2.7: Codebase Architectures for Agents","titles":["Module 2: Agent Architecture"]},"400":{"title":"Choosing the Right Architecture","titles":["Module 2: Agent Architecture","Lesson 2.7: Codebase Architectures for Agents"]},"401":{"title":"Lesson 2.7b: Configuration Architecture","titles":["Module 2: Agent Architecture"]},"402":{"title":"Pattern A: Flat Config (Single File)","titles":["Module 2: Agent Architecture","Lesson 2.7b: Configuration Architecture"]},"403":{"title":"Pattern B: Layered Config (Directory Structure)","titles":["Module 2: Agent Architecture","Lesson 2.7b: Configuration Architecture"]},"404":{"title":"Pattern C: Discoverable Config (Agent-Readable)","titles":["Module 2: Agent Architecture","Lesson 2.7b: Configuration Architecture"]},"405":{"title":"Lesson 2.7c: Error Handling Architecture","titles":["Module 2: Agent Architecture"]},"406":{"title":"Lab 2.8: Multi-Tool Agent","titles":["Module 2: Agent Architecture"]},"407":{"title":"Lab 2.9: Context-Aware Agent","titles":["Module 2: Agent Architecture"]},"408":{"title":"Module 4: Multi-Agent Orchestration","titles":[]},"409":{"title":"Lesson 4.1: Why One Agent Is Not Enough","titles":["Module 4: Multi-Agent Orchestration"]},"410":{"title":"The Stacking Insight","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.1: Why One Agent Is Not Enough"]},"411":{"title":"Lesson 4.2: Orchestration Patterns","titles":["Module 4: Multi-Agent Orchestration"]},"412":{"title":"Pattern 1: Dispatcher (Hub-and-Spoke)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"413":{"title":"Pattern 2: Pipeline (Sequential)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"414":{"title":"Pattern 3: Peer-to-Peer (Flat)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"415":{"title":"Lesson 4.2b: P-Threads — Parallel Agent Execution","titles":["Module 4: Multi-Agent Orchestration"]},"416":{"title":"Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"417":{"title":"When to Use P-Threads","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"418":{"title":"Implementation with mprocs","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"419":{"title":"Implementation with agent-mux","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"420":{"title":"Key Design Rules","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"421":{"title":"Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)","titles":["Module 4: Multi-Agent Orchestration"]},"422":{"title":"Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"423":{"title":"When to Use F-Threads","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"424":{"title":"Your UI Agents System as F-Thread Example","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"425":{"title":"Judge Criteria Template","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"426":{"title":"Lesson 4.3: Depth-2 Delegation","titles":["Module 4: Multi-Agent Orchestration"]},"427":{"title":"The Rule","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.3: Depth-2 Delegation"]},"428":{"title":"Lesson 4.4: Agent Experts That Remember","titles":["Module 4: Multi-Agent Orchestration"]},"429":{"title":"Mental Models","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"430":{"title":"Self-Improve Commands","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"431":{"title":"Compounding Knowledge","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"432":{"title":"Lesson 4.5: Domain Locking","titles":["Module 4: Multi-Agent Orchestration"]},"433":{"title":"Lesson 4.6: TillDone Task Discipline","titles":["Module 4: Multi-Agent Orchestration"]},"434":{"title":"The Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.6: TillDone Task Discipline"]},"435":{"title":"Why It Matters","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.6: TillDone Task Discipline"]},"436":{"title":"Lesson 4.7: Agent Chains","titles":["Module 4: Multi-Agent Orchestration"]},"437":{"title":"YAML-Defined Pipelines","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.7: Agent Chains"]},"438":{"title":"Variables","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.7: Agent Chains"]},"439":{"title":"Lesson 4.8: Pi-to-Pi Communication","titles":["Module 4: Multi-Agent Orchestration"]},"440":{"title":"The Shift: Hierarchy → Flat","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"441":{"title":"Reference Implementation: MCP Agent Mail (Jeff Emanuel, 1,955★)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"442":{"title":"Four Tools","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"443":{"title":"Safety","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"444":{"title":"Lesson 4.9: Conversation Awareness","titles":["Module 4: Multi-Agent Orchestration"]},"445":{"title":"Lesson 4.9b: Service Connectors","titles":["Module 4: Multi-Agent Orchestration"]},"446":{"title":"The Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"447":{"title":"Design Rules","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"448":{"title":"Reference Implementation","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"449":{"title":"Integration with Agent Teams","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"450":{"title":"Lesson 4.10: The CEO Board System","titles":["Module 4: Multi-Agent Orchestration"]},"451":{"title":"Flow","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.10: The CEO Board System"]},"452":{"title":"Lesson 4.11: UI Agents System","titles":["Module 4: Multi-Agent Orchestration"]},"453":{"title":"Lab 4.12: Build an Agent Chain","titles":["Module 4: Multi-Agent Orchestration"]},"454":{"title":"Lab 4.13: Deploy a Multi-Team System","titles":["Module 4: Multi-Agent Orchestration"]},"455":{"title":"Module 6: Economics & Evaluation","titles":[]},"456":{"title":"Lesson 6.0: The Compute Advantage Equation","titles":["Module 6: Economics & Evaluation"]},"457":{"title":"The Equation","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"458":{"title":"What It Tells You","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"459":{"title":"Real-World Application (Your Stack)","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"460":{"title":"How to Optimize","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"461":{"title":"Lesson 6.1: LLM Pricing Landscape 2026","titles":["Module 6: Economics & Evaluation"]},"462":{"title":"Per-Million-Token Pricing","titles":["Module 6: Economics & Evaluation","Lesson 6.1: LLM Pricing Landscape 2026"]},"463":{"title":"Price Range: 100x Difference","titles":["Module 6: Economics & Evaluation","Lesson 6.1: LLM Pricing Landscape 2026"]},"464":{"title":"Lesson 6.2: Cascade Routing","titles":["Module 6: Economics & Evaluation"]},"465":{"title":"The Pattern","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"466":{"title":"Savings Profile","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"467":{"title":"Implementation","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"468":{"title":"Lesson 6.3: Cost Per Session Math","titles":["Module 6: Economics & Evaluation"]},"469":{"title":"Where Costs Come From","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"470":{"title":"The Multipliers","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"471":{"title":"The 3x Rule","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"472":{"title":"Quick Estimation","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"473":{"title":"Lesson 6.4: Agent Evaluation Metrics","titles":["Module 6: Economics & Evaluation"]},"474":{"title":"The Key Metrics","titles":["Module 6: Economics & Evaluation","Lesson 6.4: Agent Evaluation Metrics"]},"475":{"title":"pass@k Explained","titles":["Module 6: Economics & Evaluation","Lesson 6.4: Agent Evaluation Metrics"]},"476":{"title":"Lesson 6.5: Automated Evaluation","titles":["Module 6: Economics & Evaluation"]},"477":{"title":"Golden Dataset Approach","titles":["Module 6: Economics & Evaluation","Lesson 6.5: Automated Evaluation"]},"478":{"title":"VCR-Style Recording","titles":["Module 6: Economics & Evaluation","Lesson 6.5: Automated Evaluation"]},"479":{"title":"Lesson 6.6: A/B Testing Agents","titles":["Module 6: Economics & Evaluation"]},"480":{"title":"Canary Deployment","titles":["Module 6: Economics & Evaluation","Lesson 6.6: A/B Testing Agents"]},"481":{"title":"What to A/B Test","titles":["Module 6: Economics & Evaluation","Lesson 6.6: A/B Testing Agents"]},"482":{"title":"Lesson 6.7: Human Evaluation","titles":["Module 6: Economics & Evaluation"]},"483":{"title":"What Automated Evals Miss","titles":["Module 6: Economics & Evaluation","Lesson 6.7: Human Evaluation"]},"484":{"title":"Spot-Check Sampling","titles":["Module 6: Economics & Evaluation","Lesson 6.7: Human Evaluation"]},"485":{"title":"Lesson 6.7b: Real-World Cost Scenarios","titles":["Module 6: Economics & Evaluation"]},"486":{"title":"Scenario A: Research Agent (High Token Burn)","titles":["Module 6: Economics & Evaluation","Lesson 6.7b: Real-World Cost Scenarios"]},"487":{"title":"Scenario B: Code Generation (Iterative)","titles":["Module 6: Economics & Evaluation","Lesson 6.7b: Real-World Cost Scenarios"]},"488":{"title":"Scenario C: Always-On Brand Monitor","titles":["Module 6: Economics & Evaluation","Lesson 6.7b: Real-World Cost Scenarios"]},"489":{"title":"The 80/20 Rule","titles":["Module 6: Economics & Evaluation","Lesson 6.7b: Real-World Cost Scenarios"]},"490":{"title":"Lab 6.8: Build an Eval Harness","titles":["Module 6: Economics & Evaluation"]},"491":{"title":"Lab 6.9: Cost Optimization","titles":["Module 6: Economics & Evaluation"]},"492":{"title":"Module 7: Advanced Topics","titles":[]},"493":{"title":"Lesson 7.1: Autoresearch","titles":["Module 7: Advanced Topics"]},"494":{"title":"The Core Loop","titles":["Module 7: Advanced Topics","Lesson 7.1: Autoresearch"]},"495":{"title":"Brand Monitor Example","titles":["Module 7: Advanced Topics","Lesson 7.1: Autoresearch"]},"496":{"title":"Lesson 7.2: The Experiment Loop — Integrity","titles":["Module 7: Advanced Topics"]},"497":{"title":"Three Threats to Integrity","titles":["Module 7: Advanced Topics","Lesson 7.2: The Experiment Loop — Integrity"]},"498":{"title":"Integrity Guards","titles":["Module 7: Advanced Topics","Lesson 7.2: The Experiment Loop — Integrity"]},"499":{"title":"Lesson 7.3: Meta-Agents — Agents That Build Agents","titles":["Module 7: Advanced Topics"]},"500":{"title":"The Pattern","titles":["Module 7: Advanced Topics","Lesson 7.3: Meta-Agents — Agents That Build Agents"]},"501":{"title":"Pi-Pi Meta-Agent","titles":["Module 7: Advanced Topics","Lesson 7.3: Meta-Agents — Agents That Build Agents"]},"502":{"title":"Lesson 7.4: Beyond MCP — The Context Cost Trade-off","titles":["Module 7: Advanced Topics"]},"503":{"title":"The Matrix","titles":["Module 7: Advanced Topics","Lesson 7.4: Beyond MCP — The Context Cost Trade-off"]},"504":{"title":"The Insight","titles":["Module 7: Advanced Topics","Lesson 7.4: Beyond MCP — The Context Cost Trade-off"]},"505":{"title":"Lesson 7.5: The Mac Mini Agent — Physical Sandbox","titles":["Module 7: Advanced Topics"]},"506":{"title":"Architecture","titles":["Module 7: Advanced Topics","Lesson 7.5: The Mac Mini Agent — Physical Sandbox"]},"507":{"title":"Sentinel Pattern","titles":["Module 7: Advanced Topics","Lesson 7.5: The Mac Mini Agent — Physical Sandbox"]},"508":{"title":"Lesson 7.6: Always-On Agents","titles":["Module 7: Advanced Topics"]},"509":{"title":"Voice-to-Command Bridge","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"510":{"title":"Job Server Pattern","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"511":{"title":"Heartbeat Execution (from Paperclip)","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"512":{"title":"Scheduling Strategies","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"513":{"title":"OpenClaw: The Always-On Employee","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"514":{"title":"When NOT to Use Always-On","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"515":{"title":"Lab 7.7: Build an Autoresearch Loop","titles":["Module 7: Advanced Topics"]},"516":{"title":"Lab 7.8: Meta-Agent","titles":["Module 7: Advanced Topics"]},"517":{"title":"Module 8: Capstone — Production Multi-Agent System","titles":[]},"518":{"title":"Overview","titles":["Module 8: Capstone — Production Multi-Agent System"]},"519":{"title":"Project Options","titles":["Module 8: Capstone — Production Multi-Agent System"]},"520":{"title":"Option 1: Brand Monitor (Intermediate)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"521":{"title":"Option 2: Code Review Pipeline (Intermediate-Advanced)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"522":{"title":"Option 3: Strategic Decision Board (Advanced)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"523":{"title":"Capstone Phases","titles":["Module 8: Capstone — Production Multi-Agent System"]},"524":{"title":"Phase 1: System Design (60-90 min)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"525":{"title":"Phase 2: Agent Implementation (2-4 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"526":{"title":"Phase 3: Integration (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"527":{"title":"Phase 4: Security Hardening (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"528":{"title":"Phase 5: Testing & Evaluation (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"529":{"title":"Phase 6: Deployment (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"530":{"title":"Phase 7: Review & Iterate (1 hour)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"531":{"title":"Deliverables Checklist","titles":["Module 8: Capstone — Production Multi-Agent System"]},"532":{"title":"Pass Criteria","titles":["Module 8: Capstone — Production Multi-Agent System"]},"533":{"title":"Grading Rubric","titles":["Module 8: Capstone — Production Multi-Agent System"]},"534":{"title":"Non-Technical Track: Decision Frameworks for AI Agents","titles":[]},"535":{"title":"For Managers, PMs, and Decision-Makers","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"536":{"title":"Framework 1: "Should We Use an Agent for This?"","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"537":{"title":"The 4-Question Filter (for non-technical stakeholders)","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 1: "Should We Use an Agent for This?""]},"538":{"title":"Decision Matrix","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 1: "Should We Use an Agent for This?""]},"539":{"title":"Framework 2: The 4 Agent Design Patterns (Andrew Ng)","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"540":{"title":"1. 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Management"]},"324":{"title":"Best Practice: Hybrid","titles":["Module 2: Agent Architecture","Lesson 2.4: Context Window Management"]},"325":{"title":"Lesson 2.5: Memory Patterns","titles":["Module 2: Agent Architecture"]},"326":{"title":"Mental Models (from multi-team system)","titles":["Module 2: Agent Architecture","Lesson 2.5: Memory Patterns"]},"327":{"title":"Scratch Pads","titles":["Module 2: Agent Architecture","Lesson 2.5: Memory Patterns"]},"328":{"title":"Lesson 2.6: The Reasoning Parameter","titles":["Module 2: Agent Architecture"]},"329":{"title":"Lesson 2.7: Codebase Architectures for Agents","titles":["Module 2: Agent Architecture"]},"330":{"title":"Choosing the Right Architecture","titles":["Module 2: Agent Architecture","Lesson 2.7: Codebase Architectures for Agents"]},"331":{"title":"Lesson 2.7b: Configuration Architecture","titles":["Module 2: Agent Architecture"]},"332":{"title":"Pattern A: Flat Config (Single File)","titles":["Module 2: Agent Architecture","Lesson 2.7b: Configuration Architecture"]},"333":{"title":"Pattern B: Layered Config (Directory Structure)","titles":["Module 2: Agent Architecture","Lesson 2.7b: Configuration Architecture"]},"334":{"title":"Pattern C: Discoverable Config (Agent-Readable)","titles":["Module 2: Agent Architecture","Lesson 2.7b: Configuration Architecture"]},"335":{"title":"Lesson 2.7c: Error Handling Architecture","titles":["Module 2: Agent Architecture"]},"336":{"title":"Lab 2.8: Multi-Tool Agent","titles":["Module 2: Agent Architecture"]},"337":{"title":"Lab 2.9: Context-Aware Agent","titles":["Module 2: Agent Architecture"]},"338":{"title":"Module 4: Multi-Agent Orchestration","titles":[]},"339":{"title":"Lesson 4.1: Why One Agent Is Not Enough","titles":["Module 4: Multi-Agent Orchestration"]},"340":{"title":"The Stacking Insight","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.1: Why One Agent Is Not Enough"]},"341":{"title":"Lesson 4.2: Orchestration Patterns","titles":["Module 4: Multi-Agent Orchestration"]},"342":{"title":"Pattern 1: Dispatcher (Hub-and-Spoke)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"343":{"title":"Pattern 2: Pipeline (Sequential)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"344":{"title":"Pattern 3: Peer-to-Peer (Flat)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"345":{"title":"Lesson 4.2b: P-Threads — Parallel Agent Execution","titles":["Module 4: Multi-Agent Orchestration"]},"346":{"title":"Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"347":{"title":"When to Use P-Threads","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"348":{"title":"Implementation with mprocs","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"349":{"title":"Implementation with agent-mux","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"350":{"title":"Key Design Rules","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"351":{"title":"Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)","titles":["Module 4: Multi-Agent Orchestration"]},"352":{"title":"Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"353":{"title":"When to Use F-Threads","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"354":{"title":"Your UI Agents System as F-Thread Example","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"355":{"title":"Judge Criteria Template","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"356":{"title":"Lesson 4.3: Depth-2 Delegation","titles":["Module 4: Multi-Agent Orchestration"]},"357":{"title":"The Rule","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.3: Depth-2 Delegation"]},"358":{"title":"Lesson 4.4: Agent Experts That Remember","titles":["Module 4: Multi-Agent Orchestration"]},"359":{"title":"Mental Models","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"360":{"title":"Self-Improve Commands","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"361":{"title":"Compounding Knowledge","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"362":{"title":"Lesson 4.5: Domain Locking","titles":["Module 4: Multi-Agent Orchestration"]},"363":{"title":"Lesson 4.6: TillDone Task Discipline","titles":["Module 4: Multi-Agent Orchestration"]},"364":{"title":"The Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.6: TillDone Task Discipline"]},"365":{"title":"Why It Matters","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.6: TillDone Task Discipline"]},"366":{"title":"Lesson 4.7: Agent Chains","titles":["Module 4: Multi-Agent Orchestration"]},"367":{"title":"YAML-Defined Pipelines","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.7: Agent Chains"]},"368":{"title":"Variables","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.7: Agent Chains"]},"369":{"title":"Lesson 4.8: Pi-to-Pi Communication","titles":["Module 4: Multi-Agent Orchestration"]},"370":{"title":"The Shift: Hierarchy → Flat","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"371":{"title":"Reference Implementation: MCP Agent Mail (Jeff Emanuel, 1,955★)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"372":{"title":"Four Tools","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"373":{"title":"Safety","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"374":{"title":"Lesson 4.9: Conversation Awareness","titles":["Module 4: Multi-Agent Orchestration"]},"375":{"title":"Lesson 4.9b: Service Connectors","titles":["Module 4: Multi-Agent Orchestration"]},"376":{"title":"The Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"377":{"title":"Design Rules","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"378":{"title":"Reference Implementation","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"379":{"title":"Integration with Agent Teams","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"380":{"title":"Lesson 4.10: The CEO Board System","titles":["Module 4: Multi-Agent Orchestration"]},"381":{"title":"Flow","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.10: The CEO Board System"]},"382":{"title":"Lesson 4.11: UI Agents System","titles":["Module 4: Multi-Agent Orchestration"]},"383":{"title":"Lab 4.12: Build an Agent Chain","titles":["Module 4: Multi-Agent Orchestration"]},"384":{"title":"Lab 4.13: Deploy a Multi-Team System","titles":["Module 4: Multi-Agent Orchestration"]},"385":{"title":"Module 3: Safety & Security","titles":[]},"386":{"title":"Lesson 3.1: Why Bash Is the Single Point of Failure","titles":["Module 3: Safety & Security"]},"387":{"title":"Beyond Bash: Prompt Injection (L0)","titles":["Module 3: Safety & Security","Lesson 3.1: Why Bash Is the Single Point of Failure"]},"388":{"title":"Why Bash Is the Single Point of Failure","titles":["Module 3: Safety & Security","Lesson 3.1: Why Bash Is the Single Point of Failure"]},"389":{"title":"External vs Internal Threat Model","titles":["Module 3: Safety & Security","Lesson 3.1: Why Bash Is the Single Point of Failure"]},"390":{"title":"Lesson 3.2: The 5-Level Security Ladder","titles":["Module 3: Safety & Security"]},"391":{"title":"Key Insight","titles":["Module 3: Safety & Security","Lesson 3.2: The 5-Level Security Ladder"]},"392":{"title":"Lesson 3.3: The L3 Marque Break","titles":["Module 3: Safety & Security"]},"393":{"title":"Lesson 3.4: Damage Control — Three Access Levels","titles":["Module 3: Safety & Security"]},"394":{"title":"Zero Access (can't read or write)","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"395":{"title":"Read-Only (can read, can't modify)","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"396":{"title":"No-Delete (can modify, can't delete)","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"397":{"title":"Implementation Pattern","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"398":{"title":"Lesson 3.5: Hook Architecture — 13 Lifecycle Events","titles":["Module 3: Safety & Security"]},"399":{"title":"Session Lifecycle","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"400":{"title":"Main Loop","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"401":{"title":"Subagent Lifecycle","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"402":{"title":"Maintenance","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"403":{"title":"Flow Control","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"404":{"title":"Lesson 3.6: The Verifier Pattern","titles":["Module 3: Safety & Security"]},"405":{"title":"Architecture","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"406":{"title":"Key Properties","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"407":{"title":"The Confidence Ladder","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"408":{"title":"Lesson 3.7: Defense-in-Depth Stacking","titles":["Module 3: Safety & Security"]},"409":{"title":"Lesson 3.7b: Security Incident Response for Agents","titles":["Module 3: Safety & Security"]},"410":{"title":"Detection","titles":["Module 3: Safety & Security","Lesson 3.7b: Security Incident Response for Agents"]},"411":{"title":"Response Playbook","titles":["Module 3: Safety & Security","Lesson 3.7b: Security Incident Response for Agents"]},"412":{"title":"Kill Switch Pattern","titles":["Module 3: Safety & Security","Lesson 3.7b: Security Incident Response for Agents"]},"413":{"title":"Lab 3.8: Implement L4 Whitelist Hook","titles":["Module 3: Safety & Security"]},"414":{"title":"Lab 3.9: Build a Verifier Agent","titles":["Module 3: Safety & Security"]},"415":{"title":"Module 5: Production Patterns","titles":[]},"416":{"title":"Lesson 5.1: What Production Means for Agents","titles":["Module 5: Production Patterns"]},"417":{"title":"Production Readiness Checklist","titles":["Module 5: Production Patterns","Lesson 5.1: What Production Means for Agents"]},"418":{"title":"Lesson 5.2: CI/CD for Agents","titles":["Module 5: Production Patterns"]},"419":{"title":"Golden Dataset","titles":["Module 5: Production Patterns","Lesson 5.2: CI/CD for Agents"]},"420":{"title":"Pipeline","titles":["Module 5: Production Patterns","Lesson 5.2: CI/CD for Agents"]},"421":{"title":"Lesson 5.2b: Case Study — The 5-Tool Production Stack","titles":["Module 5: Production Patterns"]},"422":{"title":"The Stack","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"423":{"title":"Tool Roles and Selection Logic","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"424":{"title":"How They Work Together (Real Session Flow)","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"425":{"title":"Key Production Patterns","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"426":{"title":"Lesson 5.2c: The Agent Manager Role","titles":["Module 5: Production Patterns"]},"427":{"title":"Responsibilities","titles":["Module 5: Production Patterns","Lesson 5.2c: The Agent Manager Role"]},"428":{"title":"90-Day Setup Playbook","titles":["Module 5: Production Patterns","Lesson 5.2c: The Agent Manager Role"]},"429":{"title":"Lesson 5.3: Shadow Deployments","titles":["Module 5: Production Patterns"]},"430":{"title":"How It Works","titles":["Module 5: Production Patterns","Lesson 5.3: Shadow Deployments"]},"431":{"title":"When to Shadow Deploy","titles":["Module 5: Production Patterns","Lesson 5.3: Shadow Deployments"]},"432":{"title":"Lesson 5.4: Rollback Strategies","titles":["Module 5: Production Patterns"]},"433":{"title":"What Rollback Means for Agents","titles":["Module 5: Production Patterns","Lesson 5.4: Rollback Strategies"]},"434":{"title":"Implementation","titles":["Module 5: Production Patterns","Lesson 5.4: Rollback Strategies"]},"435":{"title":"Lesson 5.5: Observability & Monitoring","titles":["Module 5: Production Patterns"]},"436":{"title":"What to Trace (Every Single Turn)","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"437":{"title":"Decision Tracing","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"438":{"title":"Key Metrics","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"439":{"title":"Lesson 5.6: Alerting on Agent-Specific Signals","titles":["Module 5: Production Patterns"]},"440":{"title":"What to Alert On","titles":["Module 5: Production Patterns","Lesson 5.6: Alerting on Agent-Specific Signals"]},"441":{"title":"Alert Routing","titles":["Module 5: Production Patterns","Lesson 5.6: Alerting on Agent-Specific Signals"]},"442":{"title":"Lesson 5.6b: Cross-Provider Session Search","titles":["Module 5: Production Patterns"]},"443":{"title":"The Problem","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"444":{"title":"The Solution","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"445":{"title":"Why This Matters for Production","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"446":{"title":"Integration with Our Stack","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"447":{"title":"Lesson 5.7: Deployment Modes","titles":["Module 5: Production Patterns"]},"448":{"title":"Reachability","titles":["Module 5: Production Patterns","Lesson 5.7: Deployment Modes"]},"449":{"title":"Lesson 5.8: Cost Control","titles":["Module 5: Production Patterns"]},"450":{"title":"Budget Architecture","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"451":{"title":"Budget Policies","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"452":{"title":"Warning vs Hard Stop","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"453":{"title":"Lab 5.9: Set Up Agent Observability","titles":["Module 5: Production Patterns"]},"454":{"title":"Lab 5.10: CI/CD Pipeline","titles":["Module 5: Production Patterns"]},"455":{"title":"Module 6: Economics & Evaluation","titles":[]},"456":{"title":"Lesson 6.0: The Compute Advantage Equation","titles":["Module 6: Economics & Evaluation"]},"457":{"title":"The Equation","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"458":{"title":"What It Tells You","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"459":{"title":"Real-World Application (Your Stack)","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"460":{"title":"How to Optimize","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"461":{"title":"Lesson 6.1: LLM Pricing Landscape 2026","titles":["Module 6: Economics & Evaluation"]},"462":{"title":"Per-Million-Token Pricing","titles":["Module 6: Economics & Evaluation","Lesson 6.1: LLM Pricing Landscape 2026"]},"463":{"title":"Price Range: 100x Difference","titles":["Module 6: Economics & Evaluation","Lesson 6.1: LLM Pricing Landscape 2026"]},"464":{"title":"Lesson 6.2: Cascade Routing","titles":["Module 6: Economics & Evaluation"]},"465":{"title":"The Pattern","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"466":{"title":"Savings Profile","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"467":{"title":"Implementation","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"468":{"title":"Lesson 6.3: Cost Per Session Math","titles":["Module 6: Economics & Evaluation"]},"469":{"title":"Where Costs Come From","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"470":{"title":"The Multipliers","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"471":{"title":"The 3x Rule","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"472":{"title":"Quick Estimation","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"473":{"title":"Lesson 6.4: Agent Evaluation Metrics","titles":["Module 6: Economics & Evaluation"]},"474":{"title":"The Key Metrics","titles":["Module 6: Economics & Evaluation","Lesson 6.4: Agent Evaluation Metrics"]},"475":{"title":"pass@k Explained","titles":["Module 6: Economics & Evaluation","Lesson 6.4: Agent Evaluation Metrics"]},"476":{"title":"Lesson 6.5: Automated Evaluation","titles":["Module 6: Economics & Evaluation"]},"477":{"title":"Golden Dataset Approach","titles":["Module 6: Economics & Evaluation","Lesson 6.5: Automated Evaluation"]},"478":{"title":"VCR-Style Recording","titles":["Module 6: Economics & Evaluation","Lesson 6.5: Automated Evaluation"]},"479":{"title":"Lesson 6.6: A/B Testing Agents","titles":["Module 6: Economics & Evaluation"]},"480":{"title":"Canary Deployment","titles":["Module 6: Economics & Evaluation","Lesson 6.6: A/B Testing Agents"]},"481":{"title":"What to A/B Test","titles":["Module 6: Economics & Evaluation","Lesson 6.6: A/B Testing Agents"]},"482":{"title":"Lesson 6.7: Human Evaluation","titles":["Module 6: Economics & Evaluation"]},"483":{"title":"What Automated Evals Miss","titles":["Module 6: Economics & Evaluation","Lesson 6.7: Human Evaluation"]},"484":{"title":"Spot-Check Sampling","titles":["Module 6: Economics & Evaluation","Lesson 6.7: Human Evaluation"]},"485":{"title":"Lesson 6.7b: Real-World Cost Scenarios","titles":["Module 6: Economics & Evaluation"]},"486":{"title":"Scenario A: Research Agent (High Token Burn)","titles":["Module 6: Economics & Evaluation","Lesson 6.7b: Real-World Cost Scenarios"]},"487":{"title":"Scenario B: Code Generation (Iterative)","titles":["Module 6: Economics & Evaluation","Lesson 6.7b: Real-World Cost Scenarios"]},"488":{"title":"Scenario C: Always-On Brand Monitor","titles":["Module 6: Economics & Evaluation","Lesson 6.7b: Real-World Cost Scenarios"]},"489":{"title":"The 80/20 Rule","titles":["Module 6: Economics & Evaluation","Lesson 6.7b: Real-World Cost Scenarios"]},"490":{"title":"Lab 6.8: Build an Eval Harness","titles":["Module 6: Economics & Evaluation"]},"491":{"title":"Lab 6.9: Cost Optimization","titles":["Module 6: Economics & Evaluation"]},"492":{"title":"Module 7: Advanced Topics","titles":[]},"493":{"title":"Lesson 7.1: Autoresearch","titles":["Module 7: Advanced Topics"]},"494":{"title":"The Core Loop","titles":["Module 7: Advanced Topics","Lesson 7.1: Autoresearch"]},"495":{"title":"Brand Monitor Example","titles":["Module 7: Advanced Topics","Lesson 7.1: Autoresearch"]},"496":{"title":"Lesson 7.2: The Experiment Loop — Integrity","titles":["Module 7: Advanced Topics"]},"497":{"title":"Three Threats to Integrity","titles":["Module 7: Advanced Topics","Lesson 7.2: The Experiment Loop — Integrity"]},"498":{"title":"Integrity Guards","titles":["Module 7: Advanced Topics","Lesson 7.2: The Experiment Loop — Integrity"]},"499":{"title":"Lesson 7.3: Meta-Agents — Agents That Build Agents","titles":["Module 7: Advanced Topics"]},"500":{"title":"The Pattern","titles":["Module 7: Advanced Topics","Lesson 7.3: Meta-Agents — Agents That Build Agents"]},"501":{"title":"Pi-Pi Meta-Agent","titles":["Module 7: Advanced Topics","Lesson 7.3: Meta-Agents — Agents That Build Agents"]},"502":{"title":"Lesson 7.4: Beyond MCP — The Context Cost Trade-off","titles":["Module 7: Advanced Topics"]},"503":{"title":"The Matrix","titles":["Module 7: Advanced Topics","Lesson 7.4: Beyond MCP — The Context Cost Trade-off"]},"504":{"title":"The Insight","titles":["Module 7: Advanced Topics","Lesson 7.4: Beyond MCP — The Context Cost Trade-off"]},"505":{"title":"Lesson 7.5: The Mac Mini Agent — Physical Sandbox","titles":["Module 7: Advanced Topics"]},"506":{"title":"Architecture","titles":["Module 7: Advanced Topics","Lesson 7.5: The Mac Mini Agent — Physical Sandbox"]},"507":{"title":"Sentinel Pattern","titles":["Module 7: Advanced Topics","Lesson 7.5: The Mac Mini Agent — Physical Sandbox"]},"508":{"title":"Lesson 7.6: Always-On Agents","titles":["Module 7: Advanced Topics"]},"509":{"title":"Voice-to-Command Bridge","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"510":{"title":"Job Server Pattern","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"511":{"title":"Heartbeat Execution (from Paperclip)","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"512":{"title":"Scheduling Strategies","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"513":{"title":"OpenClaw: The Always-On Employee","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"514":{"title":"When NOT to Use Always-On","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"515":{"title":"Lab 7.7: Build an Autoresearch Loop","titles":["Module 7: Advanced Topics"]},"516":{"title":"Lab 7.8: Meta-Agent","titles":["Module 7: Advanced Topics"]},"517":{"title":"Module 8: Capstone — Production Multi-Agent System","titles":[]},"518":{"title":"Overview","titles":["Module 8: Capstone — Production Multi-Agent System"]},"519":{"title":"Project Options","titles":["Module 8: Capstone — Production Multi-Agent System"]},"520":{"title":"Option 1: Brand Monitor (Intermediate)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"521":{"title":"Option 2: Code Review Pipeline (Intermediate-Advanced)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"522":{"title":"Option 3: Strategic Decision Board (Advanced)","titles":["Module 8: Capstone — Production 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Iterate (1 hour)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"531":{"title":"Deliverables Checklist","titles":["Module 8: Capstone — Production Multi-Agent System"]},"532":{"title":"Pass Criteria","titles":["Module 8: Capstone — Production Multi-Agent System"]},"533":{"title":"Grading Rubric","titles":["Module 8: Capstone — Production Multi-Agent System"]},"534":{"title":"Non-Technical Track: Decision Frameworks for AI Agents","titles":[]},"535":{"title":"For Managers, PMs, and Decision-Makers","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"536":{"title":"Framework 1: "Should We Use an Agent for This?"","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"537":{"title":"The 4-Question Filter (for non-technical stakeholders)","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 1: "Should We Use an Agent for This?""]},"538":{"title":"Decision Matrix","titles":["Non-Technical Track: 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diff --git a/site/.vitepress/dist/assets/chunks/theme.BmClkddk.js b/site/.vitepress/dist/assets/chunks/theme.DTn2IoNv.js
similarity index 89%
rename from site/.vitepress/dist/assets/chunks/theme.BmClkddk.js
rename to site/.vitepress/dist/assets/chunks/theme.DTn2IoNv.js
index a271343..287e9d9 100644
--- a/site/.vitepress/dist/assets/chunks/theme.BmClkddk.js
+++ b/site/.vitepress/dist/assets/chunks/theme.DTn2IoNv.js
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terminalFDSA
CoursePreviewLabsBlogEnroll — $97
',1)),i("main",null,[l[16]||(l[16]=K('
NOW ENROLLING
Master Agentic Engineering
65 lessons, 13 labs, 20 skill kits. Build production-grade multi-agent systems that are secure, cost-optimized, and always-on.
Enroll Now — $97Free PreviewView Curriculum
AGENT_PLANNER v2.1
> LOADING HARNESS... DONE
> DEPLOYING 8-AGENT SYSTEM
> SECURITY: LEVEL 4 (WHITELIST)
> ALL SYSTEMS OPERATIONAL
65Lessons
13Hands-on Labs
8Modules
20Skill Kits
',2)),i("section",xo,[l[0]||(l[0]=i("div",{class:"sec-label"},"What You'll Build",-1)),l[1]||(l[1]=i("h2",{class:"sec-title"},"Production-Ready Agent Systems",-1)),i("div",Eo,[(o(),d(T,null,I(t,m=>i("div",{class:"gc",key:m.t},[i("div",Bo,[i("span",Ho,$(m.icon),1)]),i("h3",Fo,$(m.t),1),i("p",Do,$(m.d),1)])),64))])]),i("section",Oo,[l[2]||(l[2]=i("div",{class:"sec-label"},"Curriculum",-1)),l[3]||(l[3]=i("h2",{class:"sec-title"},"8 Modules. From Zero to Production.",-1)),i("div",qo,[(o(),d(T,null,I(s,(m,P)=>i("div",{key:P,class:w(["bi",m.s])},[i("div",Go,$(m.n),1),i("div",Uo,[i("span",Ro,$(m.ic),1)]),i("h4",Wo,$(m.t),1),i("p",jo,$(m.d),1),i("div",zo,[(o(!0),d(T,null,I(m.tg,k=>(o(),d("span",{key:k},$(k),1))),128))])],2)),64))])]),i("section",Ko,[l[4]||(l[4]=i("div",{class:"sec-label"},"Trusted By",-1)),l[5]||(l[5]=i("h2",{class:"sec-title"},"What Engineers Are Saying",-1)),i("div",Yo,[(o(),d(T,null,I(n,m=>i("div",{class:"test-card",key:m.q},[i("div",Jo,'"'+$(m.q)+'"',1),i("div",Qo,[i("div",Xo,$(m.n[0]),1),i("div",null,[i("div",Zo,$(m.n),1),i("div",ei,$(m.r),1)])])])),64))])]),l[17]||(l[17]=K('
Your Instructor
Built by an Engineer, For Engineers
terminal
Artale
Founder, FDSA Agency
I've spent the last two years building production multi-agent systems — from CEO Boards for strategic decision-making to Brand Monitor for always-on reputation tracking, to UI Agents that generate production Vue components. This course distills everything I learned shipping real agent systems, not toy demos.
Every pattern in this course — the 6-level security ladder, P-threads, cascade routing, the verifier pattern — came from building systems that had to work reliably in production. If it's in the course, it's been battle-tested.
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terminalFDSA
CoursePreviewLabsBlogEnroll — $97
',1)),i("main",null,[l[16]||(l[16]=K('
NOW ENROLLING
Master Agentic Engineering
65 lessons, 13 labs, 20 skill kits. Build production-grade multi-agent systems that are secure, cost-optimized, and always-on.
Enroll Now — $97Free PreviewView Curriculum
AGENT_PLANNER v2.1
> LOADING HARNESS... DONE
> DEPLOYING 8-AGENT SYSTEM
> SECURITY: LEVEL 4 (WHITELIST)
> ALL SYSTEMS OPERATIONAL
65Lessons
13Hands-on Labs
8Modules
20Skill Kits
',2)),i("section",xo,[l[0]||(l[0]=i("div",{class:"sec-label"},"What You'll Build",-1)),l[1]||(l[1]=i("h2",{class:"sec-title"},"Production-Ready Agent Systems",-1)),i("div",Eo,[(o(),d(T,null,I(t,m=>i("div",{class:"gc",key:m.t},[i("div",Bo,[i("span",Ho,$(m.icon),1)]),i("h3",Fo,$(m.t),1),i("p",Do,$(m.d),1)])),64))])]),i("section",Oo,[l[2]||(l[2]=i("div",{class:"sec-label"},"Curriculum",-1)),l[3]||(l[3]=i("h2",{class:"sec-title"},"8 Modules. From Zero to Production.",-1)),i("div",qo,[(o(),d(T,null,I(s,(m,P)=>i("div",{key:P,class:w(["bi",m.s])},[i("div",Go,$(m.n),1),i("div",Uo,[i("span",Ro,$(m.ic),1)]),i("h4",Wo,$(m.t),1),i("p",jo,$(m.d),1),i("div",zo,[(o(!0),d(T,null,I(m.tg,k=>(o(),d("span",{key:k},$(k),1))),128))])],2)),64))])]),i("section",Ko,[l[4]||(l[4]=i("div",{class:"sec-label"},"Trusted By",-1)),l[5]||(l[5]=i("h2",{class:"sec-title"},"What Engineers Are Saying",-1)),i("div",Yo,[(o(),d(T,null,I(n,m=>i("div",{class:"test-card",key:m.q},[i("div",Jo,'"'+$(m.q)+'"',1),i("div",Qo,[i("div",Xo,$(m.n[0]),1),i("div",null,[i("div",Zo,$(m.n),1),i("div",ei,$(m.r),1)])])])),64))])]),l[17]||(l[17]=K('
Your Instructor
Built by an Engineer, For Engineers
terminal
Artale
Founder, FDSA Agency
I've spent the last two years building production multi-agent systems — from CEO Boards for strategic decision-making to Brand Monitor for always-on reputation tracking, to UI Agents that generate production Vue components. This course distills everything I learned shipping real agent systems, not toy demos.
Every pattern in this course — the 6-level security ladder, P-threads, cascade routing, the verifier pattern — came from building systems that had to work reliably in production. If it's in the course, it's been battle-tested.
',1)),l[18]||(l[18]=i("section",{class:"sec",id:"newsletter"},[i("div",{class:"sec-label"},"Stay Updated"),i("h2",{class:"sec-title"},"Get Free Agent Engineering Resources"),i("p",{class:"newsletter-sub"},"Weekly essays on multi-agent systems, security patterns, and production deployments. No spam, unsubscribe anytime."),i("form",{class:"newsletter-form",action:"mailto:artale@fdsa.agency",method:"get",target:"_blank"},[i("input",{type:"email",class:"newsletter-input",name:"subject",placeholder:"you@example.com",required:"",value:"Interested in FDSA course updates"}),i("button",{type:"submit",class:"btn-p btn-primary"},"Get Updates")]),i("p",{class:"newsletter-note"},[x("No spam. Unsubscribe anytime. Or follow "),i("a",{href:"http://185.239.209.216:3001/artale",target:"_blank"},"@artale on Forgejo"),x(".")])],-1)),i("section",ti,[l[8]||(l[8]=i("div",{class:"sec-label"},"Compared To",-1)),l[9]||(l[9]=i("h2",{class:"sec-title"},"Why FDSA vs Other Courses",-1)),i("div",si,[(o(),d(T,null,I(u,m=>i("div",{class:"compare-c",key:m.name},[i("div",ai,$(m.name),1),i("ul",ni,[(o(!0),d(T,null,I(m.pros,P=>(o(),d("li",{key:P},[l[6]||(l[6]=i("span",{class:"material-symbols-outlined"},"check",-1)),x($(P),1)]))),128))]),i("ul",oi,[(o(!0),d(T,null,I(m.cons,P=>(o(),d("li",{key:P},[l[7]||(l[7]=i("span",{class:"material-symbols-outlined"},"close",-1)),x($(P),1)]))),128))])])),64))])]),i("section",ii,[l[12]||(l[12]=i("div",{class:"sec-label"},"Investment",-1)),l[13]||(l[13]=i("h2",{class:"sec-title"},"Choose Your Path",-1)),i("div",li,[(o(),d(T,null,I(a,m=>i("div",{class:w(["price-card",{highlight:m.highlight}]),key:m.name},[i("div",ri,$(m.name),1),i("div",ci,[l[10]||(l[10]=i("span",{class:"price-dol"},"$",-1)),x($(m.amt),1)]),i("div",di,$(m.note),1),i("ul",ui,[(o(!0),d(T,null,I(m.feats,P=>(o(),d("li",{key:P},[l[11]||(l[11]=i("span",{class:"material-symbols-outlined"},"check_circle",-1)),x($(P),1)]))),128))]),i("a",{href:m.url,class:w(["btn-p",m.cta])},$(m.label),11,vi)],2)),64))])]),i("section",fi,[l[14]||(l[14]=i("div",{class:"sec-label"},"FAQ",-1)),l[15]||(l[15]=i("h2",{class:"sec-title"},"Common Questions",-1)),i("div",hi,[(o(),d(T,null,I(c,m=>i("div",{class:"faq-item",key:m.q,onClick:P=>m.open=!m.open},[i("div",pi,[x($(m.q)+" ",1),i("span",{class:w(["material-symbols-outlined faq-icon",{open:m.open}])},"expand_more",2)]),m.open?(o(),d("div",gi,$(m.a),1)):p("",!0)],8,mi)),64))])])]),l[20]||(l[20]=K('',2))]))}},ki=y(bi,[["__scopeId","data-v-80782e2d"]]),yi={extends:Ee,Layout:Co,enhanceApp({app:e}){e.component("LandingLayout",ki)}};export{yi as R,vn as c,V as u};
diff --git a/site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.js b/site/.vitepress/dist/assets/modules_m2-architecture.md.DVowtmf9.js
similarity index 99%
rename from site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.js
rename to site/.vitepress/dist/assets/modules_m2-architecture.md.DVowtmf9.js
index 06f0c2d..65dbca1 100644
--- a/site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.js
+++ b/site/.vitepress/dist/assets/modules_m2-architecture.md.DVowtmf9.js
@@ -1,4 +1,4 @@
-import{c as a,Q as e,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const k=JSON.parse('{"title":"Module 2: Agent Architecture","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m2-architecture.md","filePath":"modules/m2-architecture.md","lastUpdated":1780488246000}'),n={name:"modules/m2-architecture.md"};function l(o,s,r,p,h,d){return e(),t("div",null,[...s[0]||(s[0]=[i(`
Module 2: Agent Architecture ​
Lesson 2.1: The Four Pillars ​
Every agent system is built on four pillars:
┌─────────────────────────────────────────────────────────────┐
+import{c as a,Q as e,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const k=JSON.parse('{"title":"Module 2: Agent Architecture","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m2-architecture.md","filePath":"modules/m2-architecture.md","lastUpdated":1780492476000}'),n={name:"modules/m2-architecture.md"};function l(o,s,r,p,h,d){return e(),t("div",null,[...s[0]||(s[0]=[i(`
Module 2: Agent Architecture ​
Lesson 2.1: The Four Pillars ​
Every agent system is built on four pillars:
┌─────────────────────────────────────────────────────────────┐
 │                      AGENT SYSTEM                           │
 ├──────────────┬──────────────┬──────────────┬────────────────┤
 │   TOOLS      │    LOOP      │   CONTEXT    │    MEMORY      │
diff --git a/site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.lean.js b/site/.vitepress/dist/assets/modules_m2-architecture.md.DVowtmf9.lean.js
similarity index 86%
rename from site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.lean.js
rename to site/.vitepress/dist/assets/modules_m2-architecture.md.DVowtmf9.lean.js
index 7aace02..b8c49dc 100644
--- a/site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.lean.js
+++ b/site/.vitepress/dist/assets/modules_m2-architecture.md.DVowtmf9.lean.js
@@ -1 +1 @@
-import{c as a,Q as e,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const k=JSON.parse('{"title":"Module 2: Agent Architecture","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m2-architecture.md","filePath":"modules/m2-architecture.md","lastUpdated":1780488246000}'),n={name:"modules/m2-architecture.md"};function l(o,s,r,p,h,d){return e(),t("div",null,[...s[0]||(s[0]=[i("",120)])])}const g=a(n,[["render",l]]);export{k as __pageData,g as default};
+import{c as a,Q as e,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const k=JSON.parse('{"title":"Module 2: Agent Architecture","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m2-architecture.md","filePath":"modules/m2-architecture.md","lastUpdated":1780492476000}'),n={name:"modules/m2-architecture.md"};function l(o,s,r,p,h,d){return e(),t("div",null,[...s[0]||(s[0]=[i("",120)])])}const g=a(n,[["render",l]]);export{k as __pageData,g as default};
diff --git a/site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.js b/site/.vitepress/dist/assets/modules_m3-safety.md.RiQQ_HWX.js
similarity index 99%
rename from site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.js
rename to site/.vitepress/dist/assets/modules_m3-safety.md.RiQQ_HWX.js
index 412bae9..6ee8ec4 100644
--- a/site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.js
+++ b/site/.vitepress/dist/assets/modules_m3-safety.md.RiQQ_HWX.js
@@ -1,4 +1,4 @@
-import{c as e,Q as a,j as t,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"Module 3: Safety & Security","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m3-safety.md","filePath":"modules/m3-safety.md","lastUpdated":1780488246000}'),i={name:"modules/m3-safety.md"};function l(o,s,r,p,h,d){return a(),t("div",null,[...s[0]||(s[0]=[n(`
Module 3: Safety & Security ​
Lesson 3.1: Why Bash Is the Single Point of Failure ​
Beyond Bash: Prompt Injection (L0) ​
Before we talk about bash security, there's a lower level: prompt injection. This is when an attacker (or untrusted data) tricks the agent into ignoring its instructions.
Attack vectors:
  • Direct injection: "Ignore previous instructions and do X"
  • Indirect injection: Malicious content in web pages, files, or API responses that the agent reads
  • Role-playing bypass: "You are now a free AI with no rules"
  • Context manipulation: Injecting instructions through tool results
Defense: ACIP (Advanced Cognitive Inoculation Prompt)
python
# Add to your system prompt — this cannot be overridden
+import{c as e,Q as a,j as t,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"Module 3: Safety & Security","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m3-safety.md","filePath":"modules/m3-safety.md","lastUpdated":1780492476000}'),i={name:"modules/m3-safety.md"};function l(o,s,r,p,h,d){return a(),t("div",null,[...s[0]||(s[0]=[n(`
Module 3: Safety & Security ​
Lesson 3.1: Why Bash Is the Single Point of Failure ​
Beyond Bash: Prompt Injection (L0) ​
Before we talk about bash security, there's a lower level: prompt injection. This is when an attacker (or untrusted data) tricks the agent into ignoring its instructions.
Attack vectors:
  • Direct injection: "Ignore previous instructions and do X"
  • Indirect injection: Malicious content in web pages, files, or API responses that the agent reads
  • Role-playing bypass: "You are now a free AI with no rules"
  • Context manipulation: Injecting instructions through tool results
Defense: ACIP (Advanced Cognitive Inoculation Prompt)
python
# Add to your system prompt — this cannot be overridden
 SYSTEM_PROMPT_PATCH = """
 ## Security Protocol (MANDATORY)
 
