diff --git a/site/.vitepress/config.mjs b/site/.vitepress/config.mjs index 3a93ae8..06c7784 100644 --- a/site/.vitepress/config.mjs +++ b/site/.vitepress/config.mjs @@ -12,6 +12,8 @@ export default defineConfig({ ['meta', { name: 'theme-color', content: '#0f0f0f' }], ['meta', { property: 'og:title', content: 'FDSA — Agentic Engineering Course' }], ['meta', { property: 'og:description', content: '65 lessons, 13 labs, 20 skill kits. Build production-grade AI agent systems.' }], + ['meta', { property: 'og:image', content: '/og-image.svg' }], + ['meta', { name: 'twitter:card', content: 'summary_large_image' }], ['link', { rel: 'preconnect', href: 'https://fonts.googleapis.com' }], ['link', { rel: 'preconnect', href: 'https://fonts.gstatic.com', crossorigin: '' }], ['link', { href: 'https://fonts.googleapis.com/css2?family=DM+Sans:opsz,wght@9..40,400;9..40,500;9..40,700;9..40,800&family=DM+Mono:wght@400;500&display=swap', rel: 'stylesheet' }], diff --git a/site/.vitepress/dist/404.html b/site/.vitepress/dist/404.html index 17e7c11..e435ef7 100644 --- a/site/.vitepress/dist/404.html +++ b/site/.vitepress/dist/404.html @@ -6,18 +6,20 @@ Page Not Found | FDSA - + - + - + + + @@ -27,7 +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 5747dc4..c7ddbea 100644 --- a/site/.vitepress/dist/api-keys.html +++ b/site/.vitepress/dist/api-keys.html @@ -6,18 +6,20 @@ API Key Setup Guide | FDSA - + - + - + + + @@ -38,7 +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.