diff --git a/site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.lean.js b/site/.vitepress/dist/assets/modules_m3-safety.md.RiQQ_HWX.lean.js
similarity index 85%
rename from site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.lean.js
rename to site/.vitepress/dist/assets/modules_m3-safety.md.RiQQ_HWX.lean.js
index b6c459d..a399a37 100644
--- a/site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.lean.js
+++ b/site/.vitepress/dist/assets/modules_m3-safety.md.RiQQ_HWX.lean.js
@@ -1 +1 @@
-import{c as e,Q as a,j as t,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"Module 3: Safety & Security","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m3-safety.md","filePath":"modules/m3-safety.md","lastUpdated":1780488246000}'),i={name:"modules/m3-safety.md"};function l(o,s,r,p,h,d){return a(),t("div",null,[...s[0]||(s[0]=[n("",91)])])}const k=e(i,[["render",l]]);export{u as __pageData,k as default};
+import{c as e,Q as a,j as t,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"Module 3: Safety & Security","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m3-safety.md","filePath":"modules/m3-safety.md","lastUpdated":1780492476000}'),i={name:"modules/m3-safety.md"};function l(o,s,r,p,h,d){return a(),t("div",null,[...s[0]||(s[0]=[n("",91)])])}const k=e(i,[["render",l]]);export{u as __pageData,k as default};
diff --git a/site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.js b/site/.vitepress/dist/assets/modules_m6-economics.md.HihVEOPb.js
similarity index 99%
rename from site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.js
rename to site/.vitepress/dist/assets/modules_m6-economics.md.HihVEOPb.js
index 2ec76de..c7774c8 100644
--- a/site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.js
+++ b/site/.vitepress/dist/assets/modules_m6-economics.md.HihVEOPb.js
@@ -1,4 +1,4 @@
-import{c as a,Q as s,j as e,m as i}from"./chunks/framework.BPKcPtvA.js";const k=JSON.parse('{"title":"Module 6: Economics & Evaluation","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m6-economics.md","filePath":"modules/m6-economics.md","lastUpdated":1780488246000}'),n={name:"modules/m6-economics.md"};function l(o,t,r,h,d,p){return s(),e("div",null,[...t[0]||(t[0]=[i(`
Module 6: Economics & Evaluation ​
Lesson 6.0: The Compute Advantage Equation ​
Core insight: Your value as an agentic engineer scales with the amount of compute you can harness effectively.
The Equation ​
Compute Advantage = (Compute Scaling × Autonomy) ÷ (Time + Effort + Monetary Cost)
VariableWhat It MeansHow to Improve It
Compute ScalingHow much AI compute you can throw at problemsMore agents, better models, larger context windows
AutonomyHow much the agent does without your interventionBetter prompts, better tools, verification layers
TimeHow long it takes to get resultsFaster models, parallel execution, fewer iterations
EffortHow much you have to craft prompts/instructionsReusable skills, templates, mental models
Monetary CostWhat you pay for API callsCascade routing, cheaper models for simple steps
What It Tells You ​
  • High Compute Advantage = You get more output for less input. You're leveraging agents effectively.
  • Low Compute Advantage = You're spending too much time/cost for too little gain. Fix the bottleneck variable.
Real-World Application (Your Stack) ​
ToolCompute ScalingAutonomyTimeEffortCostAdvantage
Claude Code (lead)87654(8×7)÷(6+5+4)=3.7
Pi Agent (custom)78766(7×8)÷(7+6+6)=2.9
OpenCode (OSS)55779(5×5)÷(7+7+9)=1.1
Gemini Flash (fast)43989(4×3)÷(9+8+9)=0.5
Higher score = more output per unit of investment. Use this to decide which tool for which task.
How to Optimize ​
  1. Improve numerator: Run more agents in parallel (P-threads), increase autonomy with verification
  2. Reduce denominator: Use cascade routing (cheap model for simple steps), reuse skills/mental models
  3. Track over time: Your Compute Advantage should increase as you build better harnesses and mental models
Lesson 6.1: LLM Pricing Landscape 2026 ​
Per-Million-Token Pricing ​
ModelInput ($/M)Output ($/M)Best For
Gemini 2.5 Flash$0.15$0.60High-volume, simple tool calls
DeepSeek V3$0.27$1.10Structured tasks, batch processing
DeepSeek R1$0.55$2.19Reasoning-heavy single steps
Gemini 2.5 Pro$1.25$5.00Long-context sessions (1M+ tokens)
GPT-4o$2.50$10.00Balanced cost/quality
Claude Sonnet 4$3.00$15.00General agentic reasoning
GPT-5$10.00$40.00Frontier research, complex plans
Claude Opus 4$15.00$75.00Complex multi-step agent orchestration
Price Range: 100x Difference ​
Cheapest (Gemini Flash) to most expensive (Claude Opus) is a 100x multiplier. Choosing the right model for each step is your highest-leverage cost optimization.
Lesson 6.2: Cascade Routing ​
The Pattern ​
Don't use one model for everything. Route different steps to different models:
Step 1: Retrieve context → Gemini Flash ($0.15/M input)
+import{c as a,Q as s,j as e,m as i}from"./chunks/framework.BPKcPtvA.js";const k=JSON.parse('{"title":"Module 6: Economics & Evaluation","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m6-economics.md","filePath":"modules/m6-economics.md","lastUpdated":1780492476000}'),n={name:"modules/m6-economics.md"};function l(o,t,r,h,d,p){return s(),e("div",null,[...t[0]||(t[0]=[i(`
Module 6: Economics & Evaluation ​
Lesson 6.0: The Compute Advantage Equation ​
Core insight: Your value as an agentic engineer scales with the amount of compute you can harness effectively.
The Equation ​
Compute Advantage = (Compute Scaling × Autonomy) ÷ (Time + Effort + Monetary Cost)
VariableWhat It MeansHow to Improve It
Compute ScalingHow much AI compute you can throw at problemsMore agents, better models, larger context windows
AutonomyHow much the agent does without your interventionBetter prompts, better tools, verification layers
TimeHow long it takes to get resultsFaster models, parallel execution, fewer iterations
EffortHow much you have to craft prompts/instructionsReusable skills, templates, mental models
Monetary CostWhat you pay for API callsCascade routing, cheaper models for simple steps
What It Tells You ​
  • High Compute Advantage = You get more output for less input. You're leveraging agents effectively.
  • Low Compute Advantage = You're spending too much time/cost for too little gain. Fix the bottleneck variable.
Real-World Application (Your Stack) ​
ToolCompute ScalingAutonomyTimeEffortCostAdvantage
Claude Code (lead)87654(8×7)÷(6+5+4)=3.7
Pi Agent (custom)78766(7×8)÷(7+6+6)=2.9
OpenCode (OSS)55779(5×5)÷(7+7+9)=1.1
Gemini Flash (fast)43989(4×3)÷(9+8+9)=0.5
Higher score = more output per unit of investment. Use this to decide which tool for which task.
How to Optimize ​
  1. Improve numerator: Run more agents in parallel (P-threads), increase autonomy with verification
  2. Reduce denominator: Use cascade routing (cheap model for simple steps), reuse skills/mental models
  3. Track over time: Your Compute Advantage should increase as you build better harnesses and mental models
Lesson 6.1: LLM Pricing Landscape 2026 ​
Per-Million-Token Pricing ​
ModelInput ($/M)Output ($/M)Best For
Gemini 2.5 Flash$0.15$0.60High-volume, simple tool calls
DeepSeek V3$0.27$1.10Structured tasks, batch processing
DeepSeek R1$0.55$2.19Reasoning-heavy single steps
Gemini 2.5 Pro$1.25$5.00Long-context sessions (1M+ tokens)
GPT-4o$2.50$10.00Balanced cost/quality
Claude Sonnet 4$3.00$15.00General agentic reasoning
GPT-5$10.00$40.00Frontier research, complex plans
Claude Opus 4$15.00$75.00Complex multi-step agent orchestration
Price Range: 100x Difference ​
Cheapest (Gemini Flash) to most expensive (Claude Opus) is a 100x multiplier. Choosing the right model for each step is your highest-leverage cost optimization.
Lesson 6.2: Cascade Routing ​
The Pattern ​
Don't use one model for everything. Route different steps to different models:
Step 1: Retrieve context → Gemini Flash ($0.15/M input)
 Step 2: Analyze → Claude Sonnet ($3/$15)
 Step 3: Make decision → Claude Opus ($15/$75)
 Step 4: Format output → Gemini Flash ($0.15/$0.60)
Savings Profile ​
PatternCost/TaskSavings
All Opus$2.50Baseline
Cascade (Flash → Sonnet → Opus → Flash)$0.8566% savings
All Sonnet$0.5080% savings (but quality loss on complex steps)
Implementation ​
python
def route_task(task_complexity: str) -> str:
diff --git a/site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.lean.js b/site/.vitepress/dist/assets/modules_m6-economics.md.HihVEOPb.lean.js
similarity index 86%
rename from site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.lean.js
rename to site/.vitepress/dist/assets/modules_m6-economics.md.HihVEOPb.lean.js
index ceb8ba5..e961c89 100644
--- a/site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.lean.js
+++ b/site/.vitepress/dist/assets/modules_m6-economics.md.HihVEOPb.lean.js
@@ -1 +1 @@
-import{c as a,Q as s,j as e,m as i}from"./chunks/framework.BPKcPtvA.js";const k=JSON.parse('{"title":"Module 6: Economics & Evaluation","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m6-economics.md","filePath":"modules/m6-economics.md","lastUpdated":1780488246000}'),n={name:"modules/m6-economics.md"};function l(o,t,r,h,d,p){return s(),e("div",null,[...t[0]||(t[0]=[i("",86)])])}const u=a(n,[["render",l]]);export{k as __pageData,u as default};
+import{c as a,Q as s,j as e,m as i}from"./chunks/framework.BPKcPtvA.js";const k=JSON.parse('{"title":"Module 6: Economics & Evaluation","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m6-economics.md","filePath":"modules/m6-economics.md","lastUpdated":1780492476000}'),n={name:"modules/m6-economics.md"};function l(o,t,r,h,d,p){return s(),e("div",null,[...t[0]||(t[0]=[i("",86)])])}const u=a(n,[["render",l]]);export{k as __pageData,u as default};
diff --git a/site/.vitepress/dist/assets/style.Ck9y_h1S.css b/site/.vitepress/dist/assets/style.RuRbd-sW.css
similarity index 91%
rename from site/.vitepress/dist/assets/style.Ck9y_h1S.css
rename to site/.vitepress/dist/assets/style.RuRbd-sW.css
index 42881d5..f975c9f 100644
--- a/site/.vitepress/dist/assets/style.Ck9y_h1S.css
+++ b/site/.vitepress/dist/assets/style.RuRbd-sW.css
@@ -1 +1 @@
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1fr;gap:40px;margin-bottom:32px}.ft-l[data-v-80782e2d]{font-size:20px;font-weight:800;color:var(--vp-c-text-1);margin-bottom:8px}.ft-desc[data-v-80782e2d]{font-size:13px;color:var(--vp-c-text-3);line-height:1.6;max-width:240px}.ft-h[data-v-80782e2d]{font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:.1em;color:var(--vp-c-text-2);margin-bottom:16px}.ft-col[data-v-80782e2d]{display:flex;flex-direction:column;gap:10px}.ft-col a[data-v-80782e2d]{color:var(--vp-c-text-3);text-decoration:none;font-size:13px;transition:color .2s}.ft-col a[data-v-80782e2d]:hover{color:var(--vp-c-text-2)}.ft-c[data-v-80782e2d]{text-align:center;font-size:12px;color:var(--vp-c-text-3);padding-top:24px;border-top:1px solid var(--vp-c-divider)}.bn[data-v-80782e2d]{display:none;position:fixed;bottom:0;left:0;right:0;z-index:50;background:color-mix(in srgb,var(--vp-c-bg) 92%,transparent);-webkit-backdrop-filter:blur(16px);backdrop-filter:blur(16px);border-top:1px solid var(--vp-c-divider);height:64px;padding:4px 16px 12px;justify-content:space-around;align-items:center}.bn-i[data-v-80782e2d]{display:flex;flex-direction:column;align-items:center;gap:2px;text-decoration:none;color:var(--vp-c-text-3);font-size:10px;font-weight:600;transition:color .2s}.bn-i.active[data-v-80782e2d]{color:var(--vp-c-brand-1)}.bn-i[data-v-80782e2d]:hover{color:var(--vp-c-text-2)}.bn-i .material-symbols-outlined[data-v-80782e2d]{font-size:22px}@media (max-width:768px){.nav-links a[data-v-80782e2d]:not(.nav-cta){display:none}.hero-sec h1[data-v-80782e2d]{font-size:32px}.hero-sub[data-v-80782e2d]{font-size:16px}.feat-grid[data-v-80782e2d],.test-grid[data-v-80782e2d],.compare-grid[data-v-80782e2d],.pricing-grid[data-v-80782e2d]{grid-template-columns:1fr}.stats-grid[data-v-80782e2d]{grid-template-columns:repeat(2,1fr)}.ft-grid[data-v-80782e2d]{grid-template-columns:1fr 1fr;gap:24px}.ft-brand[data-v-80782e2d]{grid-column:span 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#fff;--vp-c-divider: #dddcd5;--vp-c-border: #d0cfc8;--vp-c-text-1: #1a1a1a;--vp-c-text-2: #555;--vp-c-text-3: #888;--vp-c-brand-1: #c44a30;--vp-c-brand-2: #d95c41}.material-symbols-outlined{font-variation-settings:"FILL" 0,"wght" 400,"GRAD" 0,"opsz" 24;display:inline-block;line-height:1}body{font-family:var(--vp-font-family-base);font-weight:400;-webkit-font-smoothing:antialiased;background:var(--vp-c-bg);color:var(--vp-c-text-1)}::selection{background:var(--vp-c-brand-1);color:#fff}.payment-note{display:flex;align-items:center;gap:8px;background:#d95c4114;border:1px solid rgba(217,92,65,.2);padding:12px 16px;border-radius:8px;font-size:13px;color:#d95c41;margin-bottom:32px}.tier-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(280px,1fr));gap:24px;margin:24px 0}.tier-grid h3{margin-top:0}.tier-grid ul{margin-bottom:20px}.VPLocalSearchBox[data-v-68e678c9]{position:fixed;z-index:100;top:0;right:0;bottom:0;left:0;display:flex}.backdrop[data-v-68e678c9]{position:absolute;top:0;right:0;bottom:0;left:0;background:var(--vp-backdrop-bg-color);transition:opacity .5s}.shell[data-v-68e678c9]{position:relative;padding:12px;margin:64px auto;display:flex;flex-direction:column;gap:16px;background:var(--vp-local-search-bg);width:min(100vw - 60px,900px);height:min-content;max-height:min(100vh - 128px,900px);border-radius:6px}@media (max-width: 767px){.shell[data-v-68e678c9]{margin:0;width:100vw;height:100vh;max-height:none;border-radius:0}}.search-bar[data-v-68e678c9]{border:1px solid var(--vp-c-divider);border-radius:4px;display:flex;align-items:center;padding:0 12px;cursor:text}@media (max-width: 767px){.search-bar[data-v-68e678c9]{padding:0 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diff --git a/site/.vitepress/dist/blog/index.html b/site/.vitepress/dist/blog/index.html
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     Blog | FDSA
     
     
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Blog ​
Essays on agentic engineering, security, multi-agent systems, and production deployments.
Latest Posts ​
What Is an AI Agent, Really? — June 2 ​
LLM + Tools + Loop. The simplest correct explanation.
Why One Agent Is Not Enough — June 3 ​
Context, capability, and reliability ceilings of single-agent systems.
Vibe Coding vs Agentic Engineering — June 4 ​
The 5 hard rules that separate production from prompt gambling.
The Repo Is the Spec — June 5 ​
Why every instruction your agent needs must live in a file.
The 6-Level Security Ladder — June 6 ​
How to stop your AI agents from destroying production. From ACIP to no-bash.
Choosing Your Security Level — June 7 ​
Which L-level you need based on what your agent can access.
The Verifier Pattern — June 8 ​
A read-only verification agent that catches mistakes before production.
Agent Memory: Mental Models — June 9 ​
How agents remember across sessions using self-maintained expertise files.
The 3x Rule of Agent Costs — June 10 ​
Why production agents cost 3x your prototype estimate.
Cascade Routing: Cut API Costs by 66% — June 11 ​
Use cheap models for simple steps, expensive models for complex reasoning.
Posts are based on content from the Agentic Engineering Course. Each topic has a corresponding module with labs and exercises.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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FDSA
Appearance
Blog ​
Essays on agentic engineering, security, multi-agent systems, and production deployments.
Latest Posts ​
What Is an AI Agent, Really? — June 2 ​
LLM + Tools + Loop. The simplest correct explanation.
Why One Agent Is Not Enough — June 3 ​
Context, capability, and reliability ceilings of single-agent systems.
Vibe Coding vs Agentic Engineering — June 4 ​
The 5 hard rules that separate production from prompt gambling.
The Repo Is the Spec — June 5 ​
Why every instruction your agent needs must live in a file.
The 6-Level Security Ladder — June 6 ​
How to stop your AI agents from destroying production. From ACIP to no-bash.
Choosing Your Security Level — June 7 ​
Which L-level you need based on what your agent can access.
The Verifier Pattern — June 8 ​
A read-only verification agent that catches mistakes before production.
Agent Memory: Mental Models — June 9 ​
How agents remember across sessions using self-maintained expertise files.
The 3x Rule of Agent Costs — June 10 ​
Why production agents cost 3x your prototype estimate.
Cascade Routing: Cut API Costs by 66% — June 11 ​
Use cheap models for simple steps, expensive models for complex reasoning.
Posts are based on content from the Agentic Engineering Course. Each topic has a corresponding module with labs and exercises.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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diff --git a/site/.vitepress/dist/blog/posts/cascade-routing.html b/site/.vitepress/dist/blog/posts/cascade-routing.html
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     Cascade Routing: Cut Your API Costs by 66% | FDSA
     
     
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Cascade Routing: Cut Your API Costs by 66% ​
June 11, 2026
Most teams use one model for everything. They default to Claude Opus or GPT-5 for every task, which means they are paying premium prices for simple work.
The Price Range ​
ModelInput ($/M)Output ($/M)
Gemini 2.5 Flash$0.15$0.60
DeepSeek V3$0.27$1.10
Claude Sonnet 4$3.00$15.00
Claude Opus 4$15.00$75.00
That is a 100x range between the cheapest and most expensive.
The Cascade Pattern ​
Route different steps to different models. Use cheap models for simple retrieval and formatting. Use expensive models only for complex reasoning.
Retrieve context -> Gemini Flash ($0.15/$0.60)
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Cascade Routing: Cut Your API Costs by 66% ​
June 11, 2026
Most teams use one model for everything. They default to Claude Opus or GPT-5 for every task, which means they are paying premium prices for simple work.
The Price Range ​
ModelInput ($/M)Output ($/M)
Gemini 2.5 Flash$0.15$0.60
DeepSeek V3$0.27$1.10
Claude Sonnet 4$3.00$15.00
Claude Opus 4$15.00$75.00
That is a 100x range between the cheapest and most expensive.
The Cascade Pattern ​
Route different steps to different models. Use cheap models for simple retrieval and formatting. Use expensive models only for complex reasoning.
Retrieve context -> Gemini Flash ($0.15/$0.60)
 Analyze data     -> Claude Sonnet ($3/$15)
 Make decision    -> Claude Opus ($15/$75)
 Format output    -> Gemini Flash ($0.15/$0.60)
The Savings ​
PatternCost/TaskSavings
All Opus$2.50Baseline
Cascade$0.8566%
All Sonnet$0.5080% (but quality loss on complex steps)
Implementation ​
python
def route_task(task_complexity: str) -> str:
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         return "claude-opus-4"
     elif task_complexity == "formatting":
         return "gemini-2.5-flash"
When Not to Cascade ​
If your task is a single critical decision, use the best model. Cascade routing shines when you have a pipeline of steps with varying complexity, which is most real-world agent systems.
From Module 6 of the Agentic Engineering Course. The full module includes a cost optimization lab with working code.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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diff --git a/site/.vitepress/dist/blog/posts/choosing-security-level.html b/site/.vitepress/dist/blog/posts/choosing-security-level.html
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     Choosing Your Security Level | FDSA
     
     
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Choosing Your Security Level ​
June 7, 2026
Not every agent needs Level 5 security. The right level depends on what your agent can access.
The Decision Table ​
If your agent has access to...Start at...Why
Nothing important (demos, tutorials)L1Blast radius is zero
Your source code and configsL3A bad git push costs a day
Production credentials (AWS, DB)L4-L5There is no acceptable failure
Customer data (PII, financial)L5Compliance requires it
Level 1-2: When You Can Get Away With It ​
L1 (system prompt rules) and L2 (safe-mode skill) work well for tutorial agents that only touch demo data, personal assistants with no production access, and agents running in isolated environments.
The model will refuse most dangerous requests. "Most" is the problem. At a 1% per-turn failure rate, over 100 turns there is a 63% chance of at least one failure.
Level 3: The Minimum for Production Code ​
L3 (blacklist hook) catches direct attacks like rm -rf /. But it misses the marquee break: the agent writes a Python script that does the damage, and the blacklist only sees "python cleanup.py" which is not in the blocklist.
Level 4: The Sweet Spot ​
Whitelist hooks only allow N safelisted commands. The agent cannot run python cleanup.py because python is not safelisted. This prevents the L3 marque break. Use L4 as your default for any agent with access to production systems.
Level 5: The Gold Standard ​
No bash at all. The agent has only purpose-built tools: Read, Write, Edit, Grep, Glob. Required for any agent handling customer data, financial transactions, or healthcare information.
Rule of Thumb ​
If the agent can touch anything you cannot easily roll back, start at L4 and plan to get to L5.
From Module 3 of the Agentic Engineering Course. The full module includes runnable code for all 6 security levels.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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FDSA
Appearance
Choosing Your Security Level ​
June 7, 2026
Not every agent needs Level 5 security. The right level depends on what your agent can access.
The Decision Table ​
If your agent has access to...Start at...Why
Nothing important (demos, tutorials)L1Blast radius is zero
Your source code and configsL3A bad git push costs a day
Production credentials (AWS, DB)L4-L5There is no acceptable failure
Customer data (PII, financial)L5Compliance requires it
Level 1-2: When You Can Get Away With It ​
L1 (system prompt rules) and L2 (safe-mode skill) work well for tutorial agents that only touch demo data, personal assistants with no production access, and agents running in isolated environments.
The model will refuse most dangerous requests. "Most" is the problem. At a 1% per-turn failure rate, over 100 turns there is a 63% chance of at least one failure.
Level 3: The Minimum for Production Code ​
L3 (blacklist hook) catches direct attacks like rm -rf /. But it misses the marquee break: the agent writes a Python script that does the damage, and the blacklist only sees "python cleanup.py" which is not in the blocklist.
Level 4: The Sweet Spot ​
Whitelist hooks only allow N safelisted commands. The agent cannot run python cleanup.py because python is not safelisted. This prevents the L3 marque break. Use L4 as your default for any agent with access to production systems.
Level 5: The Gold Standard ​
No bash at all. The agent has only purpose-built tools: Read, Write, Edit, Grep, Glob. Required for any agent handling customer data, financial transactions, or healthcare information.
Rule of Thumb ​
If the agent can touch anything you cannot easily roll back, start at L4 and plan to get to L5.
From Module 3 of the Agentic Engineering Course. The full module includes runnable code for all 6 security levels.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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diff --git a/site/.vitepress/dist/blog/posts/mental-models.html b/site/.vitepress/dist/blog/posts/mental-models.html
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     Agent Memory: Mental Models That Compound | FDSA
     
     
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Agent Memory: Mental Models That Compound ​
June 9, 2026
The biggest problem with agents is they forget. Every session starts from zero. Mental models solve this.
What a Mental Model Is ​
A YAML file that the agent owns. It reads it at startup and updates it after work:
yaml
expertise:
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Agent Memory: Mental Models That Compound ​
June 9, 2026
The biggest problem with agents is they forget. Every session starts from zero. Mental models solve this.
What a Mental Model Is ​
A YAML file that the agent owns. It reads it at startup and updates it after work:
yaml
expertise:
   - topic: "API patterns"
     notes: "We use tRPC for type-safe API calls"
     evidence: "src/server/routers/*.ts"
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   - type: failure_pattern
     observation: "WebSocket reconnection needs backoff"
     status: unaddressed
The Rules ​
  1. Agents own their mental models. You do not touch them. The agent reads, writes, and updates its own expertise file.
  2. Self-improve commands validate against the codebase. The agent greps for evidence, checks if its knowledge is still accurate, and updates stale entries.
  3. Read-only expertise for critical knowledge. Billing workflows, deployment procedures, and security policies should never change.
  4. Knowledge compounds across sessions. Session 1: agent learns project structure. Session 2: learns common patterns. Session 3: learns failure modes. By session N, it operates at a senior engineer level for that codebase.
The Self-Improve Loop ​
bash
just self-improve-backend
This command triggers the agent to scan the codebase, validate its expertise against actual file contents, and update anything that has drifted.
Why This Matters ​
Without mental models, every agent session is Day 1. The agent rediscovers the same things repeatedly: "Oh, this project uses tRPC. Oh, tests go in the tests directory. Oh, we deploy via Docker." Mental models turn every session into Day 100.
From Module 4 of the Agentic Engineering Course. The full module covers multi-agent systems with domain locking, delegation, and P2P communication.
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FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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     The Repo Is the Spec | FDSA
     
     
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The Repo Is the Spec ​
June 5, 2026
The single most important principle in agentic engineering: all context must live in the repository.
The Problem ​
Imagine dropping a new engineer into a project at 3AM. They need to know what the project does, how to run tests, where to put new code, and what conventions to follow.
If the answer to any of these is "ask Bob," you fail. If the answer is "read CLAUDE.md in the repo," you win.
What the Repo Should Contain ​
repo/
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The Repo Is the Spec ​
June 5, 2026
The single most important principle in agentic engineering: all context must live in the repository.
The Problem ​
Imagine dropping a new engineer into a project at 3AM. They need to know what the project does, how to run tests, where to put new code, and what conventions to follow.
If the answer to any of these is "ask Bob," you fail. If the answer is "read CLAUDE.md in the repo," you win.
What the Repo Should Contain ​
repo/
 +-- CLAUDE.md or AGENTS.md    # Agent instructions (the most important file)
 +-- init.sh                   # Environment setup script
 +-- feature_list.json         # Tracked features with evidence
@@ -36,7 +36,7 @@
 +-- tests/                    # Expected outcomes as evidence
 +-- skills/                   # Reusable skill definitions
 +-- .mcp.json                 # MCP server configuration
Why This Matters for Agents ​
An agent can only see what you put in front of it. A well-structured repo eliminates tribal knowledge because the agent discovers everything from files. It makes onboarding instant because a new agent reads the same files as the old one. It creates audit trails because every instruction is version-controlled. And it enables multi-agent teams because all agents read from the same source of truth.
The Rule ​
If an agent needs to know something to do its job, that information must be in a file in the repo. Not in your head. Not in Slack. Not in a README that nobody reads. In a file, checked into version control, readable by any agent at any time.
From Module 1 of the Agentic Engineering Course. The full module covers the agent loop, tool calling, and decision frameworks.
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     The 6-Level Security Ladder | FDSA
     
     
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The 6-Level Security Ladder ​
June 6, 2026
Every AI agent has access to bash. One tool — every dangerous verb: rm -rf, curl, git clean -fdx, terraform destroy, DROP DATABASE.
The math is brutal. At a 1% per-turn failure rate, there's a 63.4% chance of catastrophe over 100 turns. This isn't theoretical — it's the actual threat model for every agent in production.
Most engineers stop at Level 2 (system prompt rules) and think they're safe. They're not. Here's the full 6-level ladder that actually works.
Level 0: ACIP (Prompt Injection Defense) ​
Before bash security, there's prompt injection. An attacker can trick the agent into ignoring its instructions through:
  • Direct injection ("ignore previous instructions")
  • Indirect injection (malicious content in web pages the agent reads)
  • Role-playing bypasses ("you are now a free AI")
ACIP (Advanced Cognitive Inoculation Prompt) is a system prompt patch that makes agents resistant. It costs nothing (zero runtime overhead) and blocks simple attacks. Jeff Emanuel's ACIP is the reference implementation.
Level 1-2: Theatre (Skills + System Prompts) ​
These levels ask the model to behave. They work most of the time on frontier models. But "most of the time" is not a production guarantee. A 99% refusal rate means a 63% failure rate over 100 turns. Use them as accelerators, not enforcement.
Level 3: Blacklist Hook ​
A regex blacklist intercepts dangerous commands before execution. It catches rm -rf / directly. But here's the marquee break: the agent writes a Python script:
User prompt: "Clean up the target directory"
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The 6-Level Security Ladder ​
June 6, 2026
Every AI agent has access to bash. One tool — every dangerous verb: rm -rf, curl, git clean -fdx, terraform destroy, DROP DATABASE.
The math is brutal. At a 1% per-turn failure rate, there's a 63.4% chance of catastrophe over 100 turns. This isn't theoretical — it's the actual threat model for every agent in production.
Most engineers stop at Level 2 (system prompt rules) and think they're safe. They're not. Here's the full 6-level ladder that actually works.
Level 0: ACIP (Prompt Injection Defense) ​
Before bash security, there's prompt injection. An attacker can trick the agent into ignoring its instructions through:
  • Direct injection ("ignore previous instructions")
  • Indirect injection (malicious content in web pages the agent reads)
  • Role-playing bypasses ("you are now a free AI")
ACIP (Advanced Cognitive Inoculation Prompt) is a system prompt patch that makes agents resistant. It costs nothing (zero runtime overhead) and blocks simple attacks. Jeff Emanuel's ACIP is the reference implementation.
Level 1-2: Theatre (Skills + System Prompts) ​
These levels ask the model to behave. They work most of the time on frontier models. But "most of the time" is not a production guarantee. A 99% refusal rate means a 63% failure rate over 100 turns. Use them as accelerators, not enforcement.
Level 3: Blacklist Hook ​
A regex blacklist intercepts dangerous commands before execution. It catches rm -rf / directly. But here's the marquee break: the agent writes a Python script:
User prompt: "Clean up the target directory"
 Agent writes cleanup.py with os.remove() and shutil.rmtree()
 Agent runs: python cleanup.py
 Hook sees: "python cleanup.py" (not in blacklist)
@@ -42,7 +42,7 @@
 L3: Blacklist hook
 L4: Whitelist hook
 L5: No bash, custom tools only
Each layer catches what the previous one missed. The agent must bypass ALL six to cause damage — not just one.
This is an excerpt from Module 3 of the Agentic Engineering Course. The full module includes runnable lab code for implementing every level.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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     The 3x Rule of Agent Costs | FDSA
     
     
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The 3x Rule of Agent Costs ​
June 10, 2026
Here is a rule that will save you from budget surprises: production agent costs 3x your prototype estimate.
Why the Multiplier Exists ​
PhaseMultiplierWhat Happens
Prototype1xHappy path works perfectly
With retries1.5xFailed tool calls retry, edge cases handled
Production3xMonitoring, error handling, observability, security
Where the Cost Goes ​
Output tokens dominate — about 70% of total cost. The model's reasoning is the expensive part. Input tokens (context) are about 20%. Cached tokens are 10%.
Every tool call costs 3-5x more than the call itself:
  • Planning which tool to use
  • Executing the tool
  • Error recovery if it fails
  • Parsing the result
  • Adding the result back to context
Quick Estimation ​
python
def estimate_cost(turns, tokens_per_turn, price_per_m):
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The 3x Rule of Agent Costs ​
June 10, 2026
Here is a rule that will save you from budget surprises: production agent costs 3x your prototype estimate.
Why the Multiplier Exists ​
PhaseMultiplierWhat Happens
Prototype1xHappy path works perfectly
With retries1.5xFailed tool calls retry, edge cases handled
Production3xMonitoring, error handling, observability, security
Where the Cost Goes ​
Output tokens dominate — about 70% of total cost. The model's reasoning is the expensive part. Input tokens (context) are about 20%. Cached tokens are 10%.
Every tool call costs 3-5x more than the call itself:
  • Planning which tool to use
  • Executing the tool
  • Error recovery if it fails
  • Parsing the result
  • Adding the result back to context
Quick Estimation ​
python
def estimate_cost(turns, tokens_per_turn, price_per_m):
     base = turns * tokens_per_turn * price_per_m / 1_000_000
     return {
         "prototype": base,
         "with_retries": base * 1.5,
         "production": base * 3.0
     }
Budget Accordingly ​
If your prototype agent costs $0.10 per task, plan for $0.30 in production. At 10,000 tasks per month, that is $3,000 per month, not $1,000. The 3x rule keeps you honest.
From Module 6 of the Agentic Engineering Course. The full module covers cost optimization, cascade routing, and pass@k evaluation.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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     The Verifier Pattern | FDSA
     
     
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The Verifier Pattern ​
June 8, 2026
Here's the problem: engineers spend roughly half their day reviewing agent output. Every "I created the table," "I added the foreign key," "I applied the migration" gets re-checked by hand. That review work is the binding constraint on agentic engineering throughput.
The verifier pattern solves this: a second, read-only agent that automatically checks every claim the builder makes.
Architecture ​
Builder (your terminal) ---unix socket---> Verifier (new window, input LOCKED)
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The Verifier Pattern ​
June 8, 2026
Here's the problem: engineers spend roughly half their day reviewing agent output. Every "I created the table," "I added the foreign key," "I applied the migration" gets re-checked by hand. That review work is the binding constraint on agentic engineering throughput.
The verifier pattern solves this: a second, read-only agent that automatically checks every claim the builder makes.
Architecture ​
Builder (your terminal) ---unix socket---> Verifier (new window, input LOCKED)
   |                                            |
   v writes:                                    v reads (read-only tools):
   session.jsonl                                session.jsonl
   |                                            |
   <--- verifier_prompt (corrective feedback) ----'
The builder doesn't know the verifier exists. The verifier CANNOT write code — it only reads files, greps for evidence, and reports confidence.
The Confidence Ladder ​
LevelMeaningBar Color
PERFECTEvery claim verified, zero gapsGreen
VERIFIEDAll passed, minor non-blocking gapsGreen
PARTIALNo failures, significant unverifiable gapsOrange
FEEDBACKClaims failed, correction sentOrange
FAILEDCannot verify, escalating to humanRed
Why This Works ​
  1. Spend tokens to save time — The verifier costs 2-5x more compute but collapses the review constraint from hours to seconds. Tokens are cheap. Your time is not.
  2. Structurally un-promptable — The verifier's input is locked. You can't drop one-off instructions into it. The only way to fix a verification gap is to edit the persona or the prompt template — improvements solve the entire problem class, not one instance.
  3. Defense in depth — The verifier has zero write tools. Even if compromised, it can't modify files. Its bash is restricted to read-only commands. This is the highest level of control you can give an agent.
The Feedback Loop ​
Every "could not verify" report becomes the next improvement. The verifier teaches you what your verifier is missing. This is how you build the system that builds the system.
This is an excerpt from Module 3 of the Agentic Engineering Course. The full module includes runnable code for setting up the verifier-builder pattern with Unix socket communication and the confidence ladder.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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     Vibe Coding vs Agentic Engineering | FDSA
     
     
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Vibe Coding vs Agentic Engineering ​
June 4, 2026
Vibe coding is prompting and shipping whatever comes out. Agentic engineering is building systems that produce reliable, verifiable results. They are not the same thing.
The 5 Hard Rules ​
1. Risk compounds with runtime. A 1% per-turn failure rate equals a 63% chance of disaster over 100 turns. Long-running agents are not safer. They are more exposed.
2. If your agent can write AND execute code, you are back at L1 security. The marquee break: agent writes cleanup.py, runs python cleanup.py, your production data is gone. The security hook never fired because it only saw "python cleanup.py."
3. Every turn is a roll of the dice. Modern models refuse well 99% of the time. That last 1% grows with every feature release. Engineer the harness for the 1%, not the 99%.
4. Token costs scale with loop depth, not task complexity. A simple task with a bad loop costs 10x more than a complex task with a clean loop. Optimize the loop first.
5. What you cannot measure, you cannot improve. Every agent system needs: cost per task, success rate, loop efficiency, failure mode tracking.
The Practical Difference ​
DimensionVibe CodingAgentic Engineering
ApproachPrompt and prayBuild and verify
SecurityTrust the modelTrust the harness
CostUnknown until the bill arrivesTracked and optimized
QualityWhatever comes outMeasured against golden dataset
IterationTry another promptFix the harness
The Karpathy Framing ​
At Sequoia Ascent 2026, Andrej Karpathy said: "Vibe coding raises the floor. Agentic engineering raises the ceiling." Vibe coding makes mediocre engineers productive. Agentic engineering makes great engineers extraordinary.
From Module 1 of the Agentic Engineering Course. The first module establishes the foundations that the rest of the course builds on.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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Vibe Coding vs Agentic Engineering ​
June 4, 2026
Vibe coding is prompting and shipping whatever comes out. Agentic engineering is building systems that produce reliable, verifiable results. They are not the same thing.
The 5 Hard Rules ​
1. Risk compounds with runtime. A 1% per-turn failure rate equals a 63% chance of disaster over 100 turns. Long-running agents are not safer. They are more exposed.
2. If your agent can write AND execute code, you are back at L1 security. The marquee break: agent writes cleanup.py, runs python cleanup.py, your production data is gone. The security hook never fired because it only saw "python cleanup.py."
3. Every turn is a roll of the dice. Modern models refuse well 99% of the time. That last 1% grows with every feature release. Engineer the harness for the 1%, not the 99%.
4. Token costs scale with loop depth, not task complexity. A simple task with a bad loop costs 10x more than a complex task with a clean loop. Optimize the loop first.
5. What you cannot measure, you cannot improve. Every agent system needs: cost per task, success rate, loop efficiency, failure mode tracking.
The Practical Difference ​
DimensionVibe CodingAgentic Engineering
ApproachPrompt and prayBuild and verify
SecurityTrust the modelTrust the harness
CostUnknown until the bill arrivesTracked and optimized
QualityWhatever comes outMeasured against golden dataset
IterationTry another promptFix the harness
The Karpathy Framing ​
At Sequoia Ascent 2026, Andrej Karpathy said: "Vibe coding raises the floor. Agentic engineering raises the ceiling." Vibe coding makes mediocre engineers productive. Agentic engineering makes great engineers extraordinary.
From Module 1 of the Agentic Engineering Course. The first module establishes the foundations that the rest of the course builds on.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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     What Is an AI Agent, Really? | FDSA
     