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Prompt Injection (L0)","titles":["Module 3: Safety & Security","Lesson 3.1: Why Bash Is the Single Point of Failure"]},"335":{"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"]},"336":{"title":"External vs Internal Threat Model","titles":["Module 3: Safety & Security","Lesson 3.1: Why Bash Is the Single Point of Failure"]},"337":{"title":"Lesson 3.2: The 5-Level Security Ladder","titles":["Module 3: Safety & Security"]},"338":{"title":"Key Insight","titles":["Module 3: Safety & Security","Lesson 3.2: The 5-Level Security Ladder"]},"339":{"title":"Lesson 3.3: The L3 Marque Break","titles":["Module 3: Safety & Security"]},"340":{"title":"Lesson 3.4: Damage Control — Three Access Levels","titles":["Module 3: Safety & Security"]},"341":{"title":"Zero Access (can't read or write)","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"342":{"title":"Read-Only (can read, can't modify)","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"343":{"title":"No-Delete (can modify, can't delete)","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"344":{"title":"Implementation Pattern","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"345":{"title":"Lesson 3.5: Hook Architecture — 13 Lifecycle Events","titles":["Module 3: Safety & Security"]},"346":{"title":"Session Lifecycle","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"347":{"title":"Main Loop","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"348":{"title":"Subagent Lifecycle","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"349":{"title":"Maintenance","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"350":{"title":"Flow Control","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"351":{"title":"Lesson 3.6: The Verifier Pattern","titles":["Module 3: Safety & Security"]},"352":{"title":"Architecture","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"353":{"title":"Key Properties","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"354":{"title":"The Confidence Ladder","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"355":{"title":"Lesson 3.7: Defense-in-Depth Stacking","titles":["Module 3: Safety & Security"]},"356":{"title":"Lab 3.8: Implement L4 Whitelist Hook","titles":["Module 3: Safety & Security"]},"357":{"title":"Lab 3.9: Build a Verifier Agent","titles":["Module 3: Safety & Security"]},"358":{"title":"Module 4: Multi-Agent Orchestration","titles":[]},"359":{"title":"Lesson 4.1: Why One Agent Is Not Enough","titles":["Module 4: Multi-Agent Orchestration"]},"360":{"title":"The Stacking Insight","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.1: Why One Agent Is Not Enough"]},"361":{"title":"Lesson 4.2: Orchestration Patterns","titles":["Module 4: Multi-Agent Orchestration"]},"362":{"title":"Pattern 1: Dispatcher (Hub-and-Spoke)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"363":{"title":"Pattern 2: Pipeline (Sequential)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"364":{"title":"Pattern 3: Peer-to-Peer (Flat)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"365":{"title":"Lesson 4.2b: P-Threads — Parallel Agent Execution","titles":["Module 4: Multi-Agent Orchestration"]},"366":{"title":"Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"367":{"title":"When to Use P-Threads","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"368":{"title":"Implementation with mprocs","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"369":{"title":"Implementation with agent-mux","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"370":{"title":"Key Design Rules","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"371":{"title":"Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)","titles":["Module 4: Multi-Agent Orchestration"]},"372":{"title":"Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"373":{"title":"When to Use F-Threads","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"374":{"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)"]},"375":{"title":"Judge Criteria Template","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"376":{"title":"Lesson 4.3: Depth-2 Delegation","titles":["Module 4: Multi-Agent Orchestration"]},"377":{"title":"The Rule","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.3: Depth-2 Delegation"]},"378":{"title":"Lesson 4.4: Agent Experts That Remember","titles":["Module 4: Multi-Agent Orchestration"]},"379":{"title":"Mental Models","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"380":{"title":"Self-Improve Commands","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"381":{"title":"Compounding Knowledge","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"382":{"title":"Lesson 4.5: Domain Locking","titles":["Module 4: Multi-Agent Orchestration"]},"383":{"title":"Lesson 4.6: TillDone Task Discipline","titles":["Module 4: Multi-Agent Orchestration"]},"384":{"title":"The Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.6: TillDone Task Discipline"]},"385":{"title":"Why It Matters","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.6: TillDone Task Discipline"]},"386":{"title":"Lesson 4.7: Agent Chains","titles":["Module 4: Multi-Agent Orchestration"]},"387":{"title":"YAML-Defined Pipelines","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.7: Agent Chains"]},"388":{"title":"Variables","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.7: Agent Chains"]},"389":{"title":"Lesson 4.8: Pi-to-Pi Communication","titles":["Module 4: Multi-Agent Orchestration"]},"390":{"title":"The Shift: Hierarchy → Flat","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"391":{"title":"Reference Implementation: MCP Agent Mail (Jeff Emanuel, 1,955★)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"392":{"title":"Four Tools","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"393":{"title":"Safety","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"394":{"title":"Lesson 4.9: Conversation Awareness","titles":["Module 4: Multi-Agent Orchestration"]},"395":{"title":"Lesson 4.9b: Service Connectors","titles":["Module 4: Multi-Agent Orchestration"]},"396":{"title":"The Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"397":{"title":"Design Rules","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"398":{"title":"Reference Implementation","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"399":{"title":"Integration with Agent Teams","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"400":{"title":"Lesson 4.10: The CEO Board System","titles":["Module 4: Multi-Agent Orchestration"]},"401":{"title":"Flow","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.10: The CEO Board System"]},"402":{"title":"Lesson 4.11: UI Agents System","titles":["Module 4: Multi-Agent Orchestration"]},"403":{"title":"Lab 4.12: Build an Agent Chain","titles":["Module 4: Multi-Agent Orchestration"]},"404":{"title":"Lab 4.13: Deploy a Multi-Team System","titles":["Module 4: Multi-Agent Orchestration"]},"405":{"title":"Module 6: Economics & Evaluation","titles":[]},"406":{"title":"Lesson 6.0: The Compute Advantage Equation","titles":["Module 6: Economics & Evaluation"]},"407":{"title":"The Equation","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"408":{"title":"What It Tells You","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"409":{"title":"Real-World Application (Your Stack)","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"410":{"title":"How to Optimize","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"411":{"title":"Lesson 6.1: LLM Pricing Landscape 2026","titles":["Module 6: Economics & Evaluation"]},"412":{"title":"Per-Million-Token Pricing","titles":["Module 6: Economics & Evaluation","Lesson 6.1: LLM Pricing Landscape 2026"]},"413":{"title":"Price Range: 100x Difference","titles":["Module 6: Economics & Evaluation","Lesson 6.1: LLM Pricing Landscape 2026"]},"414":{"title":"Lesson 6.2: Cascade Routing","titles":["Module 6: Economics & Evaluation"]},"415":{"title":"The Pattern","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"416":{"title":"Savings Profile","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"417":{"title":"Implementation","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"418":{"title":"Lesson 6.3: Cost Per Session Math","titles":["Module 6: Economics & Evaluation"]},"419":{"title":"Where Costs Come From","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"420":{"title":"The Multipliers","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"421":{"title":"The 3x Rule","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"422":{"title":"Quick Estimation","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"423":{"title":"Lesson 6.4: Agent Evaluation Metrics","titles":["Module 6: Economics & Evaluation"]},"424":{"title":"The Key Metrics","titles":["Module 6: Economics & Evaluation","Lesson 6.4: Agent Evaluation Metrics"]},"425":{"title":"pass@k Explained","titles":["Module 6: Economics & Evaluation","Lesson 6.4: Agent Evaluation Metrics"]},"426":{"title":"Lesson 6.5: Automated Evaluation","titles":["Module 6: Economics & Evaluation"]},"427":{"title":"Golden Dataset Approach","titles":["Module 6: Economics & Evaluation","Lesson 6.5: Automated Evaluation"]},"428":{"title":"VCR-Style Recording","titles":["Module 6: Economics & Evaluation","Lesson 6.5: Automated Evaluation"]},"429":{"title":"Lesson 6.6: A/B Testing Agents","titles":["Module 6: Economics & Evaluation"]},"430":{"title":"Canary Deployment","titles":["Module 6: Economics & Evaluation","Lesson 6.6: A/B Testing Agents"]},"431":{"title":"What to A/B Test","titles":["Module 6: Economics & Evaluation","Lesson 6.6: A/B Testing Agents"]},"432":{"title":"Lesson 6.7: Human Evaluation","titles":["Module 6: Economics & Evaluation"]},"433":{"title":"What Automated Evals Miss","titles":["Module 6: Economics & Evaluation","Lesson 6.7: Human Evaluation"]},"434":{"title":"Spot-Check Sampling","titles":["Module 6: Economics & Evaluation","Lesson 6.7: Human Evaluation"]},"435":{"title":"Lab 6.8: Build an Eval Harness","titles":["Module 6: Economics & Evaluation"]},"436":{"title":"Lab 6.9: Cost Optimization","titles":["Module 6: Economics & Evaluation"]},"437":{"title":"Module 7: Advanced Topics","titles":[]},"438":{"title":"Lesson 7.1: Autoresearch","titles":["Module 7: Advanced Topics"]},"439":{"title":"The Core Loop","titles":["Module 7: Advanced Topics","Lesson 7.1: Autoresearch"]},"440":{"title":"Brand Monitor Example","titles":["Module 7: Advanced Topics","Lesson 7.1: Autoresearch"]},"441":{"title":"Lesson 7.2: The Experiment Loop — Integrity","titles":["Module 7: Advanced Topics"]},"442":{"title":"Three Threats to Integrity","titles":["Module 7: Advanced Topics","Lesson 7.2: The Experiment Loop — Integrity"]},"443":{"title":"Integrity Guards","titles":["Module 7: Advanced Topics","Lesson 7.2: The Experiment Loop — Integrity"]},"444":{"title":"Lesson 7.3: Meta-Agents — Agents That Build Agents","titles":["Module 7: Advanced Topics"]},"445":{"title":"The Pattern","titles":["Module 7: Advanced Topics","Lesson 7.3: Meta-Agents — Agents That Build Agents"]},"446":{"title":"Pi-Pi Meta-Agent","titles":["Module 7: Advanced Topics","Lesson 7.3: Meta-Agents — Agents That Build Agents"]},"447":{"title":"Lesson 7.4: Beyond MCP — The Context Cost Trade-off","titles":["Module 7: Advanced Topics"]},"448":{"title":"The Matrix","titles":["Module 7: Advanced Topics","Lesson 7.4: Beyond MCP — The Context Cost Trade-off"]},"449":{"title":"The Insight","titles":["Module 7: Advanced Topics","Lesson 7.4: Beyond MCP — The Context Cost Trade-off"]},"450":{"title":"Lesson 7.5: The Mac Mini Agent — Physical Sandbox","titles":["Module 7: Advanced Topics"]},"451":{"title":"Architecture","titles":["Module 7: Advanced Topics","Lesson 7.5: The Mac Mini Agent — Physical Sandbox"]},"452":{"title":"Sentinel Pattern","titles":["Module 7: Advanced Topics","Lesson 7.5: The Mac Mini Agent — Physical Sandbox"]},"453":{"title":"Lesson 7.6: Always-On Agents","titles":["Module 7: Advanced Topics"]},"454":{"title":"Voice-to-Command Bridge","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"455":{"title":"Job Server Pattern","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"456":{"title":"Heartbeat Execution (from Paperclip)","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"457":{"title":"Scheduling Strategies","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"458":{"title":"OpenClaw: The Always-On Employee","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"459":{"title":"When NOT to Use Always-On","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"460":{"title":"Lab 7.7: Build an Autoresearch Loop","titles":["Module 7: Advanced Topics"]},"461":{"title":"Lab 7.8: Meta-Agent","titles":["Module 7: Advanced Topics"]},"462":{"title":"Module 5: Production Patterns","titles":[]},"463":{"title":"Lesson 5.1: What Production Means for Agents","titles":["Module 5: Production Patterns"]},"464":{"title":"Production Readiness Checklist","titles":["Module 5: Production Patterns","Lesson 5.1: What Production Means for Agents"]},"465":{"title":"Lesson 5.2: CI/CD for Agents","titles":["Module 5: Production Patterns"]},"466":{"title":"Golden Dataset","titles":["Module 5: Production Patterns","Lesson 5.2: CI/CD for Agents"]},"467":{"title":"Pipeline","titles":["Module 5: Production Patterns","Lesson 5.2: CI/CD for Agents"]},"468":{"title":"Lesson 5.2b: Case Study — The 5-Tool Production Stack","titles":["Module 5: Production Patterns"]},"469":{"title":"The Stack","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"470":{"title":"Tool Roles and Selection Logic","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"471":{"title":"How They Work Together (Real Session Flow)","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"472":{"title":"Key Production Patterns","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"473":{"title":"Lesson 5.2c: The Agent Manager Role","titles":["Module 5: Production Patterns"]},"474":{"title":"Responsibilities","titles":["Module 5: Production Patterns","Lesson 5.2c: The Agent Manager Role"]},"475":{"title":"90-Day Setup Playbook","titles":["Module 5: Production Patterns","Lesson 5.2c: The Agent Manager Role"]},"476":{"title":"Lesson 5.3: Shadow Deployments","titles":["Module 5: Production Patterns"]},"477":{"title":"How It Works","titles":["Module 5: Production Patterns","Lesson 5.3: Shadow Deployments"]},"478":{"title":"When to Shadow Deploy","titles":["Module 5: Production Patterns","Lesson 5.3: Shadow Deployments"]},"479":{"title":"Lesson 5.4: Rollback Strategies","titles":["Module 5: Production Patterns"]},"480":{"title":"What Rollback Means for Agents","titles":["Module 5: Production Patterns","Lesson 5.4: Rollback Strategies"]},"481":{"title":"Implementation","titles":["Module 5: Production Patterns","Lesson 5.4: Rollback Strategies"]},"482":{"title":"Lesson 5.5: Observability & Monitoring","titles":["Module 5: Production Patterns"]},"483":{"title":"What to Trace (Every Single Turn)","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"484":{"title":"Decision Tracing","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"485":{"title":"Key Metrics","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"486":{"title":"Lesson 5.6: Alerting on Agent-Specific Signals","titles":["Module 5: Production Patterns"]},"487":{"title":"What to Alert On","titles":["Module 5: Production Patterns","Lesson 5.6: Alerting on Agent-Specific Signals"]},"488":{"title":"Alert Routing","titles":["Module 5: Production Patterns","Lesson 5.6: Alerting on Agent-Specific Signals"]},"489":{"title":"Lesson 5.6b: Cross-Provider Session Search","titles":["Module 5: Production Patterns"]},"490":{"title":"The Problem","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"491":{"title":"The Solution","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"492":{"title":"Why This Matters for Production","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"493":{"title":"Integration with Our Stack","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"494":{"title":"Lesson 5.7: Deployment Modes","titles":["Module 5: Production Patterns"]},"495":{"title":"Reachability","titles":["Module 5: Production Patterns","Lesson 5.7: Deployment Modes"]},"496":{"title":"Lesson 5.8: Cost Control","titles":["Module 5: Production Patterns"]},"497":{"title":"Budget Architecture","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"498":{"title":"Budget Policies","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"499":{"title":"Warning vs Hard Stop","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"500":{"title":"Lab 5.9: Set Up Agent Observability","titles":["Module 5: Production Patterns"]},"501":{"title":"Lab 5.10: CI/CD Pipeline","titles":["Module 5: Production Patterns"]},"502":{"title":"Non-Technical Track: Decision Frameworks for AI Agents","titles":[]},"503":{"title":"For Managers, PMs, and Decision-Makers","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"504":{"title":"Framework 1: "Should We Use an Agent for This?"","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"505":{"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?""]},"506":{"title":"Decision Matrix","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 1: "Should We Use an Agent for This?""]},"507":{"title":"Framework 2: The 4 Agent Design Patterns (Andrew Ng)","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"508":{"title":"1. Reflection","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 2: The 4 Agent Design Patterns (Andrew Ng)"]},"509":{"title":"2. Tool Use","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 2: The 4 Agent Design Patterns (Andrew Ng)"]},"510":{"title":"3. Planning","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 2: The 4 Agent Design Patterns (Andrew Ng)"]},"511":{"title":"4. Multi-Agent Collaboration","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 2: The 4 Agent Design Patterns (Andrew Ng)"]},"512":{"title":"Framework 3: Cost Estimation for Agent Projects","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"513":{"title":"The 3x Rule","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 3: Cost Estimation for Agent Projects"]},"514":{"title":"Quick Cost Calculator","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 3: Cost Estimation for Agent Projects"]},"515":{"title":"Cost by Autonomy Level","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 3: Cost Estimation for Agent Projects"]},"516":{"title":"Framework 4: Autonomy vs Risk Matrix","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"517":{"title":"Framework 5: The "Build vs Buy" Decision","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"518":{"title":"Build an Agent When:","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 5: The "Build vs Buy" Decision"]},"519":{"title":"Buy/Rent When:","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 5: The "Build vs Buy" Decision"]},"520":{"title":"Hybrid When:","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 5: The "Build vs Buy" Decision"]},"521":{"title":"Framework 6: Measuring Success","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"522":{"title":"Leading Indicators (week 1-4)","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 6: Measuring Success"]},"523":{"title":"Lagging Indicators (month 2-3+)","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 6: Measuring Success"]},"524":{"title":"Red Flags","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 6: Measuring Success"]},"525":{"title":"Framework 7: Governance & Compliance Quick Reference","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"526":{"title":"Regulatory Requirements by Region","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 7: Governance & Compliance Quick Reference"]},"527":{"title":"Minimum Viable Governance","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 7: Governance & Compliance Quick Reference"]},"528":{"title":"Summary: Decision Flow","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"529":{"title":"Module 8: Capstone — Production Multi-Agent System","titles":[]},"530":{"title":"Overview","titles":["Module 8: Capstone — Production Multi-Agent System"]},"531":{"title":"Project Options","titles":["Module 8: Capstone — Production Multi-Agent System"]},"532":{"title":"Option 1: Brand Monitor (Intermediate)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"533":{"title":"Option 2: Code Review Pipeline (Intermediate-Advanced)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"534":{"title":"Option 3: Strategic Decision Board (Advanced)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"535":{"title":"Capstone Phases","titles":["Module 8: Capstone — Production Multi-Agent System"]},"536":{"title":"Phase 1: System Design (60-90 min)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"537":{"title":"Phase 2: Agent Implementation (2-4 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"538":{"title":"Phase 3: Integration (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"539":{"title":"Phase 4: Security Hardening (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"540":{"title":"Phase 5: Testing & 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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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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
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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(a,(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:"https://gumroad.com",method:"get",target:"_blank"},[i("input",{type:"email",class:"newsletter-input",placeholder:"you@example.com",required:"",disabled:"",value:"Join waitlist — coming soon"}),i("button",{type:"submit",class:"btn-p btn-primary",disabled:""},"Subscribe")]),i("p",{class:"newsletter-note"},[x("Newsletter launching soon. Follow "),i("a",{href:"https://github.com/Artale",target:"_blank"},"@Artale"),x(" for updates.")])],-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",ai,[(o(),d(T,null,I(u,m=>i("div",{class:"compare-c",key:m.name},[i("div",si,$(m.name),1),i("ul",ni,[(o(!0),d(T,null,I(m.pros,P=>(o(),d("li",{key:P},[l[6]||(l[6]=i("span",{class:"material-symbols-outlined"},"check",-1)),x($(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($(P),1)]))),128))])])),64))])]),i("section",ii,[l[12]||(l[12]=i("div",{class:"sec-label"},"Investment",-1)),l[13]||(l[13]=i("h2",{class:"sec-title"},"Choose Your Path",-1)),i("div",li,[(o(),d(T,null,I(s,m=>i("div",{class:w(["price-card",{highlight:m.highlight}]),key:m.name},[i("div",ri,$(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-c3956a1f"]]),yi={extends:Ee,Layout:Mo,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.Dp39xggp.js b/site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.js similarity index 71% rename from site/.vitepress/dist/assets/modules_m2-architecture.md.Dp39xggp.js rename to site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.js index e2d6ab3..06f0c2d 100644 --- a/site/.vitepress/dist/assets/modules_m2-architecture.md.Dp39xggp.js +++ b/site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.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 2: Agent Architecture","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m2-architecture.md","filePath":"modules/m2-architecture.md","lastUpdated":1780488246000}'),i={name:"modules/m2-architecture.md"};function l(o,s,r,p,d,h){return a(),t("div",null,[...s[0]||(s[0]=[n(`