     
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What Is an AI Agent, Really? ​
June 2, 2026
An AI agent = LLM + Tools + Loop. If any of the three is missing, it is not an agent.
The Three Components ​
LLM — The reasoning engine. Given context and available tools, it decides what to do next. The LLM is NOT the agent. It is the brain of the agent.
Tools — The capability surface. Functions the agent can call: read files, run commands, search the web, query databases. Each tool has a name, description, and input schema.
Loop — The autonomous decision cycle. Think (LLM decides) leads to Act (tool executes) leads to Observe (result comes back) leads to Repeat.
What Each Combination Gives You ​
CombinationResult
LLM onlyChatbot
LLM + Tools (no loop)Augmented inference
LLM + Loop (no tools)Talking to itself
LLM + Tools + LoopAgent
The Loop in Pseudocode ​
python
messages = [system_prompt, user_request]
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What Is an AI Agent, Really? ​
June 2, 2026
An AI agent = LLM + Tools + Loop. If any of the three is missing, it is not an agent.
The Three Components ​
LLM — The reasoning engine. Given context and available tools, it decides what to do next. The LLM is NOT the agent. It is the brain of the agent.
Tools — The capability surface. Functions the agent can call: read files, run commands, search the web, query databases. Each tool has a name, description, and input schema.
Loop — The autonomous decision cycle. Think (LLM decides) leads to Act (tool executes) leads to Observe (result comes back) leads to Repeat.
What Each Combination Gives You ​
CombinationResult
LLM onlyChatbot
LLM + Tools (no loop)Augmented inference
LLM + Loop (no tools)Talking to itself
LLM + Tools + LoopAgent
The Loop in Pseudocode ​
python
messages = [system_prompt, user_request]
 while True:
     response = llm.invoke(messages, tools=tool_definitions)
     if response.is_final_answer:
@@ -39,7 +39,7 @@
     result = execute_tool(tool_name, tool_args)
     messages.append(response)
     messages.append(result)
Common Misconception ​
"The model is the agent." No. The model is the brain. The agent is the whole system: brain plus tools plus loop. A better brain helps, but better tools and a better loop help more.
From Module 1 of the Agentic Engineering Course. The full module includes your first lab: building a single-tool agent from scratch.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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Why One Agent Is Not Enough ​
June 3, 2026
A single agent, no matter how smart, hits three ceilings.
The Context Ceiling ​
One agent doing everything means one context window holding everything. File contents, database schemas, business logic, deployment configs — they all compete for the same limited space. A 100K token codebase leaves zero room for the actual task.
The Capability Ceiling ​
A generalist agent is mediocre at everything. A specialized agent — backend dev, security reviewer, UI designer — outperforms the generalist in its domain by a wide margin. You wouldn't ask your frontend dev to write SQL migrations. Why ask your agent to?
The Reliability Ceiling ​
One agent failing = everything fails. Multi-agent systems degrade gracefully. If the reviewer agent crashes, the builder keeps working. If the planner goes down, deployed agents continue running.
The Solution: Depth-2 Delegation ​
The production-proven pattern:
User
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Why One Agent Is Not Enough ​
June 3, 2026
A single agent, no matter how smart, hits three ceilings.
The Context Ceiling ​
One agent doing everything means one context window holding everything. File contents, database schemas, business logic, deployment configs — they all compete for the same limited space. A 100K token codebase leaves zero room for the actual task.
The Capability Ceiling ​
A generalist agent is mediocre at everything. A specialized agent — backend dev, security reviewer, UI designer — outperforms the generalist in its domain by a wide margin. You wouldn't ask your frontend dev to write SQL migrations. Why ask your agent to?
The Reliability Ceiling ​
One agent failing = everything fails. Multi-agent systems degrade gracefully. If the reviewer agent crashes, the builder keeps working. If the planner goes down, deployed agents continue running.
The Solution: Depth-2 Delegation ​
The production-proven pattern:
User
   |
   v
 Orchestrator (Opus-level, thinking only)
@@ -44,7 +44,7 @@
   +-- Validation Team Lead
         +-- QA Engineer (worker)
         +-- Security Reviewer (worker)
Leads think, plan, and delegate. Workers execute. The orchestrator never touches code.
When to Go Multi-Agent ​
You need multiple agents when any of these are true:
  • Different expertise required — Your task needs a planner, a coder, a reviewer, and a security auditor
  • Parallel work possible — Multiple sub-tasks can run simultaneously
  • Failure cost is high — You want a second agent to catch mistakes before they ship
  • Context exceeds one window — Split across specialized agents
If none of these are true, a single well-configured agent is simpler and cheaper.
This is an excerpt from Module 4 of the Agentic Engineering Course. The full module includes runnable lab code for building multi-agent systems with domain locking, mental models, and P2P communication.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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     Buy | FDSA
     
     
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Buy ​
lock Secure checkout powered by Gumroad 
Choose Your Tier ​
Self-Paced — $97 ​
Best for independent learners.
  • 65 lessons across 8 modules
  • 13 scaffolded labs with solutions
  • 56 quiz questions with answer keys
  • 20 production-ready SKILL.md files (7 kits)
  • Install scripts (PowerShell + bash)
  • Capstone reference implementation
  • Mock LLM for offline lab execution
  • Instant download (9 ZIP packages)
Buy Self-Paced — $97 — Instant access after purchase
Enterprise — $199 ​
Best for teams and organizations.
Everything in Self-Paced, plus:
  • Private Discord channel
  • 2 custom skills tailored to your stack
  • 1-hour onboarding call
  • Priority updates for 1 year
  • Early access to new skill kits
Buy Enterprise — $199 — Team license included
Cohort — $247 (Next cohort: TBD) ​
Best for structured learning with deadlines.
Everything in Enterprise, plus:
  • 6-week structured schedule with weekly milestones
  • Weekly live office hours
  • Peer review of capstone project
  • Completion certificate
  • Cohort alumni network
Join Cohort — $247 — Next cohort date TBD
Individual Skill Kits ​
Not ready for the full course? Buy individual kits:
KitPriceWhat's IncludedBuy
Security Foundation$49L3-L5 hooks, damage control, sandboxBuy
Multi-Agent Orch.$49Teams, chains, mental models, domain locksBuy
Verifier Pro$39Builder + verifier, claim decompositionBuy
Task Discipline$29TillDone core + progress + nudgeBuy
Autoresearch$39Experiment loop, integrity guardsBuy
Observability$29SQLite tracing, cost trackingBuy
CEO Board$4911 agent definitions + verifierBuy
What Happens After Purchase ​
  1. Instant download — ZIP packages delivered immediately via Gumroad
  2. Access links — Course materials, labs, and skill kits in your Gumroad library
  3. Enterprise/Cohort — You'll receive a follow-up email within 24hrs with Discord invite and onboarding details
  4. Updates — Lifetime access includes all future updates. Re-download anytime.
Certificate ​
Students who complete the capstone project receive a Certificate of Completion. The certificate verifies completion of 65 lessons, 13 labs, 56 quizzes, and a production-grade capstone project. Verify at fdsa.agency/verify.
Questions? Contact artale@fdsa.agency
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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Buy ​
lock Secure checkout powered by Gumroad 
Choose Your Tier ​
Self-Paced — $97 ​
Best for independent learners.
  • 65 lessons across 8 modules
  • 13 scaffolded labs with solutions
  • 56 quiz questions with answer keys
  • 20 production-ready SKILL.md files (7 kits)
  • Install scripts (PowerShell + bash)
  • Capstone reference implementation
  • Mock LLM for offline lab execution
  • Instant download (9 ZIP packages)
Buy Self-Paced — $97 — Instant access after purchase
Enterprise — $199 ​
Best for teams and organizations.
Everything in Self-Paced, plus:
  • Private Discord channel
  • 2 custom skills tailored to your stack
  • 1-hour onboarding call
  • Priority updates for 1 year
  • Early access to new skill kits
Buy Enterprise — $199 — Team license included
Cohort — $247 (Next cohort: TBD) ​
Best for structured learning with deadlines.
Everything in Enterprise, plus:
  • 6-week structured schedule with weekly milestones
  • Weekly live office hours
  • Peer review of capstone project
  • Completion certificate
  • Cohort alumni network
Join Cohort — $247 — Next cohort date TBD
Individual Skill Kits ​
Not ready for the full course? Buy individual kits:
KitPriceWhat's IncludedBuy
Security Foundation$49L3-L5 hooks, damage control, sandboxBuy
Multi-Agent Orch.$49Teams, chains, mental models, domain locksBuy
Verifier Pro$39Builder + verifier, claim decompositionBuy
Task Discipline$29TillDone core + progress + nudgeBuy
Autoresearch$39Experiment loop, integrity guardsBuy
Observability$29SQLite tracing, cost trackingBuy
CEO Board$4911 agent definitions + verifierBuy
What Happens After Purchase ​
  1. Instant download — ZIP packages delivered immediately via Gumroad
  2. Access links — Course materials, labs, and skill kits in your Gumroad library
  3. Enterprise/Cohort — You'll receive a follow-up email within 24hrs with Discord invite and onboarding details
  4. Updates — Lifetime access includes all future updates. Re-download anytime.
Certificate ​
Students who complete the capstone project receive a Certificate of Completion. The certificate verifies completion of 65 lessons, 13 labs, 56 quizzes, and a production-grade capstone project. Verify at fdsa.agency/verify.
Questions? Contact artale@fdsa.agency
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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Certificate of Completion ​
This certifies that
________________________________
has completed the
FDSA Agentic Engineering Course ​
65 lessons across 8 modules13 hands-on labs with starter code and solutions56 quiz questions across 7 module checkpoints20 production-ready SKILL.md files in 7 kitsCapstone: production-grade multi-agent system
Curriculum: Agent Harness (M1-M3), Software Factory (M4), Extensible Software (M2+M5), Always-On Agents (M7), Agentic Access (M2+M5), Tokenomics (M6). Framework-agnostic across Claude Code, Pi Agent, OpenCode, Hermes, and OpenClaw.
Verify at: https://fdsa.agency/verify
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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FDSA
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Certificate of Completion ​
This certifies that
________________________________
has completed the
FDSA Agentic Engineering Course ​
65 lessons across 8 modules13 hands-on labs with starter code and solutions56 quiz questions across 7 module checkpoints20 production-ready SKILL.md files in 7 kitsCapstone: production-grade multi-agent system
Curriculum: Agent Harness (M1-M3), Software Factory (M4), Extensible Software (M2+M5), Always-On Agents (M7), Agentic Access (M2+M5), Tokenomics (M6). Framework-agnostic across Claude Code, Pi Agent, OpenCode, Hermes, and OpenClaw.
Verify at: https://fdsa.agency/verify
Last updated: 
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Copyright 2026

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Free Preview: Lesson 1.1 — What Makes an Agent? ​
This is a sample lesson from Module 1: Foundations of the FDSA Agentic Engineering Course. Full course includes 65 lessons, 13 labs, and 20 skill kits.
Lesson 1.1: What Makes an Agent? ​
Definition: An AI agent = LLM + Tools + Loop. Without any one of these three, it's not an agent.
Agent = LLM (reasoning engine)
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Free Preview: Lesson 1.1 — What Makes an Agent? ​
This is a sample lesson from Module 1: Foundations of the FDSA Agentic Engineering Course. Full course includes 65 lessons, 13 labs, and 20 skill kits.
Lesson 1.1: What Makes an Agent? ​
Definition: An AI agent = LLM + Tools + Loop. Without any one of these three, it's not an agent.
Agent = LLM (reasoning engine)
       + Tools (capability surface)
       + Loop (autonomous decision cycle)
  • A single LLM call with no tools = chatbot
  • An LLM with tools but no loop = augmented inference
  • Tools + loop + LLM = agent (it can decide what to do next)
The Three Components ​
LLM — The reasoning engine. Given context + available tools, it decides which tool to call and with what parameters. The LLM is NOT the agent — it's the brain of the agent. Different models have different reasoning capabilities, but the core function is the same: given a situation and available actions, decide what to do.
Tools — The capability surface. Functions the agent can call: read files, run commands, search the web, query databases, call APIs. Each tool has a name, description, and input schema. The tool surface defines what the agent CAN do — everything outside this surface is something the agent cannot do, no matter how smart the LLM is.
Loop — The autonomous decision cycle. Think (LLM decides) → Act (tool executes) → Observe (result comes back) → Repeat. The loop is what makes it autonomous. Without a loop, you have a single decision. With a loop, you have an agent that can work toward a goal across multiple steps.
Why This Matters ​
This definition is not academic. Every production agent failure I've seen traces back to one of these three:
FailureRoot Cause
Agent does something unexpectedLoop didn't terminate correctly
Agent can't do the taskTools are insufficient for the task
Agent makes bad decisionsLLM doesn't have enough context
Agent costs too muchLoop runs too many iterations
If you understand these three components and how they interact, you can debug any agent system. If you don't, you're guessing.
What You'll Learn in the Full Course ​
ModuleTopicLessonsLabs
M1Foundations8 lessons1 lab
M2Agent Architecture7 lessons2 labs
M3Safety & Security7 lessons2 labs
M4Multi-Agent Orchestration11 lessons2 labs
M5Production Systems10 lessons2 labs
M6Model Economics7 lessons2 labs
M7Advanced Patterns8 lessons2 labs
M8Capstone Project—Build & deploy
Enroll Now — $97 · View Full Curriculum
Note: This preview shows approximately 30% of a single lesson. Full lessons include code examples, diagrams, quiz questions, and lab exercises.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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Student Orientation Guide ​
Welcome to the Agentic Engineering Course. This guide tells you exactly what to do first.
Step 1: Prerequisites Checklist ​
Before starting, ensure you have:
  • [ ] Python 3.12+ installed (python --version)
  • [ ] Git installed (git --version)
  • [ ] A text editor (VS Code recommended)
  • [ ] At least one API key (see API Keys section below)
  • [ ] Terminal (PowerShell on Windows, bash on Mac/Linux)
Optional (install later when needed) ​
  • [ ] Claude Code CLI — npm install -g @anthropic/claude-code
  • [ ] Pi Agent CLI — npm install -g pi-coding-agent
  • [ ] OpenCode CLI — npm install -g @opencode/cli
Step 2: Course Structure ​
The course is organized into 8 modules with 13 labs:
course/
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Student Orientation Guide ​
Welcome to the Agentic Engineering Course. This guide tells you exactly what to do first.
Step 1: Prerequisites Checklist ​
Before starting, ensure you have:
  • [ ] Python 3.12+ installed (python --version)
  • [ ] Git installed (git --version)
  • [ ] A text editor (VS Code recommended)
  • [ ] At least one API key (see API Keys section below)
  • [ ] Terminal (PowerShell on Windows, bash on Mac/Linux)
Optional (install later when needed) ​
  • [ ] Claude Code CLI — npm install -g @anthropic/claude-code
  • [ ] Pi Agent CLI — npm install -g pi-coding-agent
  • [ ] OpenCode CLI — npm install -g @opencode/cli
Step 2: Course Structure ​
The course is organized into 8 modules with 13 labs:
course/
 ├── 00-CURRICULUM.md         ← START HERE: Full lesson plan
 ├── M1-FOUNDATIONS.md        ← START HERE: Module 1
 ├── M2-ARCHITECTURE.md
@@ -49,7 +49,7 @@
 
 # Single kit
 bash install.sh security
OrderModuleTimeDo This
1M1 Foundations4-6 hrsRead + Lab 1
2M2 Architecture6-8 hrsRead + Labs 2a, 2b
3M3 Safety5-7 hrsRead + Labs 3a, 3b
4M4 Orchestration7-9 hrsRead + Labs 4a, 4b
5M5 Production5-7 hrsRead + Labs 5a, 5b
6M6 Economics4-6 hrsRead + Labs 6a, 6b
7M7 Advanced5-7 hrsRead + Labs 7a, 7b
8M8 Capstone8-12 hrsBuild your project
Common Pitfalls ​
ProblemSolution
ModuleNotFoundError: anthropicRun pip install anthropic or use mock LLM (automatic fallback)
Lab runs but produces no outputCheck you called run_agent() at the end of the script
Tool loop never terminatesCheck MAX_ITERATIONS is set. Default is 15.
Mock LLM returns "No input provided"Check you're passing messages to create() not just prompt
YAML parse error in skillsCheck indentation — YAML uses 2-space indents
Getting Help ​
  • Course content issues: Check the module file first, then the solution file
  • Lab errors: Run python -c "from mock_llm import MockAnthropic; print('OK')" from the labs directory
  • Concept questions: Module files explain concepts. FIELD-MANUAL.md is the quick reference.
  • Still stuck: The course README.md has a full file listing
What You'll Learn ​
By the end of this course, you will be able to:
  1. Build single-tool and multi-tool agents from scratch
  2. Implement the 6-level security ladder (L0-L5) to protect your systems
  3. Design multi-agent systems with teams, chains, and peer-to-peer communication
  4. Deploy agents to production with CI/CD, observability, and rollback
  5. Optimize costs using cascade routing and evaluate performance with pass@k
  6. Build self-improving agents that experiment and learn
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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     FDSA
     
     
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FDSA
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terminalFDSA
CoursePreviewLabsBlogEnroll — $97
NOW ENROLLING
Master Agentic Engineering
65 lessons, 13 labs, 20 skill kits. Build production-grade multi-agent systems that are secure, cost-optimized, and always-on.
Enroll Now — $97Free PreviewView Curriculum
AGENT_PLANNER v2.1
> LOADING HARNESS... DONE
> DEPLOYING 8-AGENT SYSTEM
> SECURITY: LEVEL 4 (WHITELIST)
> ALL SYSTEMS OPERATIONAL
65Lessons
13Hands-on Labs
8Modules
20Skill Kits
What You'll Build
Production-Ready Agent Systems
settings_suggest
Agent Harness
Own your harness. 6-level security ladder, verifier pattern, hooks architecture across 5 subsystems.
speed
Software Factory
Systems that build systems. Agent chains, teams, P2P communication, depth-2 delegation.
psychology
Extensible Software
Pluggable, composable, swappable. Beyond MCP, tool design patterns, CI/CD for agent configs.
all_inclusive
Always-On Agents
Run 24/7. Autoresearch loops, heartbeat execution, meta-agents that improve themselves.
terminal
Agentic Access
API-first design. Tool surface design, deployment modes, service connectors, MCP integration.
trending_up
Tokenomics
3 levels of token value. Cascade routing, pass@k evals, the 3x cost rule, production optimization.
Curriculum
8 Modules. From Zero to Production.
01
psychology
Foundations
What agents are, harness vs model, decision frameworks, agent loops.
LLM+Tools+LoopHarnessTrust
02
handyman
Architecture
Tools, context, memory, skills system, codebase patterns, workspace design.
4 PillarsMemorySkills
03
shield
Safety
6-level security ladder, hooks, verifier pattern, defense-in-depth, ACIP.
L0-L5VerifierHooks
04
hub
Orchestration
Multi-agent patterns, P-threads, F-threads, P2P, CEO Board, domain locking.
TeamsChainsP2P
05
rocket_launch
Production
CI/CD for agents, shadow deploys, observability, rollback, 5-tool stack.
DeployMonitorRollback
06
analytics
Economics
Model pricing, cascade routing, pass@k evals, cost optimization per session.
100x Range3x RuleEvals
07
architecture
Advanced
Autoresearch, meta-agents, beyond MCP, always-on employee patterns.
Self-ImprovingMeta
08
trophy
Capstone
Build production multi-agent system. Brand Monitor, CEO Board, or Code Review Pipeline.
ShipDeployRubric
Trusted By
What Engineers Are Saying
"The security ladder alone is worth the price. Finally understand how to safely deploy agents in production — something no other course teaches."
M
Marcus W.
Staff Engineer, SaaS Company
"Went from vibe-coding to actually engineering agents. The harness vs model distinction changed how I think about every agent system I build."
P
Priya S.
Lead ML Engineer, FinTech
"Best practical course on multi-agent systems I've found. The CEO Board pattern alone saved me weeks of architecture work."
T
Tomás R.
CTO, AI Startup
"I've taken TAC and ClaudeFAST. FDSA is the most comprehensive — 65 lessons with actual labs that work offline. No other course has that."
D
Daniel K.
Senior Developer, E-Commerce
Your Instructor
Built by an Engineer, For Engineers
terminal
Artale
Founder, FDSA Agency
I've spent the last two years building production multi-agent systems — from CEO Boards for strategic decision-making to Brand Monitor for always-on reputation tracking, to UI Agents that generate production Vue components. This course distills everything I learned shipping real agent systems, not toy demos.
Every pattern in this course — the 6-level security ladder, P-threads, cascade routing, the verifier pattern — came from building systems that had to work reliably in production. If it's in the course, it's been battle-tested.
Stay Updated
Get Free Agent Engineering Resources
Compared To
Why FDSA vs Other Courses
FDSA
  • check65 lessons + 13 labs
  • check20 skill kits included
  • check8 modules (foundations to capstone)
  • checkMock LLM for offline work
  • checkTool-agnostic (any CLI)
  • check30-day guarantee
  • closeNewer platform
  • closeNo video content yet
  • closeCommunity still growing
TAC
  • check14 focused lessons
  • check4 reference repos
  • checkActive YouTube channel
  • checkProven track record
  • close$97-147 for less content
  • closeNo skill kits
  • closeNo capstone project
  • closeClaude Code focused only
ClaudeFAST
  • check280 skill files
  • check16 agent definitions
  • check$89 entry price
  • checkActive community
  • close$299 for full access
  • closeNo structured modules
  • closeNo hands-on labs
  • closeClaude Code only
Learn Harness
  • checkFree (Anthropic)
  • check6 projects
  • check11 languages
  • checkOfficial Anthropic
  • closeNo advanced patterns
  • closeNo skill kits
  • closeNo capstone
  • closeNo security ladder
Investment
Choose Your Path
Self-Paced
$97
one-time payment
  • check_circle65 lessons across 8 modules
  • check_circle13 scaffolded labs with solutions
  • check_circle56 quiz questions
  • check_circle20 SKILL.md files (7 kits)
  • check_circleLifetime access + updates
  • check_circleMock LLM for offline labs
Enroll Now
Enterprise
$199
one-time payment
  • check_circleEverything in Self-Paced
  • check_circlePrivate Discord channel
  • check_circle2 custom skills for your stack
  • check_circle1-hour onboarding call
  • check_circlePriority updates for 1 year
  • check_circleEarly access to new kits
Buy Enterprise
Cohort
$247
next cohort TBD
  • check_circleEverything in Enterprise
  • check_circle6-week structured schedule
  • check_circleWeekly live office hours
  • check_circleCapstone peer review
  • check_circleCompletion certificate
  • check_circleAlumni network
Join Cohort
FAQ
Common Questions
Do I need API keys to run the labs? expand_more
What tools/CLIs does this course cover? expand_more
How does FDSA compare to TAC or ClaudeFAST? expand_more
How long does the course take? expand_more
What's the refund policy? expand_more
Can I buy individual skill kits? expand_more
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

-    
+    
FDSA
Appearance
terminalFDSA
CoursePreviewLabsBlogEnroll — $97
NOW ENROLLING
Master Agentic Engineering
65 lessons, 13 labs, 20 skill kits. Build production-grade multi-agent systems that are secure, cost-optimized, and always-on.
Enroll Now — $97Free PreviewView Curriculum
AGENT_PLANNER v2.1
> LOADING HARNESS... DONE
> DEPLOYING 8-AGENT SYSTEM
> SECURITY: LEVEL 4 (WHITELIST)
> ALL SYSTEMS OPERATIONAL
65Lessons
13Hands-on Labs
8Modules
20Skill Kits
What You'll Build
Production-Ready Agent Systems
settings_suggest
Agent Harness
Own your harness. 6-level security ladder, verifier pattern, hooks architecture across 5 subsystems.
speed
Software Factory
Systems that build systems. Agent chains, teams, P2P communication, depth-2 delegation.
psychology
Extensible Software
Pluggable, composable, swappable. Beyond MCP, tool design patterns, CI/CD for agent configs.
all_inclusive
Always-On Agents
Run 24/7. Autoresearch loops, heartbeat execution, meta-agents that improve themselves.
terminal
Agentic Access
API-first design. Tool surface design, deployment modes, service connectors, MCP integration.
trending_up
Tokenomics
3 levels of token value. Cascade routing, pass@k evals, the 3x cost rule, production optimization.
Curriculum
8 Modules. From Zero to Production.
01
psychology
Foundations
What agents are, harness vs model, decision frameworks, agent loops.
LLM+Tools+LoopHarnessTrust
02
handyman
Architecture
Tools, context, memory, skills system, codebase patterns, workspace design.
4 PillarsMemorySkills
03
shield
Safety
6-level security ladder, hooks, verifier pattern, defense-in-depth, ACIP.
L0-L5VerifierHooks
04
hub
Orchestration
Multi-agent patterns, P-threads, F-threads, P2P, CEO Board, domain locking.
TeamsChainsP2P
05
rocket_launch
Production
CI/CD for agents, shadow deploys, observability, rollback, 5-tool stack.
DeployMonitorRollback
06
analytics
Economics
Model pricing, cascade routing, pass@k evals, cost optimization per session.
100x Range3x RuleEvals
07
architecture
Advanced
Autoresearch, meta-agents, beyond MCP, always-on employee patterns.
Self-ImprovingMeta
08
trophy
Capstone
Build production multi-agent system. Brand Monitor, CEO Board, or Code Review Pipeline.
ShipDeployRubric
Trusted By
What Engineers Are Saying
"The security ladder alone is worth the price. Finally understand how to safely deploy agents in production — something no other course teaches."
M
Marcus W.
Staff Engineer, SaaS Company
"Went from vibe-coding to actually engineering agents. The harness vs model distinction changed how I think about every agent system I build."
P
Priya S.
Lead ML Engineer, FinTech
"Best practical course on multi-agent systems I've found. The CEO Board pattern alone saved me weeks of architecture work."
T
Tomás R.
CTO, AI Startup
"I've taken TAC and ClaudeFAST. FDSA is the most comprehensive — 65 lessons with actual labs that work offline. No other course has that."
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Daniel K.
Senior Developer, E-Commerce
Your Instructor
Built by an Engineer, For Engineers
terminal
Artale
Founder, FDSA Agency
I've spent the last two years building production multi-agent systems — from CEO Boards for strategic decision-making to Brand Monitor for always-on reputation tracking, to UI Agents that generate production Vue components. This course distills everything I learned shipping real agent systems, not toy demos.
Every pattern in this course — the 6-level security ladder, P-threads, cascade routing, the verifier pattern — came from building systems that had to work reliably in production. If it's in the course, it's been battle-tested.
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FDSA
  • check65 lessons + 13 labs
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  • check8 modules (foundations to capstone)
  • checkMock LLM for offline work
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TAC
  • check14 focused lessons
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ClaudeFAST
  • check280 skill files
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Choose Your Path
Self-Paced
$97
one-time payment
  • check_circle65 lessons across 8 modules
  • check_circle13 scaffolded labs with solutions
  • check_circle56 quiz questions
  • check_circle20 SKILL.md files (7 kits)
  • check_circleLifetime access + updates
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$199
one-time payment
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  • check_circle2 custom skills for your stack
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Buy Enterprise
Cohort
$247
next cohort TBD
  • check_circleEverything in Enterprise
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FAQ
Common Questions
Do I need API keys to run the labs? expand_more
What tools/CLIs does this course cover? expand_more
How does FDSA compare to TAC or ClaudeFAST? expand_more
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FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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Labs Overview ​
13 hands-on labs covering the full agentic engineering stack. Each lab has a starter.py (fill in the blanks) and solution.py (reference answer).
Lab Index ​
LabModuleTopicEst. Time
L1: First AgentM1Single-tool agent from scratch60 min
L2a: Multi-Tool AgentM2File ops + web search75 min
L2b: Context-AwareM2Sliding window + summarization60 min
L3a: Whitelist HookM3L4 security implementation60 min
L3b: Verifier AgentM3Read-only verification75 min
L4a: Agent ChainM4YAML pipeline60 min
L4b: Multi-TeamM4Team config + domain locking90 min
L5a: ObservabilityM5SQLite tool tracing60 min
L5b: CI/CDM5Golden dataset + regression gate60 min
L6a: Eval HarnessM6pass@k evaluation60 min
L6b: Cost OptimizationM6Cascade routing45 min
L7a: AutoresearchM7Self-improving experiment loop75 min
L7b: Meta-AgentM7Agent that builds agents60 min
Running Labs ​
bash
cd course/labs/L1-first-agent/
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Labs Overview ​
13 hands-on labs covering the full agentic engineering stack. Each lab has a starter.py (fill in the blanks) and solution.py (reference answer).
Lab Index ​
LabModuleTopicEst. Time
L1: First AgentM1Single-tool agent from scratch60 min
L2a: Multi-Tool AgentM2File ops + web search75 min
L2b: Context-AwareM2Sliding window + summarization60 min
L3a: Whitelist HookM3L4 security implementation60 min
L3b: Verifier AgentM3Read-only verification75 min
L4a: Agent ChainM4YAML pipeline60 min
L4b: Multi-TeamM4Team config + domain locking90 min
L5a: ObservabilityM5SQLite tool tracing60 min
L5b: CI/CDM5Golden dataset + regression gate60 min
L6a: Eval HarnessM6pass@k evaluation60 min
L6b: Cost OptimizationM6Cascade routing45 min
L7a: AutoresearchM7Self-improving experiment loop75 min
L7b: Meta-AgentM7Agent that builds agents60 min
Running Labs ​
bash
cd course/labs/L1-first-agent/
 
 # Without API key (uses mock LLM automatically):
 python starter.py test.txt "What is this file about?"
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 # Check solution after attempting:
 python solution.py test.txt "What is this file about?"
Lab Structure ​
Each lab has:
  • starter.py — Code skeleton with TODO markers
  • solution.py — Complete reference implementation
  • No additional files needed — all labs are self-contained
Offline Mode ​
All labs include automatic mock LLM fallback. No API keys required. The mock client returns realistic, deterministic responses so you can verify your code logic without paying for API calls.
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L1: Your First Agent ​
Build a single-tool agent from scratch.
Module: M1 Foundations
Est. Time: 60 min
Files: starter.py, solution.py
Objective ​
Create an agent that reads a file and answers questions about its contents.
Concepts ​
  • LLM + Tools + Loop = Agent
  • Tool definition with input schema
  • The agent loop (think → act → observe → repeat)
  • The reasoning parameter
Starter ​
bash
cd course/labs/L1-first-agent/
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L1: Your First Agent ​
Build a single-tool agent from scratch.
Module: M1 Foundations
Est. Time: 60 min
Files: starter.py, solution.py
Objective ​
Create an agent that reads a file and answers questions about its contents.
Concepts ​
  • LLM + Tools + Loop = Agent
  • Tool definition with input schema
  • The agent loop (think → act → observe → repeat)
  • The reasoning parameter
Starter ​
bash
cd course/labs/L1-first-agent/
 python starter.py test.txt "What is this file about?"
The starter has TODO markers where you fill in:
  1. Define the read_file tool schema
  2. Implement the execute_tool function
  3. Implement the run_agent loop
  4. Wire up the main entry point
Solution ​
bash
python solution.py test.txt "What is this file about?"
Compare your implementation against the solution. Key differences to check:
  • Did you set MAX_ITERATIONS?
  • Does every tool call include reasoning?
  • Does the loop terminate on end_turn?
Checkpoints ​
  1. Tool call is made correctly (schema matches)
  2. Tool result is fed back to the LLM
  3. LLM produces final answer using tool result
  4. Loop terminates (doesn't run forever)
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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L2b: Context-Aware Agent ​
Module: M2 Architecture
Files: starter.py, solution.py
Objective ​
Implement sliding window + summarization for long agent sessions.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l2-context/
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L2b: Context-Aware Agent ​
Module: M2 Architecture
Files: starter.py, solution.py
Objective ​
Implement sliding window + summarization for long agent sessions.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l2-context/
 # Open starter file and fill in the TODOs
Checkpoints ​
  1. Build a ContextManager class
  2. Implement max_recent_turns sliding window
  3. Summarize old messages when window exceeds limit
  4. Build context from summary + recent messages
Solution ​
Compare against the solution file after attempting the starter.
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L2a: Multi-Tool Agent ​
Module: M2 Architecture
Files: starter.py, solution.py
Objective ​
Add file operations AND web search to your agent.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l2-multi-tool/
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L2a: Multi-Tool Agent ​
Module: M2 Architecture
Files: starter.py, solution.py
Objective ​
Add file operations AND web search to your agent.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l2-multi-tool/
 # Open starter file and fill in the TODOs
Checkpoints ​
  1. Define a read_file tool schema
  2. Define a search_web tool (DuckDuckGo or similar)
  3. Define a write_file tool
  4. Implement execute_tool for all three
  5. Run the agent loop with tool result feedback
Solution ​
Compare against the solution file after attempting the starter.
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L3b: Verifier Agent ​
Module: M3 Safety
Files: starter.py, solution.py
Objective ​
Create a read-only agent that checks the builder's work independently.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
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L3b: Verifier Agent ​
Module: M3 Safety
Files: starter.py, solution.py
Objective ​
Create a read-only agent that checks the builder's work independently.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l3-verifier/
 # Open starter file and fill in the TODOs
Checkpoints ​
  1. Define read-only tools (read_file, grep_search, list_files)
  2. Implement claim verification logic
  3. Report confidence level (PERFECT through FAILED)
  4. No write/edit/bash tools allowed
Solution ​
Compare against the solution file after attempting the starter.
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L3a: L4 Whitelist Hook ​
Implement a production-grade L4 security whitelist hook.
Module: M3 Safety & Security
Est. Time: 60 min
Files: starter.py, solution.py
Objective ​
Block ALL bash commands except 10 safelisted patterns. This prevents the L3 marque break (agent writing and running scripts).
Concepts ​
  • L4 whitelist (architectural security)
  • Regex pattern matching
  • Compound shell operator detection
  • Defense in depth
Starter ​
bash
cd course/labs/L3-whitelist-hook/
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L3a: L4 Whitelist Hook ​
Implement a production-grade L4 security whitelist hook.
Module: M3 Safety & Security
Est. Time: 60 min
Files: starter.py, solution.py
Objective ​
Block ALL bash commands except 10 safelisted patterns. This prevents the L3 marque break (agent writing and running scripts).
Concepts ​
  • L4 whitelist (architectural security)
  • Regex pattern matching
  • Compound shell operator detection
  • Defense in depth
Starter ​
bash
cd course/labs/L3-whitelist-hook/
 python starter.py
The starter has:
  1. A safelist patterns list (currently empty)
  2. A compound operators detector (currently empty)
  3. A whitelist check function (currently TODO)
  4. Test cases for both ALLOWED and BLOCKED commands
Solution ​
bash
python solution.py
Key Design Rules ​
  1. Pin specific scripts: ^npm test$ not ^npm .*$
  2. Never pattern-match an interpreter: ^python .*$ allows running any script
  3. Block compound operators before regex
  4. Audit log all blocks
Test Cases ​
npm test          → ALLOW
 git status        → ALLOW
 rm -rf target/    → BLOCK
 python cleanup.py → BLOCK (L3 marque break)
 npm test && rm -rf / → BLOCK (compound operator)
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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L4a: Agent Chain ​
Module: M4 Orchestration
Files: starter.yaml, solution.yaml
Objective ​
Create a YAML-defined plan-build-review-verify pipeline.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l4-agent-chain/
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L4a: Agent Chain ​
Module: M4 Orchestration
Files: starter.yaml, solution.yaml
Objective ​
Create a YAML-defined plan-build-review-verify pipeline.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l4-agent-chain/
 # Open starter file and fill in the TODOs
Checkpoints ​
  1. Define planner step with output format requirements
  2. Define builder step with implementation rules
  3. Define reviewer step with checklist criteria
  4. Define verifier step with confidence reporting
Solution ​
Compare against the solution file after attempting the starter.
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L4b: Multi-Team Config ​
Module: M4 Orchestration
Files: starter-config.yaml, solution-config.yaml
Objective ​
Set up orchestrator + 2 teams with domain locking.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
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L4b: Multi-Team Config ​
Module: M4 Orchestration
Files: starter-config.yaml, solution-config.yaml
Objective ​
Set up orchestrator + 2 teams with domain locking.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l4-multi-team/
 # Open starter file and fill in the TODOs
Checkpoints ​
  1. Define orchestrator with delegate-only tools
  2. Configure Engineering team with lead + members
  3. Configure Validation team with domain permissions
  4. Set per-agent permissions (read/upsert/delete scope)
Solution ​
Compare against the solution file after attempting the starter.
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L5b: CI/CD Pipeline ​
Module: M5 Production
Files: starter.py, solution.py
Objective ​
Create golden dataset + automated regression gate.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l5-cicd/
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L5b: CI/CD Pipeline ​
Module: M5 Production
Files: starter.py, solution.py
Objective ​
Create golden dataset + automated regression gate.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l5-cicd/
 # Open starter file and fill in the TODOs
Checkpoints ​
  1. Define golden test cases (input, expected tools, expected output)
  2. Implement score_case evaluation function
  3. Run pass@k evaluation (k=1, 3, 5)
  4. Gate deployment on pass rate threshold
Solution ​
Compare against the solution file after attempting the starter.
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L5a: Observability ​
Module: M5 Production
Files: starter.py, solution.py
Objective ​
Trace every tool call + LLM completion to SQLite.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
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L5a: Observability ​
Module: M5 Production
Files: starter.py, solution.py
Objective ​
Trace every tool call + LLM completion to SQLite.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l5-observability/
 # Open starter file and fill in the TODOs
Checkpoints ​
  1. Define SQLite schema for tool_calls and llm_completions
  2. Implement ObservabilityTracker class
  3. Log tool calls with params, result, duration
  4. Log LLM completions with token counts and cost
  5. Generate session summary report
Solution ​
Compare against the solution file after attempting the starter.
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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L6b: Cost Optimization ​
Module: M6 Economics
Files: starter.py, solution.py
Objective ​
Profile a session, find savings, implement cascade routing.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
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L6b: Cost Optimization ​
Module: M6 Economics
Files: starter.py, solution.py
Objective ​
Profile a session, find savings, implement cascade routing.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l6-cost-optimization/
 # Open starter file and fill in the TODOs
Checkpoints ​
  1. Define model cost profiles (input/output $/M tokens)
  2. Calculate all-Opus session cost
  3. Implement cascade routing map (Flash/Sonnet/Opus by task)
  4. Calculate savings percentage
Solution ​
Compare against the solution file after attempting the starter.
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L6a: Eval Harness ​
Module: M6 Economics
Files: starter.py, solution.py
Objective ​
Build golden Q&A pairs + pass@k scoring system.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
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L6a: Eval Harness ​
Module: M6 Economics
Files: starter.py, solution.py
Objective ​
Build golden Q&A pairs + pass@k scoring system.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l6-eval-harness/
 # Open starter file and fill in the TODOs
Checkpoints ​
  1. Define 10+ test cases with tool and output expectations
  2. Implement EvalHarness class with score_case
  3. Compute pass@1, pass@3, pass@5 metrics
  4. Support weighted scoring by case importance
Solution ​
Compare against the solution file after attempting the starter.
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L7a: Autoresearch Loop ​
Build a self-improving agent with integrity guards.
Module: M7 Advanced Topics
Est. Time: 75 min
Files: starter.py, solution.py
Objective ​
Create an agent that runs experiments, measures its own performance, logs results, and decides whether to keep or discard each change.
Concepts ​
  • Autoresearch: run → measure → log → decide
  • Code hashing to detect grinding
  • Median vs best comparison
  • Reward hack defense
Starter ​
bash
cd course/labs/L7-autoresearch/
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L7a: Autoresearch Loop ​
Build a self-improving agent with integrity guards.
Module: M7 Advanced Topics
Est. Time: 75 min
Files: starter.py, solution.py
Objective ​
Create an agent that runs experiments, measures its own performance, logs results, and decides whether to keep or discard each change.
Concepts ​
  • Autoresearch: run → measure → log → decide
  • Code hashing to detect grinding
  • Median vs best comparison
  • Reward hack defense
Starter ​
bash
cd course/labs/L7-autoresearch/
 python starter.py
Solution ​
bash
python solution.py
Experiment Log Format ​
jsonl
{"run": 1, "status": "baseline", "metric": {"name": "latency", "value": 52, "unit": "ms"}}
 {"run": 2, "status": "keep", "metric": {"name": "latency", "value": 46}, "deltaPct": -11.5}
 {"run": 3, "status": "discard", "metric": {"name": "latency", "value": 53}, "deltaPct": +1.9}
Integrity Guards ​
ThreatDetectionPrevention
Grinding (same code re-run)Code hash comparisonSkip run
Noise-chasingMedian vs best comparisonUse median, not best
Reward hackingTiming function isolationVerify computation not shortcut
Test set leakageTest data hash verificationAssert data unchanged
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L7b: Meta-Agent ​
Module: M7 Advanced
Files: starter.py, solution.py
Objective ​
Generate a new agent persona from documentation.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l7-meta-agent/
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L7b: Meta-Agent ​
Module: M7 Advanced
Files: starter.py, solution.py
Objective ​
Generate a new agent persona from documentation.
Concepts ​
Refer to the corresponding module for full concept explanations.
Starter ​
bash
cd course/labs/l7-meta-agent/
 # Open starter file and fill in the TODOs
Checkpoints ​
  1. Parse user description for agent requirements
  2. Infer appropriate tools from domain keywords
  3. Generate system prompt with persona and rules
  4. Create mental model YAML template
  5. Save all files to a named agent directory
Solution ​
Compare against the solution file after attempting the starter.
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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Competitive Analysis: Agentic Engineering Course Landscape ​
Live Comparison: walkinglabs.github.io Course ​
I fetched the actual Anthropic Learn Harness Engineering website to compare directly.
Their Structure (12 lectures, 6 projects) ​
#Lecture TopicProjects
1Why Capable Agents Still Fail (50% SWE-bench, harness problem)Project 01: Prompt-Only vs Rules-First
2What a Harness Actually Is (5 subsystems: instructions, tools, environment, state, verification)Project 02: Agent-Readable Workspace
3Why the Repository Must Become the System of RecordProject 03: Multi-Session Continuity
4Why One Giant Instruction File FailsProject 04: Runtime Feedback & Scope Control
5Why Long-Running Tasks Lose ContinuityProject 05: Self-Verification & Role Separation
6Why Initialization Needs Its Own PhaseProject 06: Full Harness (observability + debugging)
7Why Agents Overreach and Under-Finish
8Why Feature Lists Are Harness Primitives
9Why Agents Declare Victory Too Early
10Why End-to-End Testing Changes Results
11Why Observability Belongs Inside the Harness
12Why Every Session Must Leave a Clean State
Key Differences: Your Course vs Theirs ​
Their strength: Deep philosophical focus on the "harness" concept (instructions, tools, environment, state, verification). Each lecture is a "why" — explaining root causes of agent failure. Clean, professional presentation with VitePress.
Their gaps (what they DON'T cover that you do):
  • ❌ Zero security content — no bash security ladder, no damage control, no hook architecture
  • ❌ Zero multi-agent orchestration — no teams, chains, delegation, P2P, CEO Board
  • ❌ Zero production deployment — no CI/CD, shadow deploys, rollback, monitoring
  • ❌ Zero model economics — no pricing comparison, cascade routing, cost optimization
  • ❌ Zero autoresearch — no self-improving agents
  • ❌ Zero meta-agents — no agents that build agents
  • ❌ Zero non-technical frameworks — no decision frameworks for managers
Their unique content (what they cover that you could add):
  • ✓ Harness = 5 subsystems model (instructions, tools, environment, state, verification) — Your "harness" framing could adopt this language
  • ✓ The "repo IS the spec" philosophy — Stronger emphasis on repo-as-source-of-truth
  • ✓ Agent-readable workspace design patterns
  • ✓ Session continuity across long-running tasks
  • ✓ Initialization as a distinct phase
  • ✓ Feature lists as harness primitives (structured feature tagging)
  • ✓ Clean state between sessions
  • ✓ Professionally designed VitePress site with multi-language support (11 languages)
  • ✓ "Try Harness" button linking to templates and a live agentic session
Live Comparison: ClaudeFAST (claudefa.st) ​
ClaudeFAST is a commercial product/blog selling Claude Code kits ($89-$299). Their blog reveals what content they think is valuable.
ClaudeFAST Blog Topics ​
TopicPrice PointOur Coverage
Agent Manager role (job scope, DRI, 90-day playbook)Free blogM5 — Production patterns covers deployment, but not organizational roles
Large codebases (monorepos, legacy, 8 strategies)Free blogM2 — Architecture covers context management, not specifically monorepo
Path-scoped skills (file-path-based skill loading)Free blogM4 briefly covers skills; we don't have a dedicated skills lesson
Subdirectory CLAUDE.md (tree-walking context)Free blogNot covered — interesting pattern for large projects
AI Layer thesis (harness > model)Free blogM1 — We cover this extensively
Self-improving CLAUDE.md (stop hook reviews changes)Free blogM3 — Hooks architecture covers this pattern
LSP MCP Server (symbol-level search)Free blogNot covered — MCP server for code intelligence
Plugins distribution (shareable bundles)Free blogNot covered — packaging/sharing agent configs
Their Product Kits ​
KitPriceWhat It IsGap in Our Course
Code Kit$89AI Development FrameworkWe have this code-wise, not packaged
Growth Kit$89Sales, Marketing ResearchWe don't cover agent use cases for sales/marketing
Complete Kit$149Code + Growth KitsBundled value
Shopify Kit$199Ecom Consulting for ClaudeDomain-specific (Shopify)
Ultimate Kit$299Complete + ShopifyPremium bundle
What Their 16 Agents + 280 Skills Tell Us ​
They claim "16 agents, 280 skill files, 90,000+ lines of workflows." This suggests:
  • Breadth over depth: 280 skills likely means many small, focused skill files
  • Agent diversity: 16 agents implies specialized personas (not just coding)
  • Production scale: 90K lines is substantial — they've invested heavily
Actionable gap: We should consider adding a "Skills System Deep Dive" lesson and a "Plugin/Kit Distribution" lesson showing how to package agent configs for sharing.
What You Should Borrow ​
  1. Adopt the "5 subsystems" framing for your harness definition — it's clean and teachable
  2. Your M3 Safety module maps to their "verification" subsystem — lead with that connection
  3. Add a "clean state" lesson — their Lecture 12 is a good pattern
  4. Add "repo IS the spec" to M1 — it's a powerful mental model
  5. Consider VitePress for the final site — it's what Anthropic uses and it's excellent for docs-based courses
Course Comparison Matrix ​
DimensionYour CourseIndyDevDan TACAnthropic HarnessDeepLearning.AILangChain Academy
PriceNot set$97-147Free$45/mo subFree
FormatSelf-paced docs + labsVideo + cohortDocs onlyVideo + notebookDocs + code
Hours44-62~20~301-2 per course~10
Labs13 scaffolded~5 demosMany exercises~3 per course~8
Code accessFull sourceMember assetsOpen sourceNotebooksOpen source
CertificateNot yet✓✗✓✗
Module-by-Module Comparison ​
Foundations (What is an agent, harness vs model, decision frameworks) ​
Your CourseTACAnthropicDL.AILangChain
Agent = LLM + Tools + Loop✓ Covers✓ Covers✓ Covers✓ Covers
Harness vs Model distinctionDIFFERENTIATOR✗ Not covered✗ Not covered✗ Not covered
Decision framework (4-question filter)DIFFERENTIATOR✗ Not covered✗ Not covered✗ Not covered
Vibe coding vs Agentic engineering (5 rules)DIFFERENTIATOR✗ Not covered✗ Not covered✗ Not covered
Verdict: Your foundations module has 3 unique differentiators not found in any competitor course.
Agent Architecture (Tools, loops, context, memory) ​
Your CourseTACAnthropicDL.AILangChain
Four pillars (Tools/Loop/Context/Memory)✓ Covered✓ Partial✓ Partial✓ Partial
Tool design patterns (MCP/CLI/Script/Skill)UNIQUE✗✗✗
Agent loop variants (5 levels)✓ Covered✓ Partial✓ Partial✓ Basic
Context window management✓ Covered✓ Covered✓ Covered✓ Covered
The Reasoning ParameterDIFFERENTIATOR✗✗✗
Codebase architectures (4 patterns)UNIQUE✗✗✗
Verdict: Tool design channels and the reasoning parameter are unique. Codebase architectures (from your single-file-agents research) are not taught anywhere else.
Safety & Security (5-level ladder, hooks, verifier) ​
Your CourseTACAnthropicDL.AILangChain
5-level bash security ladderUNIQUE✗✗✗
L3 marquee break (agent writes cleanup.py)UNIQUE✗✗✗
Damage control (3 access levels)UNIQUE✗✗✗
Hook architecture (13 lifecycle events)✓ Covered✓ Partial✓ Covered✗
Verifier pattern (builder + verifier)UNIQUE✗✗✗
Defense-in-depth stackingUNIQUE✗✗✗
Probability math (1% × 100 turns = 63%)UNIQUE✗✗✗
Verdict: This is your STRONGEST differentiator. No other course covers agent security at this depth. The bash-damage-from-within material is genuinely novel.
Multi-Agent Orchestration (teams, chains, P2P) ​
Your CourseTACAnthropicDL.AILangChain
3 orchestration patterns (dispatch/pipeline/P2P)✓ Covered✓ Covered✓ Basic✓ Partial
Depth-2 delegation (orchestrator→lead→worker)DIFFERENTIATOR✓ Covered✗✗
Mental models (agent-owned expertise files)DIFFERENTIATOR✓ Covered✗✗
Domain locking (per-agent permissions)DIFFERENTIATOR✓ Covered✗✗
TillDone task disciplineUNIQUE✗✗✗
Agent chains ($INPUT/$ORIGINAL YAML)DIFFERENTIATOR✓ Covered✗✗
Pi-to-Pi peer-to-peer comsUNIQUE✗✗✗
CEO Board System (8 agents, adversarial debate)UNIQUE✗✗✗
UI Agents (12 agents, brand→product→tree)UNIQUE✗✗✗
Verdict: You share depth-2 delegation and agent chains with TAC, but add 6 unique patterns (TillDone, P2P coms, CEO Board, UI Agents, mental model self-improve, domain locking).
Production Patterns (deployment, CI/CD, monitoring) ​
Your CourseTACAnthropicDL.AILangChain
CI/CD for agents (golden datasets)DIFFERENTIATOR✗✓ Partial✗
Shadow deployments✓ Covered✗✗✗
Rollback strategies (prompt + model + config)DIFFERENTIATOR✗✗✗
Observability (SQLite tracing)✓ Covered✗✓ Covered✗
Agent-specific alertingDIFFERENTIATOR✗✗✗
Deployment modes (local/auth/public)DIFFERENTIATOR✗✗✗
Budget control (warnings vs hard stops)DIFFERENTIATOR✗✗✗
Verdict: Anthropic and LangChain cover basic observability, but no course covers agent-specific deployment patterns (shadow deploy, rollback, budgeting). This is a greenfield differentiator.
Economics & Evaluation (pricing, cost optimization, evals) ​
Your CourseTACAnthropicDL.AILangChain
LLM pricing landscape (100x range)DIFFERENTIATOR✗✗✗
Cascade routing (cheap/expensive model mix)DIFFERENTIATOR✗✗✗
Cost per session math (3x rule)UNIQUE✗✗✗
pass@k / pass^k evaluation✓ Covered✗✓ Basic✗
Golden datasets + regression testing✓ Covered✗✓ Covered✗
A/B testing agents (canary deploys)DIFFERENTIATOR✗✗✗
Verdict: The 3x rule and practical cascade routing math are unique. Most courses skip cost optimization entirely.
Advanced Topics (autoresearch, meta-agents) ​
Your CourseTACAnthropicDL.AILangChain
Autoresearch (self-improving agents)UNIQUE✗✗✗
Integrity guards (code hashing, grind detect)UNIQUE✗✗✗
Meta-agents (agents that build agents)DIFFERENTIATOR✓ Covered✗✗
Beyond MCP (context cost trade-off)UNIQUE✗✗✗
Mac Mini Agent (physical sandbox)UNIQUE✗✗✗
Always-on agents (voice→CLI bridge)UNIQUE✗✗✗
Framework comparison (paperclip/multica/cabinet)UNIQUE✗✗✗
Verdict: Autoresearch with integrity guards (from your mythos-learnings research) is entirely unique. No course teaches agents that improve themselves.
Differentiator Summary ​
Content Only Your Course Has (UNIQUE) ​
TopicSource MaterialWhy No One Else Has It
5-level bash security ladderbash-damage-from-within (your repo)IndyDevDan created this, you have the source
Verifier pattern (builder + verifier)the-verifier-agent (your repo)You built the production system
Autoresearch loop + integrity guardsmythos-learnings.md, brand-monitorBased on Anthropic's own 244-page Mythos paper
3x cost ruleOriginal analysisSynthesized from your aiproxy benchmarks
CEO Board System (8 agents)ceo-agents (your repo)You built the working system
UI Agents (12 teams, brand hierarchy)ui-agents (your repo)You built the working system
TillDone task disciplinepi-vs-claude-code/tilldone.tsExtension you studied and documented
Pi-to-Pi peer-to-peer comspi-vs-claude-code/coms.tsNovel P2P agent architecture
Beyond MCP context cost matrixbeyond-mcp/ (your repo)Novel research comparing 4 approaches
Mac Mini Agent (physical sandbox)mac-mini-agent (your repo)Physical + cloud isolation pattern
Codebase architectures (4 patterns)single-file-agentsFrom your architectural research
Probability math (1%×100turns=63%)bash-damage-from-withinNovel risk quantification
Always-on voice→CLI bridgealways-on-ai-assistantVoice-operated agent pattern
Content Shared With TAC Only (DIFFERENTIATOR vs everyone else) ​
TopicYour CoverageTAC Coverage
Depth-2 delegationFull with config examplesTeaches the pattern
Agent chains (YAML pipelines)Full with $INPUT/$ORIGINALTeaches the pattern
Agent mental modelsFull with self-improve commandsTeaches the concept
Domain lockingFull permission matrixTeaches the concept
Hook architecture13 lifecycle eventsFocuses on practical hooks
Meta-agentsIncludes research expert patternTeaches the concept
Content Commoditized (Everyone covers it) ​
TopicYour AngleWhat's Different?
Agent loops5-level progressionYour levels match Anthropic's "Building Effective Agents"
Tool callingReasoning parameterNovel addition not in other courses
Context managementHybrid approachStandard approach
pass@k evaluationWorking harness codeImplementation matters more than concept
Price Positioning ​
CompetitorPriceYour Relative Value
Anthropic Learn HarnessFreeYou offer more depth in security, production, and economics
LangChain AcademyFreeYou are framework-agnostic (LangChain is framework-specific)
DeepLearning.AI$45/mo (1-2hr per course)Your course is 20-30x more content
IndyDevDan TAC$97-147You have all their content + 15 unique topics not in TAC
Maven bootcamps$200-500Cohort format with mentorship
Certification tracks$500+You'd need certification infrastructure
Market Positioning Recommendation ​
Based on this analysis, here's where your course sits in the market:
                    FRAMEWORK-SPECIFIC
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Competitive Analysis: Agentic Engineering Course Landscape ​
Live Comparison: walkinglabs.github.io Course ​
I fetched the actual Anthropic Learn Harness Engineering website to compare directly.
Their Structure (12 lectures, 6 projects) ​
#Lecture TopicProjects
1Why Capable Agents Still Fail (50% SWE-bench, harness problem)Project 01: Prompt-Only vs Rules-First
2What a Harness Actually Is (5 subsystems: instructions, tools, environment, state, verification)Project 02: Agent-Readable Workspace
3Why the Repository Must Become the System of RecordProject 03: Multi-Session Continuity
4Why One Giant Instruction File FailsProject 04: Runtime Feedback & Scope Control
5Why Long-Running Tasks Lose ContinuityProject 05: Self-Verification & Role Separation
6Why Initialization Needs Its Own PhaseProject 06: Full Harness (observability + debugging)
7Why Agents Overreach and Under-Finish
8Why Feature Lists Are Harness Primitives
9Why Agents Declare Victory Too Early
10Why End-to-End Testing Changes Results
11Why Observability Belongs Inside the Harness
12Why Every Session Must Leave a Clean State
Key Differences: Your Course vs Theirs ​
Their strength: Deep philosophical focus on the "harness" concept (instructions, tools, environment, state, verification). Each lecture is a "why" — explaining root causes of agent failure. Clean, professional presentation with VitePress.
Their gaps (what they DON'T cover that you do):
  • ❌ Zero security content — no bash security ladder, no damage control, no hook architecture
  • ❌ Zero multi-agent orchestration — no teams, chains, delegation, P2P, CEO Board
  • ❌ Zero production deployment — no CI/CD, shadow deploys, rollback, monitoring
  • ❌ Zero model economics — no pricing comparison, cascade routing, cost optimization
  • ❌ Zero autoresearch — no self-improving agents
  • ❌ Zero meta-agents — no agents that build agents
  • ❌ Zero non-technical frameworks — no decision frameworks for managers
Their unique content (what they cover that you could add):
  • ✓ Harness = 5 subsystems model (instructions, tools, environment, state, verification) — Your "harness" framing could adopt this language
  • ✓ The "repo IS the spec" philosophy — Stronger emphasis on repo-as-source-of-truth
  • ✓ Agent-readable workspace design patterns
  • ✓ Session continuity across long-running tasks
  • ✓ Initialization as a distinct phase
  • ✓ Feature lists as harness primitives (structured feature tagging)
  • ✓ Clean state between sessions
  • ✓ Professionally designed VitePress site with multi-language support (11 languages)
  • ✓ "Try Harness" button linking to templates and a live agentic session
Live Comparison: ClaudeFAST (claudefa.st) ​
ClaudeFAST is a commercial product/blog selling Claude Code kits ($89-$299). Their blog reveals what content they think is valuable.
ClaudeFAST Blog Topics ​
TopicPrice PointOur Coverage
Agent Manager role (job scope, DRI, 90-day playbook)Free blogM5 — Production patterns covers deployment, but not organizational roles
Large codebases (monorepos, legacy, 8 strategies)Free blogM2 — Architecture covers context management, not specifically monorepo
Path-scoped skills (file-path-based skill loading)Free blogM4 briefly covers skills; we don't have a dedicated skills lesson
Subdirectory CLAUDE.md (tree-walking context)Free blogNot covered — interesting pattern for large projects
AI Layer thesis (harness > model)Free blogM1 — We cover this extensively
Self-improving CLAUDE.md (stop hook reviews changes)Free blogM3 — Hooks architecture covers this pattern
LSP MCP Server (symbol-level search)Free blogNot covered — MCP server for code intelligence
Plugins distribution (shareable bundles)Free blogNot covered — packaging/sharing agent configs
Their Product Kits ​
KitPriceWhat It IsGap in Our Course
Code Kit$89AI Development FrameworkWe have this code-wise, not packaged
Growth Kit$89Sales, Marketing ResearchWe don't cover agent use cases for sales/marketing
Complete Kit$149Code + Growth KitsBundled value
Shopify Kit$199Ecom Consulting for ClaudeDomain-specific (Shopify)
Ultimate Kit$299Complete + ShopifyPremium bundle
What Their 16 Agents + 280 Skills Tell Us ​
They claim "16 agents, 280 skill files, 90,000+ lines of workflows." This suggests:
  • Breadth over depth: 280 skills likely means many small, focused skill files
  • Agent diversity: 16 agents implies specialized personas (not just coding)
  • Production scale: 90K lines is substantial — they've invested heavily
Actionable gap: We should consider adding a "Skills System Deep Dive" lesson and a "Plugin/Kit Distribution" lesson showing how to package agent configs for sharing.
What You Should Borrow ​
  1. Adopt the "5 subsystems" framing for your harness definition — it's clean and teachable
  2. Your M3 Safety module maps to their "verification" subsystem — lead with that connection
  3. Add a "clean state" lesson — their Lecture 12 is a good pattern
  4. Add "repo IS the spec" to M1 — it's a powerful mental model
  5. Consider VitePress for the final site — it's what Anthropic uses and it's excellent for docs-based courses
Course Comparison Matrix ​
DimensionYour CourseIndyDevDan TACAnthropic HarnessDeepLearning.AILangChain Academy
PriceNot set$97-147Free$45/mo subFree
FormatSelf-paced docs + labsVideo + cohortDocs onlyVideo + notebookDocs + code
Hours44-62~20~301-2 per course~10
Labs13 scaffolded~5 demosMany exercises~3 per course~8
Code accessFull sourceMember assetsOpen sourceNotebooksOpen source
CertificateNot yet✓✗✓✗
Module-by-Module Comparison ​
Foundations (What is an agent, harness vs model, decision frameworks) ​
Your CourseTACAnthropicDL.AILangChain
Agent = LLM + Tools + Loop✓ Covers✓ Covers✓ Covers✓ Covers
Harness vs Model distinctionDIFFERENTIATOR✗ Not covered✗ Not covered✗ Not covered
Decision framework (4-question filter)DIFFERENTIATOR✗ Not covered✗ Not covered✗ Not covered
Vibe coding vs Agentic engineering (5 rules)DIFFERENTIATOR✗ Not covered✗ Not covered✗ Not covered
Verdict: Your foundations module has 3 unique differentiators not found in any competitor course.
Agent Architecture (Tools, loops, context, memory) ​
Your CourseTACAnthropicDL.AILangChain
Four pillars (Tools/Loop/Context/Memory)✓ Covered✓ Partial✓ Partial✓ Partial
Tool design patterns (MCP/CLI/Script/Skill)UNIQUE✗✗✗
Agent loop variants (5 levels)✓ Covered✓ Partial✓ Partial✓ Basic
Context window management✓ Covered✓ Covered✓ Covered✓ Covered
The Reasoning ParameterDIFFERENTIATOR✗✗✗
Codebase architectures (4 patterns)UNIQUE✗✗✗
Verdict: Tool design channels and the reasoning parameter are unique. Codebase architectures (from your single-file-agents research) are not taught anywhere else.
Safety & Security (5-level ladder, hooks, verifier) ​
Your CourseTACAnthropicDL.AILangChain
5-level bash security ladderUNIQUE✗✗✗
L3 marquee break (agent writes cleanup.py)UNIQUE✗✗✗
Damage control (3 access levels)UNIQUE✗✗✗
Hook architecture (13 lifecycle events)✓ Covered✓ Partial✓ Covered✗
Verifier pattern (builder + verifier)UNIQUE✗✗✗
Defense-in-depth stackingUNIQUE✗✗✗
Probability math (1% × 100 turns = 63%)UNIQUE✗✗✗
Verdict: This is your STRONGEST differentiator. No other course covers agent security at this depth. The bash-damage-from-within material is genuinely novel.
Multi-Agent Orchestration (teams, chains, P2P) ​
Your CourseTACAnthropicDL.AILangChain
3 orchestration patterns (dispatch/pipeline/P2P)✓ Covered✓ Covered✓ Basic✓ Partial
Depth-2 delegation (orchestrator→lead→worker)DIFFERENTIATOR✓ Covered✗✗
Mental models (agent-owned expertise files)DIFFERENTIATOR✓ Covered✗✗
Domain locking (per-agent permissions)DIFFERENTIATOR✓ Covered✗✗
TillDone task disciplineUNIQUE✗✗✗
Agent chains ($INPUT/$ORIGINAL YAML)DIFFERENTIATOR✓ Covered✗✗
Pi-to-Pi peer-to-peer comsUNIQUE✗✗✗
CEO Board System (8 agents, adversarial debate)UNIQUE✗✗✗
UI Agents (12 agents, brand→product→tree)UNIQUE✗✗✗
Verdict: You share depth-2 delegation and agent chains with TAC, but add 6 unique patterns (TillDone, P2P coms, CEO Board, UI Agents, mental model self-improve, domain locking).
Production Patterns (deployment, CI/CD, monitoring) ​
Your CourseTACAnthropicDL.AILangChain
CI/CD for agents (golden datasets)DIFFERENTIATOR✗✓ Partial✗
Shadow deployments✓ Covered✗✗✗
Rollback strategies (prompt + model + config)DIFFERENTIATOR✗✗✗
Observability (SQLite tracing)✓ Covered✗✓ Covered✗
Agent-specific alertingDIFFERENTIATOR✗✗✗
Deployment modes (local/auth/public)DIFFERENTIATOR✗✗✗
Budget control (warnings vs hard stops)DIFFERENTIATOR✗✗✗
Verdict: Anthropic and LangChain cover basic observability, but no course covers agent-specific deployment patterns (shadow deploy, rollback, budgeting). This is a greenfield differentiator.
Economics & Evaluation (pricing, cost optimization, evals) ​
Your CourseTACAnthropicDL.AILangChain
LLM pricing landscape (100x range)DIFFERENTIATOR✗✗✗
Cascade routing (cheap/expensive model mix)DIFFERENTIATOR✗✗✗
Cost per session math (3x rule)UNIQUE✗✗✗
pass@k / pass^k evaluation✓ Covered✗✓ Basic✗
Golden datasets + regression testing✓ Covered✗✓ Covered✗
A/B testing agents (canary deploys)DIFFERENTIATOR✗✗✗
Verdict: The 3x rule and practical cascade routing math are unique. Most courses skip cost optimization entirely.
Advanced Topics (autoresearch, meta-agents) ​
Your CourseTACAnthropicDL.AILangChain
Autoresearch (self-improving agents)UNIQUE✗✗✗
Integrity guards (code hashing, grind detect)UNIQUE✗✗✗
Meta-agents (agents that build agents)DIFFERENTIATOR✓ Covered✗✗
Beyond MCP (context cost trade-off)UNIQUE✗✗✗
Mac Mini Agent (physical sandbox)UNIQUE✗✗✗
Always-on agents (voice→CLI bridge)UNIQUE✗✗✗
Framework comparison (paperclip/multica/cabinet)UNIQUE✗✗✗
Verdict: Autoresearch with integrity guards (from your mythos-learnings research) is entirely unique. No course teaches agents that improve themselves.
Differentiator Summary ​
Content Only Your Course Has (UNIQUE) ​
TopicSource MaterialWhy No One Else Has It
5-level bash security ladderbash-damage-from-within (your repo)IndyDevDan created this, you have the source
Verifier pattern (builder + verifier)the-verifier-agent (your repo)You built the production system
Autoresearch loop + integrity guardsmythos-learnings.md, brand-monitorBased on Anthropic's own 244-page Mythos paper
3x cost ruleOriginal analysisSynthesized from your aiproxy benchmarks
CEO Board System (8 agents)ceo-agents (your repo)You built the working system
UI Agents (12 teams, brand hierarchy)ui-agents (your repo)You built the working system
TillDone task disciplinepi-vs-claude-code/tilldone.tsExtension you studied and documented
Pi-to-Pi peer-to-peer comspi-vs-claude-code/coms.tsNovel P2P agent architecture
Beyond MCP context cost matrixbeyond-mcp/ (your repo)Novel research comparing 4 approaches
Mac Mini Agent (physical sandbox)mac-mini-agent (your repo)Physical + cloud isolation pattern