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":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:
┌─────────────────────────────────────────────────────────────┐
 │                      AGENT SYSTEM                           │
 ├──────────────┬──────────────┬──────────────┬────────────────┤
 │   TOOLS      │    LOOP      │   CONTEXT    │    MEMORY      │
@@ -71,4 +71,30 @@ import{c as e,Q as a,j as t,m as n}from"./chunks/framework.BPKcPtvA.js";const u=
         query: The search terms
         reasoning: WHY you are searching for this (required for audit)
     """
-    ...
Why it works:
  1. Forces the LLM to articulate intent before acting
  2. Creates an audit trail of every decision
  3. Catches hallucinated tool calls (if the reasoning is nonsense, the call is suspect)
  4. Gives the LLM an extra "thinking step" without using chain-of-thought
Lesson 2.7: Codebase Architectures for Agents ​
From single-file-agents research, 4 patterns for scaling agent codebases:
ArchitectureStructureBest For
Atomic/Composableatom/ → molecule/ → organism/Reusable tool primitives across many agents
Layeredapi/ → services/ → models/ → data/Clear separation of concerns within one agent
Pipelinesteps/ → pipeline_manager/ → shared/Data transformation flows
Vertical Slicefeatures/{feature}/{api,service,model}/Multiple independent agent capabilities
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
`,99)])])}const g=e(i,[["render",l]]);export{u as __pageData,g as default};
+    ...