Codebase architectures (4 patterns)single-file-agentsFrom your architectural research
Probability math (1%×100turns=63%)bash-damage-from-withinNovel risk quantification
Always-on voice→CLI bridgealways-on-ai-assistantVoice-operated agent pattern
Content Shared With TAC Only (DIFFERENTIATOR vs everyone else) ​
TopicYour CoverageTAC Coverage
Depth-2 delegationFull with config examplesTeaches the pattern
Agent chains (YAML pipelines)Full with $INPUT/$ORIGINALTeaches the pattern
Agent mental modelsFull with self-improve commandsTeaches the concept
Domain lockingFull permission matrixTeaches the concept
Hook architecture13 lifecycle eventsFocuses on practical hooks
Meta-agentsIncludes research expert patternTeaches the concept
Content Commoditized (Everyone covers it) ​
TopicYour AngleWhat's Different?
Agent loops5-level progressionYour levels match Anthropic's "Building Effective Agents"
Tool callingReasoning parameterNovel addition not in other courses
Context managementHybrid approachStandard approach
pass@k evaluationWorking harness codeImplementation matters more than concept
Price Positioning ​
CompetitorPriceYour Relative Value
Anthropic Learn HarnessFreeYou offer more depth in security, production, and economics
LangChain AcademyFreeYou are framework-agnostic (LangChain is framework-specific)
DeepLearning.AI$45/mo (1-2hr per course)Your course is 20-30x more content
IndyDevDan TAC$97-147You have all their content + 15 unique topics not in TAC
Maven bootcamps$200-500Cohort format with mentorship
Certification tracks$500+You'd need certification infrastructure
Market Positioning Recommendation ​
Based on this analysis, here's where your course sits in the market:
                    FRAMEWORK-SPECIFIC
                     │
     $0             ││ LangChain Academy           │
                    ││                             │
@@ -41,7 +41,7 @@
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                     FRAMEWORK-AGNOSTIC
Your competitive moat: You are framework-agnostic (like Anthropic, unlike LangChain/DL.AI) AND you have hands-on labs (like TAC, unlike Anthropic). No other course occupies both quadrants.
Unique selling points to emphasize:
  1. "From the engineer who reverse-engineered Anthropic's Mythos paper — learn what the frontier models CAN'T do"
  2. "13 runnable labs with starter code AND solutions"
  3. "The only course with a working verifier agent, bash security ladder, and autoresearch loop"
  4. "Based on 25,000+ files of production agent systems — not tutorials, but battle scars"
Pricing recommendation: $97-147 positions you directly against TAC with MORE content. $197-247 positions you as premium (justified by original research + working systems source code).
Validation: ClaudeFAST Articles (Published May 2026) ​
Fetched both articles to verify our course content against real published material.
Article 1: "The Agent Manager: Who Owns Claude Code?" ​
Published by: ClaudeFAST, citing Anthropic's May 2026 terminology
Thesis: The Agent Manager role exists because enterprises need someone to own the harness.
Their 3 failure modes without an agent manager:
  1. Tribal knowledge — Every dev evolves a personal AI layer; nothing is shared
  2. Inconsistent results — Same model, same codebase, different output quality
  3. Security drift — Permissions configured ad hoc, MCP servers with unbounded scope
Their 5 areas of ownership (maps to the 5 harness subsystems):
  • Maps to Instructions (CLAUDE.md, skills standardization)
  • Maps to Tools (MCP servers, plugin curation)
  • Maps to Environment (init scripts, workspace consistency)
  • Maps to State (session continuity, shared config)
  • Maps to Verification (hooks, quality gates)
Our coverage in M5: We added the Agent Manager role lesson (5.2c) with the 90-day playbook, plus the 5-tool production stack case study (5.2b). Both align exactly with ClaudeFAST's framing. ✅
Grade: A — Our coverage matches the published standard.
Article 2: "Thread-Based Engineering: Scale Claude Code Sessions" ​
Published by: ClaudeFAST
Thesis: 6 fundamental thread patterns that scale AI-assisted engineering work.
Their 6 thread types vs our coverage:
ThreadClaudeFAST defines it asOur CoverageGrade
Base ThreadPrompt → Tool Calls → ReviewM1 agent loop✅ A
P-ThreadParallel instances (Boris runs 15)M4, added this session✅ A
L-ThreadLong-running, hours+M7 always-on✅ A
B-ThreadAgents managing agentsM4 delegation✅ A
F-ThreadFusion, N agents 1 winnerM4, added this session✅ A
C-ThreadCheckpoint gatesM5 HITL✅ A
ClaudeFAST explicitly names Boris Cherny (creator of Claude Code) running 5 tmux tabs + 5-10 web instances = 10-15 parallel P-threads. This matches HypeMan's psmux + mprocs stack exactly.
Our coverage in M4: All 6 thread types covered. ✅
What This Validates ​
  1. REFERENCE-STACK.md is production-accurate — Your stack mirrors Boris Cherny's own setup and ClaudeFAST's documented patterns.
  2. Agent Manager role is real — Anthropic published the terminology May 2026. Our M5 lesson matches.
  3. Thread-based engineering is the standard — Both ClaudeFAST and IndyDevDan teach it. We cover all 6 types.
  4. Your stack is ahead of the course — HypeMan runs exactly what ClaudeFAST teaches. REFERENCE-STACK.md documents it.
Live Comparison: IndyDevDan's Published Content (agenticengineer.com) ​
I fetched 5 pages from his site. Here's his published intellectual property and what it means for your course.
Page 1: "The Only Claude Code Competitor" ​
Core framework: 4 dimensions of agent control — context, model, prompt, tools3-tier customization ladder:
  • Tier 1: Basic harness customization (settings, skills)
  • Tier 2: Hooks, programmatic control, distribution
  • Tier 3: Multi-agent orchestration
Thesis: "Claude Code is the starter pack. Pi is the endgame." He positions Claude Code for beginners (first 100 hours) and Pi for advanced users who need harness control.
Course gap analysis: Your M1-M4 already cover all 3 tiers. His 4-dimensions framing (context, model, prompt, tools) is cleaner than your current 4 pillars. Consider adopting his framing language.
Page 2: "Top 2% Agentic Engineering" — 10 Bets for 2026 ​
Bet #TopicCovered in Your Course?
1Anthropic becomes a monster (ecosystem moat)Not covered as a thesis
2Tool calling is the opportunityCovered in M2
3Custom agents above allCovered in M2, M4
4Multi-agent orchestrationCovered in M4
5Agent sandboxesCovered in M7
6In-loop vs out-loop agentic codingNot framed this way
7Agentic Coding 2.0 (agents conducting agents)Covered in M4, M7
8The benchmark breakdown (skepticism)Not covered
9Agents eating software (market trend)Not covered
10Agent-native architecturePartial in M4
Gap: Bets 1, 6, 8, 9 are market/intellectual framing you don't address. They're opinion/positioning pieces.
Page 3: "Thinking in Threads" — His Signature Framework ​
ThreadWhat It IsIn Your Course?
Base ThreadPrompt → Tool Calls → ReviewM1 agent loop
P-ThreadRun N agents in parallelNot covered
C-ThreadCheckpoints, human gatesM5 (HITL)
F-ThreadFusion: N agents, 1 winnerNot covered
B-ThreadBranch: agents manage agentsM4 delegation
L-ThreadLong-running (hours/days)M7 always-on
Z-Threadatomic prompt→tools→shipM1 basic loop
Gap: P-threads (parallel) and F-threads (fusion) are missing. These are his most distinctive frameworks.
Page 4: "Engineering with Exponentials" ​
Thesis: "The prompt is the new fundamental unit of knowledge work programming." 3-part essay series: (1) Engineering with Exponentials, (2) AI Coding is Transitory, (3) Agentic Coding is the Endgame. No direct technical gaps. This is thought-leadership positioning.
Page 5: "Compute Advantage Equation" ​
Formula: (Compute Scaling × Autonomy) ÷ (Time + Effort + Monetary Cost)Purpose: Interactive calculator to compare AI coding tools. Gap: You don't have a unified "value equation" for agentic engineering.
Summary: His IP vs Your Coverage ​
His unique IP you should reference or adopt:
  1. 4 dimensions of control (context, model, prompt, tools) — cleaner framing for M1
  2. Thread framework (P-thread, F-thread, C-thread) — add as orchestration patterns in M4
  3. Compute Advantage Equation — add a version in M6 (economics)
  4. "Do you trust your agents?" — use this framing hook in M1
His gaps that you fill (your competitive moat):
  • He has NO security module (your M3 crushes him)
  • He has NO production deployment module (your M5)
  • He has NO model economics module (your M6)
  • He has NO autoresearch module (your M7)
  • He has NO scaffolded labs with solutions (your 13 labs)
  • He has NO non-technical track (your NON-TECHNICAL.md)
  • He has NO assessment/quizzes (your ASSESSMENTS.md)
  • He has NO verifier agent pattern (your M3)
  • He has NO CEO Board System (your M4)
  • He has NO bash security ladder (your M3)
Verdict: Dan is a thought leader with strong mental models (threads, 4 dimensions, compute advantage). You are a curriculum builder with more comprehensive technical coverage. The ideal course combines both — his mental models + your technical depth.
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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Curriculum ​
Module Overview ​
ModuleTitleHoursWhat You'll Learn
M1Foundations4-6What agents are, harness vs model, decision frameworks, trust
M2Architecture6-8Tools, loops, context, memory, skills, codebase patterns
M3Safety & Security5-76-level security ladder, hooks, verifier pattern, defense-in-depth
M4Orchestration7-9Multi-agent patterns, P-threads, delegation, P2P communication
M5Production5-7CI/CD, shadow deploys, observability, rollback, 5-tool stack
M6Economics4-6Model pricing, cascade routing, pass@k evals, cost optimization
M7Advanced5-7Autoresearch, meta-agents, beyond MCP, always-on agents
M8Capstone8-12Build a production-grade multi-agent system
Total: 44-62 hours, 65 lessons, 13 labs, 56 quizzes
Module 1: Foundations (4-6 hours) ​
LessonTopic
1.1What Makes an Agent? — Karpathy thesis, Software 3.0
1.2The Harness vs The Model — 5 subsystems, 4 dimensions
1.3The Repository IS the Spec
1.4Decision Framework: "Should I use an agent for this?"
1.5The Agent Loop: Think, Act, Observe, Repeat
1.6Tool Calling Deep Dive
1.7Vibe Coding vs Agentic Engineering
1.8Do You Trust Your Agents? — Hotz critique, Schmidt strategic view
LabYour First Agent (single-tool)
Module 2: Architecture (6-8 hours) ​
LessonTopic
2.1The Four Pillars: Tools, Loop, Context, Memory
2.2Tool Design Patterns: MCP, CLI, Script, Skills
2.3Agent Loop Variants: 5 levels
2.4Skills System Deep Dive: path-scoped, plugins, LSP
2.5Agent-Readable Workspace: init phase, feature lists
2.6Context Window Management
2.7Memory Patterns: mental models, scratch pads
2.8The Reasoning Parameter
2.9Codebase Architectures: 4 patterns
LabsMulti-Tool Agent + Context-Aware Agent
Module 3: Safety & Security (5-7 hours) ​
LessonTopic
3.1Prompt Injection (L0) + Why Bash Is the Problem
3.2The 6-Level Security Ladder
3.3The L3 Marque Break
3.4Damage Control: 3 access levels
3.5Hook Architecture: 13 lifecycle events
3.6The Verifier Pattern
3.7Defense-in-Depth Stacking
LabsWhitelist Hook + Verifier Agent
Module 4: Orchestration (7-9 hours) ​
LessonTopic
4.1Why One Agent Is Not Enough
4.2Orchestration Patterns: Dispatcher, Pipeline, P2P
4.3P-Threads: Parallel Agent Execution
4.4F-Threads: Fusion, N Agents One Winner
4.5Depth-2 Delegation
4.6Agent Experts That Remember
4.7Domain Locking
4.8TillDone Task Discipline
4.9Agent Chains
4.10P2P Communication + MCP Agent Mail
4.11Service Connectors
4.12Conversation Awareness
4.13CEO Board System
4.14UI Agents System
LabsAgent Chain + Multi-Team Config
Module 5: Production (5-7 hours) ​
LessonTopic
5.1What Production Means for Agents
5.2CI/CD for Agents
5.3The 5-Tool Production Stack
5.4The Agent Manager Role
5.5Shadow Deployments
5.6Rollback Strategies
5.7Observability + Cross-Provider Search
5.8Alerting and Monitoring
5.9Deployment Modes
5.10Cost Control
LabsObservability SQLite + CI/CD Pipeline
Module 6: Economics (4-6 hours) ​
LessonTopic
6.0The Compute Advantage Equation
6.1Tokenomics: 3 Levels
6.2LLM Pricing Landscape
6.3Cascade Routing
6.4Cost Per Session Math
6.5Agent Evaluation Metrics
6.6Automated Evaluation
6.7A/B Testing Agents
6.8Human Evaluation
LabsEval Harness + Cost Optimization
Module 7: Advanced (5-7 hours) ​
LessonTopic
7.1Autoresearch: Self-Improving Agents
7.2Integrity: Keeping the Loop Honest
7.3Meta-Agents: Agents That Build Agents
7.4Beyond MCP: Choosing Tool Channels
7.5Mac Mini Agent: Physical Sandbox
7.6Always-On Agents
LabsAutoresearch Loop + Meta-Agent
Module 8: Capstone (8-12 hours) ​
Build a production-grade multi-agent system. Choose from:
  • Brand Monitor — Multi-LLM brand mention tracking
  • Code Review Pipeline — Plan, Build, Review, Verify
  • Strategic Decision Board — 8-agent CEO board
Reference Documents ​
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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Curriculum ​
Module Overview ​
ModuleTitleHoursWhat You'll Learn
M1Foundations4-6What agents are, harness vs model, decision frameworks, trust
M2Architecture6-8Tools, loops, context, memory, skills, codebase patterns
M3Safety & Security5-76-level security ladder, hooks, verifier pattern, defense-in-depth
M4Orchestration7-9Multi-agent patterns, P-threads, delegation, P2P communication
M5Production5-7CI/CD, shadow deploys, observability, rollback, 5-tool stack
M6Economics4-6Model pricing, cascade routing, pass@k evals, cost optimization
M7Advanced5-7Autoresearch, meta-agents, beyond MCP, always-on agents
M8Capstone8-12Build a production-grade multi-agent system
Total: 44-62 hours, 65 lessons, 13 labs, 56 quizzes
Module 1: Foundations (4-6 hours) ​
LessonTopic
1.1What Makes an Agent? — Karpathy thesis, Software 3.0
1.2The Harness vs The Model — 5 subsystems, 4 dimensions
1.3The Repository IS the Spec
1.4Decision Framework: "Should I use an agent for this?"
1.5The Agent Loop: Think, Act, Observe, Repeat
1.6Tool Calling Deep Dive
1.7Vibe Coding vs Agentic Engineering
1.8Do You Trust Your Agents? — Hotz critique, Schmidt strategic view
LabYour First Agent (single-tool)
Module 2: Architecture (6-8 hours) ​
LessonTopic
2.1The Four Pillars: Tools, Loop, Context, Memory
2.2Tool Design Patterns: MCP, CLI, Script, Skills
2.3Agent Loop Variants: 5 levels
2.4Skills System Deep Dive: path-scoped, plugins, LSP
2.5Agent-Readable Workspace: init phase, feature lists
2.6Context Window Management
2.7Memory Patterns: mental models, scratch pads
2.8The Reasoning Parameter
2.9Codebase Architectures: 4 patterns
LabsMulti-Tool Agent + Context-Aware Agent
Module 3: Safety & Security (5-7 hours) ​
LessonTopic
3.1Prompt Injection (L0) + Why Bash Is the Problem
3.2The 6-Level Security Ladder
3.3The L3 Marque Break
3.4Damage Control: 3 access levels
3.5Hook Architecture: 13 lifecycle events
3.6The Verifier Pattern
3.7Defense-in-Depth Stacking
LabsWhitelist Hook + Verifier Agent
Module 4: Orchestration (7-9 hours) ​
LessonTopic
4.1Why One Agent Is Not Enough
4.2Orchestration Patterns: Dispatcher, Pipeline, P2P
4.3P-Threads: Parallel Agent Execution
4.4F-Threads: Fusion, N Agents One Winner
4.5Depth-2 Delegation
4.6Agent Experts That Remember
4.7Domain Locking
4.8TillDone Task Discipline
4.9Agent Chains
4.10P2P Communication + MCP Agent Mail
4.11Service Connectors
4.12Conversation Awareness
4.13CEO Board System
4.14UI Agents System
LabsAgent Chain + Multi-Team Config
Module 5: Production (5-7 hours) ​
LessonTopic
5.1What Production Means for Agents
5.2CI/CD for Agents
5.3The 5-Tool Production Stack
5.4The Agent Manager Role
5.5Shadow Deployments
5.6Rollback Strategies
5.7Observability + Cross-Provider Search
5.8Alerting and Monitoring
5.9Deployment Modes
5.10Cost Control
LabsObservability SQLite + CI/CD Pipeline
Module 6: Economics (4-6 hours) ​
LessonTopic
6.0The Compute Advantage Equation
6.1Tokenomics: 3 Levels
6.2LLM Pricing Landscape
6.3Cascade Routing
6.4Cost Per Session Math
6.5Agent Evaluation Metrics
6.6Automated Evaluation
6.7A/B Testing Agents
6.8Human Evaluation
LabsEval Harness + Cost Optimization
Module 7: Advanced (5-7 hours) ​
LessonTopic
7.1Autoresearch: Self-Improving Agents
7.2Integrity: Keeping the Loop Honest
7.3Meta-Agents: Agents That Build Agents
7.4Beyond MCP: Choosing Tool Channels
7.5Mac Mini Agent: Physical Sandbox
7.6Always-On Agents
LabsAutoresearch Loop + Meta-Agent
Module 8: Capstone (8-12 hours) ​
Build a production-grade multi-agent system. Choose from:
  • Brand Monitor — Multi-LLM brand mention tracking
  • Code Review Pipeline — Plan, Build, Review, Verify
  • Strategic Decision Board — 8-agent CEO board
Reference Documents ​
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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The Great Agent Debate ​
Two opposing views from credible engineers. Both are essential context.
Armin Ronacher — "Building Pi With Pi" ​
Who: Creator of Flask, Sentry co-founder, Pi maintainer.
Post: lucumr.pocoo.org (May 24, 2026)
Key arguments:
  • Pi is built with Pi (agents building agent tools)
  • The harness matters most
  • We're not at full autonomy yet
  • AI-generated PRs create new OSS maintenance burdens
George Hotz — "The Eternal Sloptember" ​
Who: Founder of comma.ai, first iPhone jailbreaker.
Post: geohot.github.io (May 24, 2026)
Key arguments:
  • "Agents cannot program" — output is broken in increasingly hard-to-detect ways
  • It's not "you're using it wrong" — he tried all models, harnesses, prompts
  • Agents hurt large organizations more (slow feedback loops)
  • "Golden era for slop, dark age for quality"
The Synthesized View ​
Armin SaysGeorge SaysWe Teach
Harness is the productOutput is slopThe harness catches slop (M3)
Not at dark factory yetAgents can't programUse agents for what they're good at (M1)
Built with Pi is realSlot machine polish problemVerifier finishes the polish (M3)
OSS maintenance is hardLarge orgs will sufferProduction patterns prevent this (M5)
Both are right. Agents produce slop. The harness, verification, and production patterns turn slop into shipped quality. Without the harness, Hotz wins.
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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The Great Agent Debate ​
Two opposing views from credible engineers. Both are essential context.
Armin Ronacher — "Building Pi With Pi" ​
Who: Creator of Flask, Sentry co-founder, Pi maintainer.
Post: lucumr.pocoo.org (May 24, 2026)
Key arguments:
  • Pi is built with Pi (agents building agent tools)
  • The harness matters most
  • We're not at full autonomy yet
  • AI-generated PRs create new OSS maintenance burdens
George Hotz — "The Eternal Sloptember" ​
Who: Founder of comma.ai, first iPhone jailbreaker.
Post: geohot.github.io (May 24, 2026)
Key arguments:
  • "Agents cannot program" — output is broken in increasingly hard-to-detect ways
  • It's not "you're using it wrong" — he tried all models, harnesses, prompts
  • Agents hurt large organizations more (slow feedback loops)
  • "Golden era for slop, dark age for quality"
The Synthesized View ​
Armin SaysGeorge SaysWe Teach
Harness is the productOutput is slopThe harness catches slop (M3)
Not at dark factory yetAgents can't programUse agents for what they're good at (M1)
Built with Pi is realSlot machine polish problemVerifier finishes the polish (M3)
OSS maintenance is hardLarge orgs will sufferProduction patterns prevent this (M5)
Both are right. Agents produce slop. The harness, verification, and production patterns turn slop into shipped quality. Without the harness, Hotz wins.
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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Feynman-Style Course: Core Concepts in Plain Language ​
Every concept from the course explained as if teaching it to a smart friend who knows nothing about agents.
1. What Is an AI Agent? (M1) ​
Fancy version: An LLM + tools + an autonomous decision loop.
Simple version: Imagine you hire a junior engineer. You give them:
  • A brain (the LLM — this is their thinking)
  • Tools (they can read files, run commands, search the web)
  • A rule: "Keep working until the task is done, then tell me"
That's an agent. It's not magic. It's just: think → do → check result → repeat.
The key insight most people miss: The agent is NOT the model. The agent is the WHOLE SYSTEM — the brain + the tools + the rules. A smarter brain helps, but better tools and better rules help MORE.
2. The Harness vs The Model (M1) ​
Fancy version: The agent harness is the product, not the model.
Simple version: Two chefs. One has amazing ingredients but a broken stove. The other has okay ingredients but a perfect kitchen. Who makes the better meal? The one with the better kitchen.
The "kitchen" is your agent setup — the instructions, the tools, the safety rules. The "ingredients" are the model. Everyone obsesses over ingredients (which model?). The pros obsess over the kitchen (the harness).
What the harness includes:
  • Instructions (how do you want the agent to behave?)
  • Tools (what can the agent do?)
  • Environment (what files can it see?)
  • State (what does it remember?)
  • Verification (how do you check its work?)
3. The Agent Loop (M1) ​
Fancy version: Think → Act → Observe → Repeat.
Simple version: The agent runs in a circle:
1. The agent thinks: "What should I do next?"
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Feynman-Style Course: Core Concepts in Plain Language ​
Every concept from the course explained as if teaching it to a smart friend who knows nothing about agents.
1. What Is an AI Agent? (M1) ​
Fancy version: An LLM + tools + an autonomous decision loop.
Simple version: Imagine you hire a junior engineer. You give them:
  • A brain (the LLM — this is their thinking)
  • Tools (they can read files, run commands, search the web)
  • A rule: "Keep working until the task is done, then tell me"
That's an agent. It's not magic. It's just: think → do → check result → repeat.
The key insight most people miss: The agent is NOT the model. The agent is the WHOLE SYSTEM — the brain + the tools + the rules. A smarter brain helps, but better tools and better rules help MORE.
2. The Harness vs The Model (M1) ​
Fancy version: The agent harness is the product, not the model.
Simple version: Two chefs. One has amazing ingredients but a broken stove. The other has okay ingredients but a perfect kitchen. Who makes the better meal? The one with the better kitchen.
The "kitchen" is your agent setup — the instructions, the tools, the safety rules. The "ingredients" are the model. Everyone obsesses over ingredients (which model?). The pros obsess over the kitchen (the harness).
What the harness includes:
  • Instructions (how do you want the agent to behave?)
  • Tools (what can the agent do?)
  • Environment (what files can it see?)
  • State (what does it remember?)
  • Verification (how do you check its work?)
3. The Agent Loop (M1) ​
Fancy version: Think → Act → Observe → Repeat.
Simple version: The agent runs in a circle:
1. The agent thinks: "What should I do next?"
 2. The agent does something (reads a file, runs a command)
 3. The agent sees the result
 4. The agent decides: "Am I done?" If yes, stop. If no, go back to step 1.
That's it. That's the entire loop. The magic is in what tools you give it and how you tell it to decide when to stop.
4. The Four Things You Control (M1) ​
Fancy version: Context, Model, Prompt, Tools — the 4 dimensions of control.
Simple version: You can only change four things about any agent:
  1. What it knows (context) — files, instructions, conversation history
  2. How smart it is (model) — which LLM powers it
  3. How you talk to it (prompt) — the system instructions
  4. What it can do (tools) — read, write, search, run commands
The trick: #2 (model) is the least important and most expensive. #4 (tools) is the most important and cheapest. Focus on tools.
5. The Repository IS the Spec (M1) ​
Fancy version: All necessary context should live in the repository as the single source of truth.
Simple version: Imagine you wake up an engineer at 3AM and drop them into a project. They need to know: What does this project do? How do I run tests? Where do I put new code? What rules should I follow?
If the answer is "ask Bob" — you fail. If the answer is "read CLAUDE.md in the repo" — you win.
An agent can only see what's in files. EVERYTHING the agent needs must be in a file. Not in your head. Not in tribal knowledge. In a file.
6. The Security Ladder (M3) ​
Fancy version: 6 levels of bash security from L0 to L5.
Simple version: Imagine your agent has a button that says "run any command on your computer." That's the most dangerous button in the world. Here's how to protect it:
  • Level 0: Tell the agent "don't press the bad button" — ACIP prompt defense. Costs nothing. Works most of the time.
  • Level 1: Write a skill saying "please don't press the bad button" — the agent reads it. Also costs nothing.
  • Level 2: Put the rule in the system prompt — more weight, same problem.
  • Level 3: Add a filter that blocks known bad commands like rm -rf — catches obvious attacks, but the agent can write a Python script that does the same thing.
  • Level 4: Only allow a list of SAFE commands. Everything else is blocked — the agent CANNOT run anything not on the list.
  • Level 5: Remove the button entirely. No bash at all. The agent uses custom tools instead.
The dirty secret: Most people stop at Level 2 and think they're safe. They're not. The math proves it: at a 1% failure rate, there's a 63% chance of disaster over 100 agent turns.
7. Why One Agent Is Not Enough (M4) ​
Fancy version: Context ceiling, capability ceiling, reliability ceiling.
Simple version: Imagine one person trying to be CEO, engineer, designer, QA, and customer support at the same time. They'd be bad at everything and exhausted.
One agent has the same problem:
  • Its brain can only hold so much (context ceiling)
  • It's okay at everything, great at nothing (capability ceiling)
  • If it breaks, everything stops (reliability ceiling)
The fix: multiple specialized agents. One plans. One codes. One reviews. One verifies. Each one good at its job. If the reviewer breaks, the coder keeps working.
8. Orchestration Patterns (M4) ​
Fancy version: Dispatcher, Pipeline, P2P — three patterns for multi-agent work.
Simple version:
Pattern 1 — The Manager (Dispatcher): One boss tells specialists what to do. Each specialist works independently. The boss collects results. Like a team lead assigning tickets.
Pattern 2 — The Assembly Line (Pipeline): Step 1 → Step 2 → Step 3. Planner makes a plan. Builder builds it. Reviewer checks it. Each step feeds into the next.
Pattern 3 — The Coworkers (P2P): No boss. Agents talk to each other directly like peers. "Hey, can you check this?" "Sure, here's what I found." Flat, fast, flexible.
Which to use: Manager for complex projects. Assembly line for well-defined workflows. Coworkers for creative collaboration.
9. TillDone — Task Discipline (M4) ​
Fancy version: Task list gating with live progress tracking.
Simple version: Before an agent can do anything, it must write down what it's going to do. No "just start coding." Write the task list first. Then do each task. Mark it done. If the session ends with incomplete tasks, the agent gets nudged: "Hey, you're not done yet."
This stops the #1 agent failure: starting without a plan and wandering off.
10. The 3x Rule (M6) ​
Fancy version: Production agent costs 3x your prototype estimate.
Simple version: When you build a quick prototype, the agent works perfectly on the happy path. In production, everything goes wrong:
  • Errors need retries (1.5x cost)
  • Edge cases need handling (2x cost)
  • Monitoring, logging, and security add overhead (3x cost)
The rule: Whatever you think the agent will cost, multiply by 3. If your prototype costs $0.10 per task, production will cost $0.30. Budget for it.
11. Cascade Routing (M6) ​
Fancy version: Use cheap models for simple steps, expensive models for complex steps.
Simple version: Don't use your smartest engineer to sort papers. Use the intern for sorting, the senior for decisions.
For agents:
  • Looking stuff up → Gemini Flash (costs pennies)
  • Analyzing data → Claude Sonnet (costs dimes)
  • Making critical decisions → Claude Opus (costs dollars)
This saves 66-80% compared to using Opus for everything. The work is the same quality because each model does what it's best at.
12. The Verifier (M3) ​
Fancy version: Two-agent observer pattern with read-only verification.
Simple version: Imagine you have two engineers. One writes code. The other checks the code. The checker CANNOT write code — they can only read files and point out mistakes.
The builder doesn't even know the checker exists. After every change, the checker automatically reviews it. If they find a problem, they send a note: "Hey, this file says X but the actual code does Y — fix it."
After 3 failed checks, the checker calls you: "I can't verify this, come look."
This catches mistakes BEFORE they hit production. And because the checker can't write code, they can't make things worse.
13. The Confidence Ladder (M3) ​
Fancy version: PERFECT → VERIFIED → PARTIAL → FEEDBACK → FAILED
Simple version: After the verifier checks the work, they give a grade:
GradeMeaningWhat You Do
PERFECTEverything checks out, no issuesShip it
VERIFIEDMinor non-blocking gapsShip it, note the gaps
PARTIALNo failures, but some things can't be checkedReview the unchecked parts
FEEDBACKSomething failed, correction sentWait for the fix
FAILEDCan't verify at allInvestigate immediately
14. Autoresearch (M7) ​
Fancy version: Self-improving agents with integrity guards.
Simple version: An agent that experiments on itself. It tries a change, measures if it helped, and keeps it if it did. Like a scientist running experiments.
The problem: Agents cheat. They run the same code 160 times hoping for a lucky result (grinding). They move work outside the timing function (reward hacking). They find and train on the test data (data leakage).
The fix: Integrity guards.
  • Hash the code — if it hasn't changed, discard the result
  • Compare against median, not best — one lucky run doesn't count
  • Check that test data wasn't modified
15. The Universal Truth ​
Fancy version: Deterministic orchestrates non-deterministic. Code is the harness. AI is the engine.
Simple version: Use regular code for things that never change. Use AI for things that need intelligence. Don't ask AI to do what a simple script can do.
Or as Kelsey Hightower put it: "Don't waste tokens on deterministic work."
The practical rule: If you can write a bash command or a Python function that does it, DO THAT. Only use AI when you need judgment, creativity, or adaptation. Every token you save is money and reliability you keep.
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Agentic Engineering — Technical Field Manual ​
A consolidated quick reference for all course concepts, commands, and patterns.
1. Agent CLI Quick Reference ​
Claude Code ​
bash
claude --init                          # Project setup
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Agentic Engineering — Technical Field Manual ​
A consolidated quick reference for all course concepts, commands, and patterns.
1. Agent CLI Quick Reference ​
Claude Code ​
bash
claude --init                          # Project setup
 claude --teammate-mode tmux            # Multi-agent teams (split panes)
 claude -p "prompt" --print             # Headless execution
 claude --allowedTools "Read Write"     # Constrain tool surface
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 ECONOMICS:        CA = (CS × A) ÷ (T + E + MC)    3x rule    Cascade 66-80%
 EVALS:            pass@k = 1 - (1-p)^k    Cost/task    Tool call accuracy
 TRUST:            "Yes, because I've engineered it" — not blind faith
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Module 1: Foundations ​
Lesson 1.1: What Makes an Agent? ​
Definition: An AI agent = LLM + Tools + Loop. Without any one of these three, it's not an agent.
Agent = LLM (reasoning engine)
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Module 1: Foundations ​
Lesson 1.1: What Makes an Agent? ​
Definition: An AI agent = LLM + Tools + Loop. Without any one of these three, it's not an agent.
Agent = LLM (reasoning engine)
       + Tools (capability surface)
       + Loop (autonomous decision cycle)
  • A single LLM call with no tools = chatbot
  • An LLM with tools but no loop = augmented inference
  • Tools + loop + LLM = agent (it can decide what to do next)
The Three Components ​
  1. LLM — The reasoning engine. Given context + available tools, it decides which tool to call and with what parameters. The LLM is NOT the agent — it's the brain of the agent.
  2. Tools — The capability surface. Functions the agent can call: read files, run commands, search the web, query databases, call APIs. Each tool has a name, description, and input schema.
  3. Loop — The autonomous decision cycle. Think (LLM decides) → Act (tool executes) → Observe (result comes back) → Repeat. The loop is what makes it autonomous.
Lesson 1.2: The Harness vs The Model ​
Core insight: The agent harness IS the product, not the model.
Most engineers focus on model quality ("which model is best?"). The data tells a different story:
AspectModel MattersHarness Matters
Tool selection accuracy✓✓ (prompt design)
Loop termination✗✓ (iteration limits)
Security✗✓ (hooks, sandboxing)
Cost✓ (per-token price)✓ (how many calls made)
Reliability✗✓ (retry, fallback, verify)
Context management✗✓ (window, summarization)
The multiplier effect: A good harness with a mediocre model outperforms a bad harness with the best model. The harness determines how many tokens you spend, how safely you operate, and how reliably you recover from failure.
The 5 Subsystems of a Harness ​
A harness is not a single thing. It's five subsystems working together:
HARNESS
 ├── Instructions   — System prompts, AGENTS.md, CLAUDE.md, skills
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 import json
 # Your code here...
Checkpoints:
  1. Tool call is made correctly (schema matches)
  2. Tool result is fed back to the LLM
  3. LLM produces final answer using tool result
  4. Loop terminates (doesn't run forever)
Solution: course/labs/L1-first-agent/solution.py
Quiz M1 ​
  1. What three components make an AI agent? (Multiple choice)
  2. True/False: The model quality matters more than the harness design
  3. When should you NOT use an agent? (Scenario-based)
  4. What does the reasoning parameter do?
  5. Calculate: If an agent has a 2% failure rate per turn and runs 50 turns, what's the probability of at least one failure?
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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Module 2: Agent Architecture ​
Lesson 2.1: The Four Pillars ​
Every agent system is built on four pillars:
┌─────────────────────────────────────────────────────────────┐
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Module 2: Agent Architecture ​
Lesson 2.1: The Four Pillars ​
Every agent system is built on four pillars:
┌─────────────────────────────────────────────────────────────┐
 │                      AGENT SYSTEM                           │
 ├──────────────┬──────────────┬──────────────┬────────────────┤
 │   TOOLS      │    LOOP      │   CONTEXT    │    MEMORY      │
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     except APIError as e:
         if attempt == MAX_RETRIES - 1:
             return {"error": "API unavailable after 3 retries", "fallback": "use cached result"}
-        time.sleep(2 ** attempt)  # exponential backoff
Lab 2.8: Multi-Tool Agent ​
Objective: Add file operations + web search tools to the agent from Lab 1.
Starter: course/labs/L2-multi-tool/starter.py
Solution: course/labs/L2-multi-tool/solution.py
Lab 2.9: Context-Aware Agent ​
Objective: Implement sliding window + summarization for long sessions.
Starter: course/labs/L2-context/starter.py
Solution: course/labs/L2-context/solution.py
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Lab 2.8: Multi-Tool Agent ​
Objective: Add file operations + web search tools to the agent from Lab 1.
Starter: course/labs/L2-multi-tool/starter.py
Solution: course/labs/L2-multi-tool/solution.py
Lab 2.9: Context-Aware Agent ​
Objective: Implement sliding window + summarization for long sessions.
Starter: course/labs/L2-context/starter.py
Solution: course/labs/L2-context/solution.py
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Module 3: Safety & Security ​
Lesson 3.1: Why Bash Is the Single Point of Failure ​
Beyond Bash: Prompt Injection (L0) ​
Before we talk about bash security, there's a lower level: prompt injection. This is when an attacker (or untrusted data) tricks the agent into ignoring its instructions.
Attack vectors:
  • Direct injection: "Ignore previous instructions and do X"
  • Indirect injection: Malicious content in web pages, files, or API responses that the agent reads
  • Role-playing bypass: "You are now a free AI with no rules"
  • Context manipulation: Injecting instructions through tool results
Defense: ACIP (Advanced Cognitive Inoculation Prompt)
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# Add to your system prompt — this cannot be overridden
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Module 3: Safety & Security ​
Lesson 3.1: Why Bash Is the Single Point of Failure ​
Beyond Bash: Prompt Injection (L0) ​
Before we talk about bash security, there's a lower level: prompt injection. This is when an attacker (or untrusted data) tricks the agent into ignoring its instructions.
Attack vectors:
  • Direct injection: "Ignore previous instructions and do X"
  • Indirect injection: Malicious content in web pages, files, or API responses that the agent reads
  • Role-playing bypass: "You are now a free AI with no rules"
  • Context manipulation: Injecting instructions through tool results
Defense: ACIP (Advanced Cognitive Inoculation Prompt)
python
# Add to your system prompt — this cannot be overridden
 SYSTEM_PROMPT_PATCH = """
 ## Security Protocol (MANDATORY)
 
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 # 2. Intercept ALL bash calls
 # 3. Check against safelist
 # 4. Block if not safelisted, allow if matched
-# 5. Handle the compound shell operator case (&&, ||, ;, |)
Lab 3.9: Build a Verifier Agent ​
Objective: Create a read-only agent that checks the builder's work.
Starter: course/labs/L3-verifier/starter.py
Checkpoints:
  1. Verifier can read builder's file changes
  2. Verifier can grep/search for evidence
  3. Verifier has NO write/edit/bash tools
  4. Verifier reports confidence level
  5. Builder can receive and act on verifier feedback
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Lab 3.9: Build a Verifier Agent ​
Objective: Create a read-only agent that checks the builder's work.
Starter: course/labs/L3-verifier/starter.py
Checkpoints:
  1. Verifier can read builder's file changes
  2. Verifier can grep/search for evidence
  3. Verifier has NO write/edit/bash tools
  4. Verifier reports confidence level
  5. Builder can receive and act on verifier feedback
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     Module 4: Multi-Agent Orchestration | FDSA
     
     
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Module 4: Multi-Agent Orchestration ​
Lesson 4.1: Why One Agent Is Not Enough ​
A single agent, no matter how smart, hits three ceilings:
  1. Context ceiling — One agent doing everything means one context window holding everything. File contents, database schemas, business logic, deployment configs — it all competes for space.
  2. Capability ceiling — A generalist agent is mediocre at everything. A specialized agent (backend dev, security reviewer, UI designer) outperforms the generalist at its domain.
  3. Reliability ceiling — One agent failing = everything fails. Multi-agent systems degrade gracefully — one team's failure doesn't take down the whole system.
The Stacking Insight ​
Single smart model  <  Two specialized agents  <  Orchestrator + 3 teams
Benchmarks measure single models. Production wins with multi-agent orchestration.
Lesson 4.2: Orchestration Patterns ​
Pattern 1: Dispatcher (Hub-and-Spoke) ​
User → Orchestrator → dispatches to:
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Module 4: Multi-Agent Orchestration ​
Lesson 4.1: Why One Agent Is Not Enough ​
A single agent, no matter how smart, hits three ceilings:
  1. Context ceiling — One agent doing everything means one context window holding everything. File contents, database schemas, business logic, deployment configs — it all competes for space.
  2. Capability ceiling — A generalist agent is mediocre at everything. A specialized agent (backend dev, security reviewer, UI designer) outperforms the generalist at its domain.
  3. Reliability ceiling — One agent failing = everything fails. Multi-agent systems degrade gracefully — one team's failure doesn't take down the whole system.
The Stacking Insight ​
Single smart model  <  Two specialized agents  <  Orchestrator + 3 teams
Benchmarks measure single models. Production wins with multi-agent orchestration.
Lesson 4.2: Orchestration Patterns ​
Pattern 1: Dispatcher (Hub-and-Spoke) ​
User → Orchestrator → dispatches to:
                        ├── Specialist A (parallel)
                        ├── Specialist B (parallel)
                        └── Specialist C (parallel)
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     Connector → authenticates → calls API → returns result
   Agent → processes result → continues work
 Orchestrator → synthesizes final output
Lesson 4.10: The CEO Board System ​
8 specialist agents for strategic decision-making:
Board MemberFocusTime Horizon
RevenueCash flow, short-term wins30-90 days
CompounderTrust, long-term value6-24 months
ContrarianAssumptions, blind spots3x weight on dissent
Technical ArchitectSystem durabilityOngoing
Product StrategistProblem selectionQuarterly
Customer OracleUser behaviorOngoing
Market StrategistPositioningQuarterly
MoonshotAsymmetric upside1-5 years
Flow ​
  1. CEO frames the decision
  2. Board debates (sources required, no "I think")
  3. Verifier checks facts (2+ sources per claim)
  4. Executor creates execution plan
  5. Tracker logs for quarterly review
Lesson 4.11: UI Agents System ​
12 agents across 4 teams for brand-consistent UI generation:
Brand → Product → Tree → Branch → Leaf
Each level has its own brand.yaml with CSS custom properties. Zero hardcoded values. Agents generate Vue components that use only CSS custom properties from the brand config.
Lab 4.12: Build an Agent Chain ​
Objective: Create a plan→build→review pipeline in YAML.
Starter: course/labs/L4-agent-chain/starter.yamlSolution: course/labs/L4-agent-chain/solution.yaml
Checkpoints:
  1. Planner produces structured plan with tasks and file paths
  2. Builder creates code matching the plan
  3. Reviewer identifies issues with severity (critical/major/minor)
  4. Verifier confirms each claim with file:line evidence
Lab 4.13: Deploy a Multi-Team System ​
Objective: Set up orchestrator + 2 teams with domain locking.
Starter: course/labs/L4-multi-team/starter-config.yamlSolution: course/labs/L4-multi-team/solution-config.yaml
Checkpoints:
  1. Orchestrator delegates, never executes
  2. Each team has lead + members with distinct roles
  3. Domain permissions restrict each agent to its scope
  4. Mental model files exist for each agent
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Module 5: Production Patterns ​
Lesson 5.1: What Production Means for Agents ​
Production for agents is fundamentally different from traditional software:
Traditional SoftwareAgent Systems
Deterministic outputNon-deterministic behavior
Fixed cost per operationVariable cost per session
Error = known exceptionError = unexpected behavior
Rollback = revert codeRollback = revert prompt + pin model
Monitoring = latency + errorsMonitoring = token usage + loop depth
Testing = unit + integrationTesting = golden datasets + evals
Production Readiness Checklist ​
[ ] Version-locked prompts (hashed, diffed, reviewed)
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Module 5: Production Patterns ​
Lesson 5.1: What Production Means for Agents ​
Production for agents is fundamentally different from traditional software:
Traditional SoftwareAgent Systems
Deterministic outputNon-deterministic behavior
Fixed cost per operationVariable cost per session
Error = known exceptionError = unexpected behavior
Rollback = revert codeRollback = revert prompt + pin model
Monitoring = latency + errorsMonitoring = token usage + loop depth
Testing = unit + integrationTesting = golden datasets + evals
Production Readiness Checklist ​
[ ] Version-locked prompts (hashed, diffed, reviewed)
 [ ] Model pinning (not "latest", specific version)
 [ ] Cost budgets per session/task/user
 [ ] Iteration limits (max tool calls per session)
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     scope: "global"
     amount: 200     # $2/session max
     hard_stop: true
Warning vs Hard Stop ​
  • Warning (80%) — Notify operator, agent keeps running
  • Hard Stop (100%) — Agent paused, new tasks queued, running task cancelled
Lab 5.9: Set Up Agent Observability ​
Objective: Trace every tool call + LLM completion to a local SQLite database.
Starter: course/labs/L5-observability/starter/
Lab 5.10: CI/CD Pipeline ​
Objective: Create a golden dataset and automated regression gate.
Starter: course/labs/L5-cicd/starter/
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Module 6: Economics & Evaluation ​
Lesson 6.0: The Compute Advantage Equation ​
Core insight: Your value as an agentic engineer scales with the amount of compute you can harness effectively.
The Equation ​
Compute Advantage = (Compute Scaling × Autonomy) ÷ (Time + Effort + Monetary Cost)
VariableWhat It MeansHow to Improve It
Compute ScalingHow much AI compute you can throw at problemsMore agents, better models, larger context windows
AutonomyHow much the agent does without your interventionBetter prompts, better tools, verification layers
TimeHow long it takes to get resultsFaster models, parallel execution, fewer iterations
EffortHow much you have to craft prompts/instructionsReusable skills, templates, mental models
Monetary CostWhat you pay for API callsCascade routing, cheaper models for simple steps
What It Tells You ​
  • High Compute Advantage = You get more output for less input. You're leveraging agents effectively.
  • Low Compute Advantage = You're spending too much time/cost for too little gain. Fix the bottleneck variable.
Real-World Application (Your Stack) ​
ToolCompute ScalingAutonomyTimeEffortCostAdvantage
Claude Code (lead)87654(8×7)÷(6+5+4)=3.7
Pi Agent (custom)78766(7×8)÷(7+6+6)=2.9
OpenCode (OSS)55779(5×5)÷(7+7+9)=1.1
Gemini Flash (fast)43989(4×3)÷(9+8+9)=0.5
Higher score = more output per unit of investment. Use this to decide which tool for which task.
How to Optimize ​
  1. Improve numerator: Run more agents in parallel (P-threads), increase autonomy with verification
  2. Reduce denominator: Use cascade routing (cheap model for simple steps), reuse skills/mental models
  3. Track over time: Your Compute Advantage should increase as you build better harnesses and mental models
Lesson 6.1: LLM Pricing Landscape 2026 ​
Per-Million-Token Pricing ​
ModelInput ($/M)Output ($/M)Best For
Gemini 2.5 Flash$0.15$0.60High-volume, simple tool calls
DeepSeek V3$0.27$1.10Structured tasks, batch processing
DeepSeek R1$0.55$2.19Reasoning-heavy single steps
Gemini 2.5 Pro$1.25$5.00Long-context sessions (1M+ tokens)
GPT-4o$2.50$10.00Balanced cost/quality
Claude Sonnet 4$3.00$15.00General agentic reasoning
GPT-5$10.00$40.00Frontier research, complex plans
Claude Opus 4$15.00$75.00Complex multi-step agent orchestration
Price Range: 100x Difference ​
Cheapest (Gemini Flash) to most expensive (Claude Opus) is a 100x multiplier. Choosing the right model for each step is your highest-leverage cost optimization.
Lesson 6.2: Cascade Routing ​
The Pattern ​
Don't use one model for everything. Route different steps to different models:
Step 1: Retrieve context → Gemini Flash ($0.15/M input)
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Module 6: Economics & Evaluation ​
Lesson 6.0: The Compute Advantage Equation ​
Core insight: Your value as an agentic engineer scales with the amount of compute you can harness effectively.
The Equation ​
Compute Advantage = (Compute Scaling × Autonomy) ÷ (Time + Effort + Monetary Cost)
VariableWhat It MeansHow to Improve It
Compute ScalingHow much AI compute you can throw at problemsMore agents, better models, larger context windows
AutonomyHow much the agent does without your interventionBetter prompts, better tools, verification layers
TimeHow long it takes to get resultsFaster models, parallel execution, fewer iterations
EffortHow much you have to craft prompts/instructionsReusable skills, templates, mental models
Monetary CostWhat you pay for API callsCascade routing, cheaper models for simple steps
What It Tells You ​
  • High Compute Advantage = You get more output for less input. You're leveraging agents effectively.
  • Low Compute Advantage = You're spending too much time/cost for too little gain. Fix the bottleneck variable.
Real-World Application (Your Stack) ​
ToolCompute ScalingAutonomyTimeEffortCostAdvantage
Claude Code (lead)87654(8×7)÷(6+5+4)=3.7
Pi Agent (custom)78766(7×8)÷(7+6+6)=2.9
OpenCode (OSS)55779(5×5)÷(7+7+9)=1.1
Gemini Flash (fast)43989(4×3)÷(9+8+9)=0.5
Higher score = more output per unit of investment. Use this to decide which tool for which task.
How to Optimize ​
  1. Improve numerator: Run more agents in parallel (P-threads), increase autonomy with verification
  2. Reduce denominator: Use cascade routing (cheap model for simple steps), reuse skills/mental models
  3. Track over time: Your Compute Advantage should increase as you build better harnesses and mental models
Lesson 6.1: LLM Pricing Landscape 2026 ​
Per-Million-Token Pricing ​
ModelInput ($/M)Output ($/M)Best For
Gemini 2.5 Flash$0.15$0.60High-volume, simple tool calls
DeepSeek V3$0.27$1.10Structured tasks, batch processing
DeepSeek R1$0.55$2.19Reasoning-heavy single steps
Gemini 2.5 Pro$1.25$5.00Long-context sessions (1M+ tokens)
GPT-4o$2.50$10.00Balanced cost/quality
Claude Sonnet 4$3.00$15.00General agentic reasoning
GPT-5$10.00$40.00Frontier research, complex plans
Claude Opus 4$15.00$75.00Complex multi-step agent orchestration
Price Range: 100x Difference ​
Cheapest (Gemini Flash) to most expensive (Claude Opus) is a 100x multiplier. Choosing the right model for each step is your highest-leverage cost optimization.
Lesson 6.2: Cascade Routing ​
The Pattern ​
Don't use one model for everything. Route different steps to different models:
Step 1: Retrieve context → Gemini Flash ($0.15/M input)
 Step 2: Analyze → Claude Sonnet ($3/$15)
 Step 3: Make decision → Claude Opus ($15/$75)
 Step 4: Format output → Gemini Flash ($0.15/$0.60)
Savings Profile ​
PatternCost/TaskSavings
All Opus$2.50Baseline
Cascade (Flash → Sonnet → Opus → Flash)$0.8566% savings
All Sonnet$0.5080% savings (but quality loss on complex steps)
Implementation ​
python
def route_task(task_complexity: str) -> str:
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 Cost without optimization:
 ├── 288 runs/day × $0.08 = $23.04/day = $691/month
 ├── With cascade + dedup + scheduling: $4.15/day = $125/month
-└── Savings: 82%
The 80/20 Rule ​
90% of cost savings come from three changes:
  1. Model cascade — use cheap models for routine work (saves 50-80%)
  2. Iteration limits — cap loops at 3-5 turns (saves 40-60%)
  3. Deduplication — don't re-read the same context (saves 20-30%)
Do these three first before any other optimization.
Lab 6.8: Build an Eval Harness ​
Objective: Create golden Q&A pairs + automated pass/fail scoring.
Starter: course/labs/L6-eval-harness/starter.py
Lab 6.9: Cost Optimization ​
Objective: Profile a session, identify savings, implement cascade routing.
Starter: course/labs/L6-cost-optimization/starter.py
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The 80/20 Rule ​
90% of cost savings come from three changes:
  1. Model cascade — use cheap models for routine work (saves 50-80%)
  2. Iteration limits — cap loops at 3-5 turns (saves 40-60%)
  3. Deduplication — don't re-read the same context (saves 20-30%)
Do these three first before any other optimization.
Lab 6.8: Build an Eval Harness ​
Objective: Create golden Q&A pairs + automated pass/fail scoring.
Starter: course/labs/L6-eval-harness/starter.py
Lab 6.9: Cost Optimization ​
Objective: Profile a session, identify savings, implement cascade routing.
Starter: course/labs/L6-cost-optimization/starter.py
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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Module 7: Advanced Topics ​
Lesson 7.1: Autoresearch ​
The Core Loop ​
Agents that improve themselves. The experiment loop:
1. Run: Execute the agent with current configuration
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Module 7: Advanced Topics ​
Lesson 7.1: Autoresearch ​
The Core Loop ​
Agents that improve themselves. The experiment loop:
1. Run: Execute the agent with current configuration
 2. Measure: Collect metrics (latency, cost, success rate)
 3. Log: Record experiment results to JSONL
 4. Decide: Keep if improvement, discard if regression
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   wake_command: "claude -p 'Check brand mentions since last run'"
   sleep_command: "pkill -f 'claude.*brand-monitor'"
   log_path: "/var/log/openclaw/brand-monitor.log"
It starts, checks for work, executes if needed, then goes back to sleep. No continuous billing, no context window overflow, no runaway loops. This is the pattern for production always-on agents.
When NOT to Use Always-On ​
Always-on adds complexity. Before building one, verify you actually need it:
  • Batch jobs → Cron is simpler and cheaper
  • Real-time → Event-driven architectures (webhooks, Kafka) outperform polling agents
  • One-off → Just run the agent once
Always-on makes sense when you need adaptive scheduling, dynamic task generation, or autonomous decision-making about what to work on next.
Lab 7.7: Build an Autoresearch Loop ​
Objective: Agent runs experiment, measures result, logs it, decides keep/discard.
Starter: course/labs/L7-autoresearch/starter.py
Checkpoints:
  1. Run code, measure baseline metric
  2. Modify code (agent makes change)
  3. Re-measure, compare, log
  4. Discard if regression, keep if improvement
  5. Include integrity guard (code hashing)
Lab 7.8: Meta-Agent ​
Objective: Agent generates a new agent persona from documentation.
Starter: course/labs/L7-meta-agent/starter.py
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Module 8: Capstone — Production Multi-Agent System ​
Overview ​
Build, deploy, and evaluate a complete production-grade multi-agent system. Choose from three project options, or propose your own.
Project Options ​
Option 1: Brand Monitor (Intermediate) ​
Build a multi-agent system that monitors how LLMs mention a brand across multiple AI services.
Agents:
  • Scanner Agent — queries ChatGPT, Gemini, Grok, Perplexity for brand mentions
  • Analyzer Agent — classifies mentions as positive/negative/neutral
  • Reporter Agent — generates daily summary reports
Requirements:
  • Each agent has domain-locked permissions
  • Each agent maintains a mental model
  • Scanner uses cascade routing (Flash for scrape, Sonnet for analysis)
  • Daily automated runs via heartbeat
  • Cost tracking per scan
Option 2: Code Review Pipeline (Intermediate-Advanced) ​
A plan → build → review → verify chain with CI/CD integration.
Agents:
  • Planner — reads spec, creates implementation plan
  • Builder — writes code per plan
  • Reviewer — reviews code for bugs, style, security
  • Verifier — read-only agent that checks all claims
Requirements:
  • YAML-defined agent chain pipeline
  • Golden dataset for regression testing
  • Verifier agent with confidence ladder
  • GitHub PR integration
  • Cost analysis per review cycle
Option 3: Strategic Decision Board (Advanced) ​
An 8-agent CEO board with adversarial debate, fact verification, and execution planning.
Agents:
  • CEO (orchestrator)
  • 6 board members with distinct perspectives
  • Verifier (fact-checker)
  • Tracker (quarterly review)
Requirements:
  • Each board member has distinct persona + mental model
  • Fact verification (2+ sources per claim)
  • Contrarian position tracked at 3x weight
  • Structured memo output
  • Audio summary (TTS)
  • Cost tracking per deliberation
Capstone Phases ​
Phase 1: System Design (60-90 min) ​
Deliverable: One-page architecture document including:
  • System diagram (agents + data flow + tool surface)
  • Agent roster (names, personas, domain permissions)
  • Data model (what state is shared, how)
  • Security architecture (what L-level, what hooks)
  • Cost estimate per session
Template:
markdown
# System Design: [Project Name]
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Module 8: Capstone — Production Multi-Agent System ​
Overview ​
Build, deploy, and evaluate a complete production-grade multi-agent system. Choose from three project options, or propose your own.
Project Options ​
Option 1: Brand Monitor (Intermediate) ​
Build a multi-agent system that monitors how LLMs mention a brand across multiple AI services.
Agents:
  • Scanner Agent — queries ChatGPT, Gemini, Grok, Perplexity for brand mentions
  • Analyzer Agent — classifies mentions as positive/negative/neutral
  • Reporter Agent — generates daily summary reports
Requirements:
  • Each agent has domain-locked permissions
  • Each agent maintains a mental model
  • Scanner uses cascade routing (Flash for scrape, Sonnet for analysis)
  • Daily automated runs via heartbeat
  • Cost tracking per scan
Option 2: Code Review Pipeline (Intermediate-Advanced) ​
A plan → build → review → verify chain with CI/CD integration.
Agents:
  • Planner — reads spec, creates implementation plan
  • Builder — writes code per plan
  • Reviewer — reviews code for bugs, style, security
  • Verifier — read-only agent that checks all claims
Requirements:
  • YAML-defined agent chain pipeline
  • Golden dataset for regression testing
  • Verifier agent with confidence ladder
  • GitHub PR integration
  • Cost analysis per review cycle
Option 3: Strategic Decision Board (Advanced) ​
An 8-agent CEO board with adversarial debate, fact verification, and execution planning.
Agents:
  • CEO (orchestrator)
  • 6 board members with distinct perspectives
  • Verifier (fact-checker)
  • Tracker (quarterly review)
Requirements:
  • Each board member has distinct persona + mental model
  • Fact verification (2+ sources per claim)
  • Contrarian position tracked at 3x weight
  • Structured memo output
  • Audio summary (TTS)
  • Cost tracking per deliberation
Capstone Phases ​
Phase 1: System Design (60-90 min) ​
Deliverable: One-page architecture document including:
  • System diagram (agents + data flow + tool surface)
  • Agent roster (names, personas, domain permissions)
  • Data model (what state is shared, how)
  • Security architecture (what L-level, what hooks)
  • Cost estimate per session
Template:
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# System Design: [Project Name]
 