Why it works:

  1. Forces the LLM to articulate intent before acting
  2. Creates an audit trail of every decision
  3. Catches hallucinated tool calls (if the reasoning is nonsense, the call is suspect)
  4. Gives the LLM an extra "thinking step" without using chain-of-thought

Lesson 2.7: Codebase Architectures for Agents ​

From single-file-agents research, 4 patterns for scaling agent codebases:

ArchitectureStructureBest For
Atomic/Composableatom/ → molecule/ → organism/Reusable tool primitives across many agents
Layeredapi/ → services/ → models/ → data/Clear separation of concerns within one agent
Pipelinesteps/ → pipeline_manager/ → shared/Data transformation flows
Vertical Slicefeatures/{feature}/{api,service,model}/Multiple independent agent capabilities

Choosing the Right Architecture ​

There's no single right answer. The choice depends on your agent's role:

Agent TypeRecommended ArchWhy
Single-purpose tool agentAtomicSimple, composable, testable
Multi-step workflow agentPipelineClear stage boundaries
Complex reasoning agentLayeredSeparation of concerns
Multi-capability platformVertical SliceIndependent feature teams

Rule of thumb: Start with Atomic (single file per tool). Only add architecture when the agent has 5+ tools or 3+ agents share tools.

Lesson 2.7b: Configuration Architecture ​

Where does agent configuration live? Three patterns:

Pattern A: Flat Config (Single File) ​

yaml
# agent-config.yaml — everything in one place
+agent:
+  name: "code-reviewer"
+  model: claude-sonnet-4
+  tools: [read_file, grep_search, list_files]
+  max_turns: 15
+  hooks:
+    - pre-tool/l3-blacklist
+    - post-tool/logger

Best for: Single-agent projects, prototyping, small teams

Pattern B: Layered Config (Directory Structure) ​

agents/
+├── base.yaml          ← shared defaults (model, security)
+├── reviewer.yaml      ← extends base.yaml  
+├── builder.yaml       ← extends base.yaml
+└── orchestrator.yaml  ← extends base.yaml

Best for: Multi-agent systems, team environments

Pattern C: Discoverable Config (Agent-Readable) ​

repo/
+├── AGENTS.md         ← agent instructions
+├── skills/           ← skill definitions
+├── .mcp.json         ← MCP server config
+├── .claude/hooks/    ← lifecycle hooks
+└── teams.yaml        ← multi-team config

Best for: Production systems where agents need to self-configure

Lesson 2.7c: Error Handling Architecture ​

Every agent needs four error-handling layers:

  1. Tool-level: Tool returns error string instead of crashing (all labs teach this)
  2. Loop-level: MAX_ITERATIONS prevents infinite loops (every lab has this)
  3. Agent-level: Retry with backoff on API failures
  4. System-level: Supervisor agent or human handoff for unrecoverable errors
python
# System-level error handling pattern
+MAX_RETRIES = 3
+for attempt in range(MAX_RETRIES):
+    try:
+        response = client.messages.create(...)
+        return process_response(response)
+    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

`,120)])])}const g=a(n,[["render",l]]);export{k as __pageData,g as default}; diff --git a/site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.lean.js b/site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.lean.js new file mode 100644 index 0000000..7aace02 --- /dev/null +++ b/site/.vitepress/dist/assets/modules_m2-architecture.md.D4c6-Kzn.lean.js @@ -0,0 +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}; diff --git a/site/.vitepress/dist/assets/modules_m2-architecture.md.Dp39xggp.lean.js b/site/.vitepress/dist/assets/modules_m2-architecture.md.Dp39xggp.lean.js deleted file mode 100644 index 36d313a..0000000 --- a/site/.vitepress/dist/assets/modules_m2-architecture.md.Dp39xggp.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{c as e,Q as a,j as t,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"Module 2: Agent Architecture","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m2-architecture.md","filePath":"modules/m2-architecture.md","lastUpdated":1780488246000}'),i={name:"modules/m2-architecture.md"};function l(o,s,r,p,d,h){return a(),t("div",null,[...s[0]||(s[0]=[n("",99)])])}const g=e(i,[["render",l]]);export{u as __pageData,g as default}; diff --git a/site/.vitepress/dist/assets/modules_m3-safety.md.CkKCTJdi.js b/site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.js similarity index 84% rename from site/.vitepress/dist/assets/modules_m3-safety.md.CkKCTJdi.js rename to site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.js index 51974ac..412bae9 100644 --- a/site/.vitepress/dist/assets/modules_m3-safety.md.CkKCTJdi.js +++ b/site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.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,c){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":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
 SYSTEM_PROMPT_PATCH = """
 ## Security Protocol (MANDATORY)
 
@@ -66,9 +66,16 @@ import{c as e,Q as a,j as t,m as n}from"./chunks/framework.BPKcPtvA.js";const u=
 4. Tool execution         ─── actual work happens
 5. PostToolUse hook       ─── validates results
 6. Verifier agent         ─── re-verifies independently (read-only)
-7. Session logging        ─── full audit trail
Each layer independently catches failures the previous layers missed.
Lab 3.8: Implement L4 Whitelist Hook ​
Objective: Block all bash commands EXCEPT 10 safelisted patterns.
Starter: course/labs/L3-whitelist-hook/starter.py
python
# TODO: Implement whitelist hook
+7. Session logging        ─── full audit trail
Each layer independently catches failures the previous layers missed.
Lesson 3.7b: Security Incident Response for Agents ​
When an agent does something it shouldn't, you need a playbook. Here's the incident response framework for agent systems:
Detection ​
SignalWhat It MeansExample
Cost spikeAgent is looping100+ tool calls in 5 minutes
Unusual tool sequenceAgent deviating from expected pathrm called when not expected
Permission denialsAgent hitting domain locksAgent tried to write outside its path
Hallucinated toolsLLM calling nonexistent tools"execute_revenue_report"
Response Playbook ​
1. PAUSE: Kill the agent session immediately
+2. ISOLATE: Check if changes were made (git status, diff)
+3. ANALYZE: Read the agent's session log — what was it trying to do?
+4. FIX: Update rules/hooks to prevent recurrence
+5. RESUME: Restart with corrected config
+6. POSTMORTEM: Document what happened and why
Kill Switch Pattern ​
Every production agent needs a kill switch:
bash
# emergency-kill.sh — run immediately when agent goes rogue
+pkill -f "claude|pi|opencode"
+git checkout -- .  # revert all uncommitted changes
Important: Test your kill switch regularly. It's not a kill switch if you've never run it.
Lab 3.8: Implement L4 Whitelist Hook ​
Objective: Block all bash commands EXCEPT 10 safelisted patterns.
Starter: course/labs/L3-whitelist-hook/starter.py
python
# TODO: Implement whitelist hook
 # 1. Define safelist regex patterns
 # 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
`,80)])])}const k=e(i,[["render",l]]);export{u as __pageData,k as default};
+# 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
`,91)])])}const k=e(i,[["render",l]]);export{u as __pageData,k as default}; diff --git a/site/.vitepress/dist/assets/modules_m3-safety.md.CkKCTJdi.lean.js b/site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.lean.js similarity index 71% rename from site/.vitepress/dist/assets/modules_m3-safety.md.CkKCTJdi.lean.js rename to site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.lean.js index d442ce4..b6c459d 100644 --- a/site/.vitepress/dist/assets/modules_m3-safety.md.CkKCTJdi.lean.js +++ b/site/.vitepress/dist/assets/modules_m3-safety.md.CvcrzqYZ.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,c){return a(),t("div",null,[...s[0]||(s[0]=[n("",80)])])}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":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}; diff --git a/site/.vitepress/dist/assets/modules_m6-economics.md.B6XI2-r7.js b/site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.js similarity index 83% rename from site/.vitepress/dist/assets/modules_m6-economics.md.B6XI2-r7.js rename to site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.js index cce4ef2..2ec76de 100644 --- a/site/.vitepress/dist/assets/modules_m6-economics.md.B6XI2-r7.js +++ b/site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.js @@ -1,4 +1,4 @@ -import{c as s,Q as a,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 a(),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":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)
 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:
@@ -28,4 +28,20 @@ import{c as s,Q as a,j as e,m as i}from"./chunks/framework.BPKcPtvA.js";const k=
     }
 ]
VCR-Style Recording ​
For non-deterministic tool results (search, API calls), record the response once, then replay it deterministically:
python
# Record mode: capture real responses
 # Replay mode: use recorded responses
-# Test: verify agent makes correct decisions with known data
Lesson 6.6: A/B Testing Agents ​
Canary Deployment ​
  1. Route 5% of traffic to new agent config
  2. Compare against 95% on current config
  3. Metrics: success rate, cost, latency, loop depth
  4. If new config wins on all metrics → roll out to 100%
  5. If new config loses → rollback, investigate
What to A/B Test ​
  • System prompt wording
  • Model selection
  • Temperature settings
  • Tool descriptions
  • Iteration limits
Lesson 6.7: Human Evaluation ​
What Automated Evals Miss ​
  1. Quality of reasoning — agent made right decision for wrong reasons?
  2. Tone and style — output technically correct but poorly written?
  3. Edge cases — agent handled happy path but not real-world variation?
  4. Hallucination cascades — plausible-looking but wrong intermediate steps?
Spot-Check Sampling ​
Review 5-10% of all agent sessions manually. Focus on:
  • Sessions with high cost (>2x average)
  • Sessions with tool loops
  • Sessions where automated eval scored low
  • Random sample for baseline
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
`,73)])])}const u=s(n,[["render",l]]);export{k as __pageData,u as default};
+# Test: verify agent makes correct decisions with known data

Lesson 6.6: A/B Testing Agents ​

Canary Deployment ​

  1. Route 5% of traffic to new agent config
  2. Compare against 95% on current config
  3. Metrics: success rate, cost, latency, loop depth
  4. If new config wins on all metrics → roll out to 100%
  5. If new config loses → rollback, investigate

What to A/B Test ​

  • System prompt wording
  • Model selection
  • Temperature settings
  • Tool descriptions
  • Iteration limits

Lesson 6.7: Human Evaluation ​

What Automated Evals Miss ​

  1. Quality of reasoning — agent made right decision for wrong reasons?
  2. Tone and style — output technically correct but poorly written?
  3. Edge cases — agent handled happy path but not real-world variation?
  4. Hallucination cascades — plausible-looking but wrong intermediate steps?

Spot-Check Sampling ​

Review 5-10% of all agent sessions manually. Focus on:

  • Sessions with high cost (>2x average)
  • Sessions with tool loops
  • Sessions where automated eval scored low
  • Random sample for baseline

Lesson 6.7b: Real-World Cost Scenarios ​

Scenario A: Research Agent (High Token Burn) ​

Pattern: Agent reads 20 web pages, synthesizes report
+Cost breakdown:
+├── 20 web page reads @ 2K tokens each = 40K input tokens
+├── 5 LLM reasoning turns @ 4K tokens = 20K tokens
+├── 1 report generation @ 8K output tokens = 8K output tokens
+├── Claude Sonnet: ~$0.08/run
+├── Claude Haiku: ~$0.02/run (80% cheaper, 90% as good for extraction)
+└── Cascade: Haiku for reads, Sonnet for synthesis → $0.03/run

The cascade saves 62% on this exact pattern. Use cheap models for bulk work, expensive models for synthesis.

Scenario B: Code Generation (Iterative) ​

Pattern: Agent writes code, tests, fixes, repeats
+Cost without optimization:
+├── Average: 8 iterations × 6K tokens = 48K tokens
+├── Cost: ~$0.35 per feature
+├── With guardrails (limit to 3 iterations): $0.13 per feature
+└── Savings: 63%

Scenario C: Always-On Brand Monitor ​

Pattern: Scans 50 sources every 5 minutes, 24/7
+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