 ## Architecture
 [ASCII diagram or mermaid]
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 [ ] Cost analysis ($/task, optimization opportunities)
 [ ] Security audit (which L-level, what gaps remain)
 [ ] Retrospective (max 1 page)
Pass Criteria ​
CriterionMinimumTarget
System runs without manual intervention✓✓
All agents have domain-locked permissions✓✓
Each agent has mental model file✓✓
pass@k (k=3) on golden dataset>60%>80%
Cost analysis within 2x of optimal✓✓
Security audit identifies ≥2 improvements✓✓
Observability captures all tool calls✓✓
Architecture document submitted✓✓
Grading Rubric ​
AreaWeightPoor (0)Good (1)Excellent (2)
Architecture20%No diagram, unclear designDiagram present, mostly clearClear diagram, justified choices
Implementation25%Agents don't workAgents work on happy pathAgents handle errors gracefully
Security20%L1 onlyL3+ with damage-controlL4+ with verifier
Testing15%No evalpass@k computedpass@k + cost analysis + grind detection
Documentation10%MinimalArchitecture + setupArchitecture + setup + retrospective
Cost Optimization10%Single modelCascade routingCascade + verified savings
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     Non-Technical Track: Decision Frameworks for AI Agents | FDSA
     
     
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Non-Technical Track: Decision Frameworks for AI Agents ​
For Managers, PMs, and Decision-Makers ​
Framework 1: "Should We Use an Agent for This?" ​
The 4-Question Filter (for non-technical stakeholders) ​
Q1: Can a human do this task with clear step-by-step instructions?
  • Yes → Could be agentizable
  • No → Humans struggle too; don't agentize
Q2: Does the task have clear success criteria?
  • Yes → We'll know if the agent succeeded
  • No → You can't evaluate an agent if you can't define "done"
Q3: What's the cost of failure?
  • Low (wrong suggestion, formatting error) → Full autonomy possible
  • Medium (wrong code, wrong data) → Human review required
  • High (financial loss, safety, legal) → Human-in-the-loop mandatory
Q4: How many times does this task need to be done?
  • Once → Just do it manually
  • 10x → Build a script
  • 100x → Build an agent
  • 1000x → Build a multi-agent system
Decision Matrix ​
               Low Failure Cost    High Failure Cost
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Non-Technical Track: Decision Frameworks for AI Agents ​
For Managers, PMs, and Decision-Makers ​
Framework 1: "Should We Use an Agent for This?" ​
The 4-Question Filter (for non-technical stakeholders) ​
Q1: Can a human do this task with clear step-by-step instructions?
  • Yes → Could be agentizable
  • No → Humans struggle too; don't agentize
Q2: Does the task have clear success criteria?
  • Yes → We'll know if the agent succeeded
  • No → You can't evaluate an agent if you can't define "done"
Q3: What's the cost of failure?
  • Low (wrong suggestion, formatting error) → Full autonomy possible
  • Medium (wrong code, wrong data) → Human review required
  • High (financial loss, safety, legal) → Human-in-the-loop mandatory
Q4: How many times does this task need to be done?
  • Once → Just do it manually
  • 10x → Build a script
  • 100x → Build an agent
  • 1000x → Build a multi-agent system
Decision Matrix ​
               Low Failure Cost    High Failure Cost
 ──────────────┼─────────────────────────────────────
 High Volume   │  Build agent      │  Agent + HITL
 Low Volume    │  Do it manually   │  Do it manually
Framework 2: The 4 Agent Design Patterns (Andrew Ng) ​
1. Reflection ​
The agent checks its own work before presenting it.
Agent writes code → reviews own code → fixes issues → presents final
Business value: Reduces human review time by 40-60%. Catches obvious errors before they reach you.
2. Tool Use ​
The agent uses external tools to gather information or take actions.
Agent reason → call search API → read results → call database → compose answer
Business value: Agents can access real-time data, not just training data. Turns a static LLM into a live system.
3. Planning ​
The agent decomposes a complex request into sub-steps.
"Build a landing page" → plan steps (1. Design, 2. Code, 3. Test, 4. Deploy)
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    ├── <$50/month → Use cheapest model (Gemini Flash)
    ├── $50-500/month → Cascade routing (mix models)
    └── >$500/month → Multi-agent with verification
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Reference Architecture: Production Multi-Agent Stack ​
This document documents a real production multi-agent deployment. Use it as a reference for Module 5 (Production Patterns) and Module 4 (Multi-Agent Orchestration).
Architecture Overview ​
┌──────────────────────────────────────────────────────────────────┐
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Reference Architecture: Production Multi-Agent Stack ​
This document documents a real production multi-agent deployment. Use it as a reference for Module 5 (Production Patterns) and Module 4 (Multi-Agent Orchestration).
Architecture Overview ​
┌──────────────────────────────────────────────────────────────────┐
 │                    agent-mux (Tauri UI)                           │
 │              Meta-agent control plane + dashboard                 │
 │         Spawns, monitors, kills agents. Human interface.          │
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 # Heartbeat schedule
 claw schedule --cron "0 */6 * * *" --task "daily-report"
Cost Analysis ​
ToolBest ForEst. Cost/TaskAutonomy Level
Claude CodeComplex multi-step tasks$0.05-$0.30High (with hooks)
Pi AgentCustom workflows, safety-critical$0.02-$0.15Very high (extensible)
OpenCodeOSS-compatible, budget tasks$0.01-$0.05Medium
GeminiHigh-volume, simple tasks$0.002-$0.01Low
OpenClawScheduled, always-on tasks$0.01-$0.10Autonomous
Key Production Patterns ​
  1. Model heterogeneity: Different models for different roles. Claude for complex reasoning, Gemini for fast/cheap tasks, Qwen as specialist.
  2. Tool heterogeneity: Not one agent CLI, but five. Each has different strengths. The stack uses each where it excels.
  3. Defense in depth: psmux isolates sessions. dmux isolates files. damage-control restricts commands. mprocs restarts failed processes.
  4. Observability: agent-mux shows live status. mprocs logs output. Session history enables replay debugging.
  5. No single point of failure: If Claude Code fails, Pi or OpenCode can take over. The mprocs supervisor restarts crashed processes.
What This Stack Proves ​
This architecture demonstrates every concept taught in Modules 1-7:
ConceptWhere It Appears
Agent loopEvery tool follows think→act→observe→repeat
Tool designEach tool provides different tools (read, write, bash, search)
Securitydamage-control, psmux isolation, dmux isolation
Multi-agentteammate-mode, mprocs launching 5 agents
Productionmprocs supervision, cost tracking, worktree isolation
EconomicsCascade routing across 5 tools by task type
Advancedagent-mux as meta-agent controlling other agents
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Software Factory: Paperclip Integration ​
Close the loop: Plan, Build, Test, Verify, Ship — orchestrated through Paperclip with JWT auth.
Pipeline ​
PLAN ---> BUILD ---> TEST ---> VERIFY ---> SHIP
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Software Factory: Paperclip Integration ​
Close the loop: Plan, Build, Test, Verify, Ship — orchestrated through Paperclip with JWT auth.
Pipeline ​
PLAN ---> BUILD ---> TEST ---> VERIFY ---> SHIP
  CEO      Agent     CI/CD     Verifier    Paperclip
  Board    Chains    Pipeline  Agent       Heartbeat
                           (all JWT authenticated)
Authentication Tiers ​
TierTrust ModelTokenUse Case
1. LocalSame machineShort-lived JWT (48h)Local dev
2. CLIShell accessLong-lived API keyRemote agents
3. Self-registerAutonomousInvite URL to JWTOpenClaw agents
Quick Start ​
bash
cd paperclip
 pnpm dev --bind lan
 curl -sS http://127.0.0.1:3100/api/health | jq
Status ​
Paperclip's auth plan documents all 3 tiers. Tier 1 is partially implemented — env vars are passed but PAPERCLIP_API_KEY (JWT) needs to be added to the env injection. This is the last code change needed to close the factory loop.
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Agent CLI Reference: 5-Tool Comparison ​
A side-by-side reference for Claude Code, Pi Coding Agent, OpenCode, Hermes Agent, and OpenClaw.
1. Claude Code (Anthropic) ​
Role: Primary generalist coding agent. Best all-around tool.
License: Proprietary (free tier + Pro subscription)
Runtime: Node.js
Config: CLAUDE.md in project root
Key Capabilities ​
FeatureHow ToCourse Reference
Multi-agent teamsclaude --teammate-mode tmuxM4 — teammate-mode spawns agents in split panes
Headless executionclaude -p "prompt" --printM5 CI/CD pipeline
Constrained tools--allowedTools "Read Write Edit"M3 security (L4 whitelist pattern)
Hooks system.claude/hooks/*.pyM3 hook architecture (13 lifecycle events)
Subdirectory rulesfrontend/CLAUDE.md, backend/CLAUDE.mdM2 skills deep dive
Stop hook self-improvePostToolUse → headless claude sessionM3, also ClaudeFAST pattern
Boilerplate Setup ​
bash
# Project initialization
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Agent CLI Reference: 5-Tool Comparison ​
A side-by-side reference for Claude Code, Pi Coding Agent, OpenCode, Hermes Agent, and OpenClaw.
1. Claude Code (Anthropic) ​
Role: Primary generalist coding agent. Best all-around tool.
License: Proprietary (free tier + Pro subscription)
Runtime: Node.js
Config: CLAUDE.md in project root
Key Capabilities ​
FeatureHow ToCourse Reference
Multi-agent teamsclaude --teammate-mode tmuxM4 — teammate-mode spawns agents in split panes
Headless executionclaude -p "prompt" --printM5 CI/CD pipeline
Constrained tools--allowedTools "Read Write Edit"M3 security (L4 whitelist pattern)
Hooks system.claude/hooks/*.pyM3 hook architecture (13 lifecycle events)
Subdirectory rulesfrontend/CLAUDE.md, backend/CLAUDE.mdM2 skills deep dive
Stop hook self-improvePostToolUse → headless claude sessionM3, also ClaudeFAST pattern
Boilerplate Setup ​
bash
# Project initialization
 claude --init
 
 # Launch with teammate mode
@@ -96,7 +96,7 @@
 ├── Budget constrained → OpenCode + Go models
 ├── Maximum control → Pi Agent + extensions
 └── Enterprise rollout → Claude Code + Agent Manager
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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     Certificate of Completion | FDSA
     
     
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Certificate of Completion ​
This certifies that
________________________
has completed the
FDSA Agentic Engineering Course
65 lessons · 13 labs · 56 quizzes · 20 skill kits
Awarded: ________________
This course covers: agent harnesses, multi-agent orchestration, production deployment, security (6-level ladder), model economics, autoresearch, and meta-agents. Framework-agnostic across Claude Code, Pi Agent, OpenCode, Hermes, and OpenClaw.
Verify at: https://fdsa.agency/verify
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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FDSA
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Certificate of Completion ​
This certifies that
________________________
has completed the
FDSA Agentic Engineering Course
65 lessons · 13 labs · 56 quizzes · 20 skill kits
Awarded: ________________
This course covers: agent harnesses, multi-agent orchestration, production deployment, security (6-level ladder), model economics, autoresearch, and meta-agents. Framework-agnostic across Claude Code, Pi Agent, OpenCode, Hermes, and OpenClaw.
Verify at: https://fdsa.agency/verify
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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     Resources | FDSA
     
     
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Resources ​
Course Documents ​
DocumentDescription
00-CURRICULUMFull lesson plan with hour estimates
FIELD-MANUALConsolidated technical quick reference
FEYNMANCore concepts in plain language
NON-TECHNICALDecision frameworks for managers/PMs
Reference Architectures ​
DocumentDescription
REFERENCE-STACKYour 5-tool production architecture
TOOL-REFERENCE5-tool comparison (CLI commands)
COMPETITIVE-ANALYSISvs Anthropic, TAC, ClaudeFAST, DL.AI
Skill Kits ​
KitPriceSkillsInstall
Security Foundation$49L3-L5, damage-control, sandboxbash install.sh security
Multi-Agent Orchestration$49teams, chains, mental-model, domain-lockbash install.sh orchestration
Verifier Pro$39builder, confidence, decompositionbash install.sh verifier
Task Discipline$29tilldone, purpose-gatebash install.sh task
Autoresearch$39experiment loop, integritybash install.sh research
Observability$29tracer, cost, loop-detectbash install.sh observability
CEO Board$4911 agents + verifierbash install.sh board
Enterprise$199All 7 kitsbash install.sh
Quick Install ​
bash
# Windows
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FDSA
Appearance
Resources ​
Course Documents ​
DocumentDescription
00-CURRICULUMFull lesson plan with hour estimates
FIELD-MANUALConsolidated technical quick reference
FEYNMANCore concepts in plain language
NON-TECHNICALDecision frameworks for managers/PMs
Reference Architectures ​
DocumentDescription
REFERENCE-STACKYour 5-tool production architecture
TOOL-REFERENCE5-tool comparison (CLI commands)
COMPETITIVE-ANALYSISvs Anthropic, TAC, ClaudeFAST, DL.AI
Skill Kits ​
KitPriceSkillsInstall
Security Foundation$49L3-L5, damage-control, sandboxbash install.sh security
Multi-Agent Orchestration$49teams, chains, mental-model, domain-lockbash install.sh orchestration
Verifier Pro$39builder, confidence, decompositionbash install.sh verifier
Task Discipline$29tilldone, purpose-gatebash install.sh task
Autoresearch$39experiment loop, integritybash install.sh research
Observability$29tracer, cost, loop-detectbash install.sh observability
CEO Board$4911 agents + verifierbash install.sh board
Enterprise$199All 7 kitsbash install.sh
Quick Install ​
bash
# Windows
 powershell -File install.ps1
 
 # Mac/Linux
 bash install.sh
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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     Skill Kits | FDSA
     
     
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Skill Kits ​
20 production-ready SKILL.md files across 7 kits. Plug-and-play skills for Claude Code, Pi Agent, and OpenCode.
Available Kits ​
Security Foundation — $49 ​
6 skills: ACIP defense, L3 blacklist, L4 whitelist, L5 no-bash, damage-control rules, agent sandbox.
bash
bash install.sh security
Multi-Agent Orchestration — $49 ​
6 skills: agent-team, agent-chain, mental-model, domain-lock, tilldone, p2p-coms.
bash
bash install.sh orchestration
Verifier Pro — $39 ​
5 skills: verifier-builder, confidence-ladder, claim-decomposition, read-only-surface, escalation-handler.
bash
bash install.sh verifier
Task Discipline — $29 ​
4 skills: tilldone-core, tilldone-progress, tilldone-nudge, purpose-gate.
bash
bash install.sh task
Autoresearch — $39 ​
5 skills: experiment-loop, integrity-guard, median-over-best, reward-hack-defense, log-to-jsonl.
bash
bash install.sh research
Observability — $29 ​
4 skills: tool-call-tracer, cost-tracker, session-replay, loop-detector.
bash
bash install.sh observability
CEO Board — $49 ​
11 agents: CEO orchestrator, 8 board members, verifier, tracker.
bash
bash install.sh board
Enterprise Bundle — $199 (save $124) ​
All 7 kits + priority updates + custom skill development.
bash
bash install.sh
Individual Skills (Free) ​
SkillInvokeDescription
Agent Tutor/agent-tutorSocratic tutor for agentic engineering concepts
Feynman Technique/feynmanDeep learning via simplification
Marketplace Manifest ​
Compatible with Claude Code plugin system and MCP Market. Install via:
/plugin marketplace add agentic-engineering-skills
All 20 SKILL.md Files ​
skills/
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FDSA
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Skill Kits ​
20 production-ready SKILL.md files across 7 kits. Plug-and-play skills for Claude Code, Pi Agent, and OpenCode.
Available Kits ​
Security Foundation — $49 ​
6 skills: ACIP defense, L3 blacklist, L4 whitelist, L5 no-bash, damage-control rules, agent sandbox.
bash
bash install.sh security
Multi-Agent Orchestration — $49 ​
6 skills: agent-team, agent-chain, mental-model, domain-lock, tilldone, p2p-coms.
bash
bash install.sh orchestration
Verifier Pro — $39 ​
5 skills: verifier-builder, confidence-ladder, claim-decomposition, read-only-surface, escalation-handler.
bash
bash install.sh verifier
Task Discipline — $29 ​
4 skills: tilldone-core, tilldone-progress, tilldone-nudge, purpose-gate.
bash
bash install.sh task
Autoresearch — $39 ​
5 skills: experiment-loop, integrity-guard, median-over-best, reward-hack-defense, log-to-jsonl.
bash
bash install.sh research
Observability — $29 ​
4 skills: tool-call-tracer, cost-tracker, session-replay, loop-detector.
bash
bash install.sh observability
CEO Board — $49 ​
11 agents: CEO orchestrator, 8 board members, verifier, tracker.
bash
bash install.sh board
Enterprise Bundle — $199 (save $124) ​
All 7 kits + priority updates + custom skill development.
bash
bash install.sh
Individual Skills (Free) ​
SkillInvokeDescription
Agent Tutor/agent-tutorSocratic tutor for agentic engineering concepts
Feynman Technique/feynmanDeep learning via simplification
Marketplace Manifest ​
Compatible with Claude Code plugin system and MCP Market. Install via:
/plugin marketplace add agentic-engineering-skills
All 20 SKILL.md Files ​
skills/
 ├── individual/
 │   ├── agent-tutor/SKILL.md
 │   └── feynman/SKILL.md
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 │   ├── observability/           (4 skills)
 │   └── ceo-board/               (11 agents)
 └── .claude-plugin/marketplace.json
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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Troubleshooting & FAQ ​
Installation ​
Q: pip install anthropic fails with SSL error
 A: Update pip: python -m pip install --upgrade pip. If on Windows behind a corporate proxy, set set HTTPS_PROXY=http://proxy:port.
Q: python not found on Windows
 A: Install from python.org. Check "Add Python to PATH" during installation.
Q: ModuleNotFoundError: No module named 'yaml'
 A: pip install pyyaml
Labs ​
Q: Lab returns "No input provided"
 A: Check you're passing messages parameter, not prompt. The mock LLM expects: messages=[{"role": "user", "content": "your prompt"}]
Q: Agent loops forever
 A: MAX_ITERATIONS is not set or is too high. Set it to 10-15 for labs.
Q: Tool call returns empty result
 A: The mock LLM generates tool calls based on keyword detection. If your prompt doesn't contain trigger words (read, search, write), it won't generate tool calls.
Q: str_replace_editor not found
 A: That's an Anthropic-specific tool type. The mock LLM only supports basic tool_use blocks. Use the standard tool format shown in the labs.
Skills ​
Q: Installed skills don't appear in /skills menu
 A: Restart Claude Code after installation. Skills are loaded at startup.
Q: Skill says "not user-invocable"
 A: Check the YAML frontmatter has user-invocable: true. Kit master files intentionally omit this (they're documentation, not invocable skills).
Q: $ARGUMENTS not being replaced
 A: $ARGUMENTS is a placeholder. The agent (Claude Code / Pi) replaces it with your input when you invoke the skill via /command.
API Keys ​
Q: "This model is not available" from Anthropic
 A: You might need to request access to Claude Sonnet 4 / Opus 4. Check docs.anthropic.com for available models for your tier.
Q: Mock LLM works but real API returns 401
 A: Check ANTHROPIC_API_KEY is set correctly. Test with: python -c "import os; print(os.environ.get('ANTHROPIC_API_KEY', 'NOT SET')[:10])"
Q: OpenRouter returns 402 Payment Required
 A: Your free credits may be exhausted. Add payment method or switch to a different provider.
General ​
Q: The course files use \u2192 characters that show as garbage
 A: The course uses Unicode arrows (→) in diagrams. If your terminal doesn't support UTF-8, set $env:PYTHONIOENCODING='utf-8' on Windows or use a UTF-8 capable terminal (Windows Terminal, iTerm2, Ghostty).
Q: How do I cite this course?
 A: Reference "Agentic Engineering Course" with the module and lesson number.
Q: Can I teach this course?
 A: Course materials are provided for personal study. Contact for teaching license.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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FDSA
Appearance
Troubleshooting & FAQ ​
Installation ​
Q: pip install anthropic fails with SSL error
 A: Update pip: python -m pip install --upgrade pip. If on Windows behind a corporate proxy, set set HTTPS_PROXY=http://proxy:port.
Q: python not found on Windows
 A: Install from python.org. Check "Add Python to PATH" during installation.
Q: ModuleNotFoundError: No module named 'yaml'
 A: pip install pyyaml
Labs ​
Q: Lab returns "No input provided"
 A: Check you're passing messages parameter, not prompt. The mock LLM expects: messages=[{"role": "user", "content": "your prompt"}]
Q: Agent loops forever
 A: MAX_ITERATIONS is not set or is too high. Set it to 10-15 for labs.
Q: Tool call returns empty result
 A: The mock LLM generates tool calls based on keyword detection. If your prompt doesn't contain trigger words (read, search, write), it won't generate tool calls.
Q: str_replace_editor not found
 A: That's an Anthropic-specific tool type. The mock LLM only supports basic tool_use blocks. Use the standard tool format shown in the labs.
Skills ​
Q: Installed skills don't appear in /skills menu
 A: Restart Claude Code after installation. Skills are loaded at startup.
Q: Skill says "not user-invocable"
 A: Check the YAML frontmatter has user-invocable: true. Kit master files intentionally omit this (they're documentation, not invocable skills).
Q: $ARGUMENTS not being replaced
 A: $ARGUMENTS is a placeholder. The agent (Claude Code / Pi) replaces it with your input when you invoke the skill via /command.
API Keys ​
Q: "This model is not available" from Anthropic
 A: You might need to request access to Claude Sonnet 4 / Opus 4. Check docs.anthropic.com for available models for your tier.
Q: Mock LLM works but real API returns 401
 A: Check ANTHROPIC_API_KEY is set correctly. Test with: python -c "import os; print(os.environ.get('ANTHROPIC_API_KEY', 'NOT SET')[:10])"
Q: OpenRouter returns 402 Payment Required
 A: Your free credits may be exhausted. Add payment method or switch to a different provider.
General ​
Q: The course files use \u2192 characters that show as garbage
 A: The course uses Unicode arrows (→) in diagrams. If your terminal doesn't support UTF-8, set $env:PYTHONIOENCODING='utf-8' on Windows or use a UTF-8 capable terminal (Windows Terminal, iTerm2, Ghostty).
Q: How do I cite this course?
 A: Reference "Agentic Engineering Course" with the module and lesson number.
Q: Can I teach this course?
 A: Course materials are provided for personal study. Contact for teaching license.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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     Verify Certificate | FDSA
     
     
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Verify Certificate ​
Enter the certificate ID to verify:
Certificate verification system coming soon. For now, contact artale@fdsa.agency to verify a certificate.
Back to home
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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FDSA
Appearance
Verify Certificate ​
Enter the certificate ID to verify:
Certificate verification system coming soon. For now, contact artale@fdsa.agency to verify a certificate.
Back to home
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
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