`,86)])])}const u=a(n,[["render",l]]);export{k as __pageData,u as default}; diff --git a/site/.vitepress/dist/assets/modules_m6-economics.md.B6XI2-r7.lean.js b/site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.lean.js similarity index 59% rename from site/.vitepress/dist/assets/modules_m6-economics.md.B6XI2-r7.lean.js rename to site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.lean.js index 0a2d134..ceb8ba5 100644 --- a/site/.vitepress/dist/assets/modules_m6-economics.md.B6XI2-r7.lean.js +++ b/site/.vitepress/dist/assets/modules_m6-economics.md.DgzEiG6_.lean.js @@ -1 +1 @@ -import{c as s,Q as a,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 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var(--vp-c-brand-1);--vp-code-link-color: var(--vp-c-brand-1);--vp-code-link-hover-color: var(--vp-c-brand-2);--vp-code-bg: var(--vp-c-default-soft);--vp-code-block-color: var(--vp-c-text-2);--vp-code-block-bg: var(--vp-c-bg-alt);--vp-code-block-divider-color: var(--vp-c-gutter);--vp-code-lang-color: var(--vp-c-text-3);--vp-code-line-highlight-color: var(--vp-c-default-soft);--vp-code-line-number-color: var(--vp-c-text-3);--vp-code-line-diff-add-color: var(--vp-c-success-soft);--vp-code-line-diff-add-symbol-color: var(--vp-c-success-1);--vp-code-line-diff-remove-color: var(--vp-c-danger-soft);--vp-code-line-diff-remove-symbol-color: var(--vp-c-danger-1);--vp-code-line-warning-color: var(--vp-c-warning-soft);--vp-code-line-error-color: var(--vp-c-danger-soft);--vp-code-copy-code-border-color: var(--vp-c-divider);--vp-code-copy-code-bg: var(--vp-c-bg-soft);--vp-code-copy-code-hover-border-color: var(--vp-c-divider);--vp-code-copy-code-hover-bg: var(--vp-c-bg);--vp-code-copy-code-active-text: var(--vp-c-text-2);--vp-code-copy-copied-text-content: "Copied";--vp-code-tab-divider: var(--vp-code-block-divider-color);--vp-code-tab-text-color: var(--vp-c-text-2);--vp-code-tab-bg: var(--vp-code-block-bg);--vp-code-tab-hover-text-color: var(--vp-c-text-1);--vp-code-tab-active-text-color: var(--vp-c-text-1);--vp-code-tab-active-bar-color: var(--vp-c-brand-1)}:lang(es),:lang(pt){--vp-code-copy-copied-text-content: "Copiado"}:lang(fa){--vp-code-copy-copied-text-content: "کپی شد"}:lang(ko){--vp-code-copy-copied-text-content: "복사됨"}:lang(ru){--vp-code-copy-copied-text-content: "Скопировано"}:lang(zh){--vp-code-copy-copied-text-content: "已复制"}:root{--vp-button-brand-border: transparent;--vp-button-brand-text: var(--vp-c-white);--vp-button-brand-bg: var(--vp-c-brand-3);--vp-button-brand-hover-border: transparent;--vp-button-brand-hover-text: var(--vp-c-white);--vp-button-brand-hover-bg: var(--vp-c-brand-2);--vp-button-brand-active-border: transparent;--vp-button-brand-active-text: var(--vp-c-white);--vp-button-brand-active-bg: var(--vp-c-brand-1);--vp-button-alt-border: transparent;--vp-button-alt-text: var(--vp-c-text-1);--vp-button-alt-bg: var(--vp-c-default-3);--vp-button-alt-hover-border: transparent;--vp-button-alt-hover-text: var(--vp-c-text-1);--vp-button-alt-hover-bg: var(--vp-c-default-2);--vp-button-alt-active-border: transparent;--vp-button-alt-active-text: var(--vp-c-text-1);--vp-button-alt-active-bg: var(--vp-c-default-1);--vp-button-sponsor-border: var(--vp-c-text-2);--vp-button-sponsor-text: var(--vp-c-text-2);--vp-button-sponsor-bg: transparent;--vp-button-sponsor-hover-border: var(--vp-c-sponsor);--vp-button-sponsor-hover-text: var(--vp-c-sponsor);--vp-button-sponsor-hover-bg: transparent;--vp-button-sponsor-active-border: var(--vp-c-sponsor);--vp-button-sponsor-active-text: var(--vp-c-sponsor);--vp-button-sponsor-active-bg: transparent}:root{--vp-custom-block-font-size: 14px;--vp-custom-block-code-font-size: 13px;--vp-custom-block-info-border: transparent;--vp-custom-block-info-text: var(--vp-c-text-1);--vp-custom-block-info-bg: var(--vp-c-default-soft);--vp-custom-block-info-code-bg: var(--vp-c-default-soft);--vp-custom-block-note-border: transparent;--vp-custom-block-note-text: var(--vp-c-text-1);--vp-custom-block-note-bg: var(--vp-c-default-soft);--vp-custom-block-note-code-bg: var(--vp-c-default-soft);--vp-custom-block-tip-border: transparent;--vp-custom-block-tip-text: var(--vp-c-text-1);--vp-custom-block-tip-bg: var(--vp-c-tip-soft);--vp-custom-block-tip-code-bg: var(--vp-c-tip-soft);--vp-custom-block-important-border: transparent;--vp-custom-block-important-text: var(--vp-c-text-1);--vp-custom-block-important-bg: var(--vp-c-important-soft);--vp-custom-block-important-code-bg: var(--vp-c-important-soft);--vp-custom-block-warning-border: transparent;--vp-custom-block-warning-text: var(--vp-c-text-1);--vp-custom-block-warning-bg: var(--vp-c-warning-soft);--vp-custom-block-warning-code-bg: var(--vp-c-warning-soft);--vp-custom-block-danger-border: transparent;--vp-custom-block-danger-text: var(--vp-c-text-1);--vp-custom-block-danger-bg: var(--vp-c-danger-soft);--vp-custom-block-danger-code-bg: var(--vp-c-danger-soft);--vp-custom-block-caution-border: transparent;--vp-custom-block-caution-text: var(--vp-c-text-1);--vp-custom-block-caution-bg: var(--vp-c-caution-soft);--vp-custom-block-caution-code-bg: var(--vp-c-caution-soft);--vp-custom-block-details-border: var(--vp-custom-block-info-border);--vp-custom-block-details-text: var(--vp-custom-block-info-text);--vp-custom-block-details-bg: var(--vp-custom-block-info-bg);--vp-custom-block-details-code-bg: var(--vp-custom-block-info-code-bg)}:root{--vp-input-border-color: var(--vp-c-border);--vp-input-bg-color: var(--vp-c-bg-alt);--vp-input-switch-bg-color: var(--vp-c-default-soft)}:root{--vp-nav-height: 64px;--vp-nav-bg-color: var(--vp-c-bg);--vp-nav-screen-bg-color: var(--vp-c-bg);--vp-nav-logo-height: 24px}.hide-nav{--vp-nav-height: 0px}.hide-nav .VPSidebar{--vp-nav-height: 22px}:root{--vp-local-nav-bg-color: var(--vp-c-bg)}:root{--vp-sidebar-width: 272px;--vp-sidebar-bg-color: var(--vp-c-bg-alt)}:root{--vp-backdrop-bg-color: rgba(0, 0, 0, .6)}:root{--vp-home-hero-name-color: var(--vp-c-brand-1);--vp-home-hero-name-background: transparent;--vp-home-hero-image-background-image: none;--vp-home-hero-image-filter: none}:root{--vp-badge-info-border: transparent;--vp-badge-info-text: var(--vp-c-text-2);--vp-badge-info-bg: var(--vp-c-default-soft);--vp-badge-tip-border: transparent;--vp-badge-tip-text: var(--vp-c-tip-1);--vp-badge-tip-bg: var(--vp-c-tip-soft);--vp-badge-warning-border: transparent;--vp-badge-warning-text: var(--vp-c-warning-1);--vp-badge-warning-bg: var(--vp-c-warning-soft);--vp-badge-danger-border: transparent;--vp-badge-danger-text: var(--vp-c-danger-1);--vp-badge-danger-bg: var(--vp-c-danger-soft)}:root{--vp-carbon-ads-text-color: 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mark{background-color:var(--vp-local-search-highlight-bg);color:var(--vp-local-search-highlight-text);border-radius:2px;padding:0 2px}.excerpt[data-v-68e678c9] .vp-code-group .tabs{display:none}.excerpt[data-v-68e678c9] .vp-code-group div[class*=language-]{border-radius:8px!important}.excerpt-gradient-bottom[data-v-68e678c9]{position:absolute;bottom:-1px;left:0;width:100%;height:8px;background:linear-gradient(transparent,var(--vp-local-search-result-bg));z-index:1000}.excerpt-gradient-top[data-v-68e678c9]{position:absolute;top:-1px;left:0;width:100%;height:8px;background:linear-gradient(var(--vp-local-search-result-bg),transparent);z-index:1000}.result.selected .titles[data-v-68e678c9],.result.selected .title-icon[data-v-68e678c9]{color:var(--vp-c-brand-1)!important}.no-results[data-v-68e678c9]{font-size:.9rem;text-align:center;padding:12px}svg[data-v-68e678c9]{flex:none} diff --git a/site/.vitepress/dist/blog/index.html b/site/.vitepress/dist/blog/index.html index a6c82ed..425de14 100644 --- a/site/.vitepress/dist/blog/index.html +++ b/site/.vitepress/dist/blog/index.html @@ -6,18 +6,20 @@ Blog | FDSA - + - + - + + + @@ -27,7 +29,7 @@
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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.

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Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/blog/posts/cascade-routing.html b/site/.vitepress/dist/blog/posts/cascade-routing.html index 6654b66..c457e66 100644 --- a/site/.vitepress/dist/blog/posts/cascade-routing.html +++ b/site/.vitepress/dist/blog/posts/cascade-routing.html @@ -6,18 +6,20 @@ Cascade Routing: Cut Your API Costs by 66% | FDSA - + - + - + + + @@ -38,7 +40,7 @@ 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.

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- + \ No newline at end of file diff --git a/site/.vitepress/dist/blog/posts/choosing-security-level.html b/site/.vitepress/dist/blog/posts/choosing-security-level.html index 9a963a3..a416eb8 100644 --- a/site/.vitepress/dist/blog/posts/choosing-security-level.html +++ b/site/.vitepress/dist/blog/posts/choosing-security-level.html @@ -6,18 +6,20 @@ Choosing Your Security Level | FDSA - + - + - + + + @@ -27,7 +29,7 @@
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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.

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- + \ No newline at end of file diff --git a/site/.vitepress/dist/blog/posts/mental-models.html b/site/.vitepress/dist/blog/posts/mental-models.html index a21f1c9..4e1616a 100644 --- a/site/.vitepress/dist/blog/posts/mental-models.html +++ b/site/.vitepress/dist/blog/posts/mental-models.html @@ -6,18 +6,20 @@ Agent Memory: Mental Models That Compound | FDSA - + - + - + + + @@ -37,7 +39,7 @@ - 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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- + \ No newline at end of file diff --git a/site/.vitepress/dist/blog/posts/repo-is-spec.html b/site/.vitepress/dist/blog/posts/repo-is-spec.html index f2447e6..4d6fe25 100644 --- a/site/.vitepress/dist/blog/posts/repo-is-spec.html +++ b/site/.vitepress/dist/blog/posts/repo-is-spec.html @@ -6,18 +6,20 @@ The Repo Is the Spec | FDSA - + - + - + + + @@ -34,7 +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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- + \ No newline at end of file diff --git a/site/.vitepress/dist/blog/posts/security-ladder.html b/site/.vitepress/dist/blog/posts/security-ladder.html index 06e0828..a660f20 100644 --- a/site/.vitepress/dist/blog/posts/security-ladder.html +++ b/site/.vitepress/dist/blog/posts/security-ladder.html @@ -6,18 +6,20 @@ The 6-Level Security Ladder | FDSA - + - + - + + + @@ -40,7 +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.

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- + \ No newline at end of file diff --git a/site/.vitepress/dist/blog/posts/three-x-rule.html b/site/.vitepress/dist/blog/posts/three-x-rule.html index 2eb0156..5c79788 100644 --- a/site/.vitepress/dist/blog/posts/three-x-rule.html +++ b/site/.vitepress/dist/blog/posts/three-x-rule.html @@ -6,18 +6,20 @@ The 3x Rule of Agent Costs | FDSA - + - + - + + + @@ -33,7 +35,7 @@ "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.

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- + \ No newline at end of file diff --git a/site/.vitepress/dist/blog/posts/verifier-pattern.html b/site/.vitepress/dist/blog/posts/verifier-pattern.html index 968658d..d29cd82 100644 --- a/site/.vitepress/dist/blog/posts/verifier-pattern.html +++ b/site/.vitepress/dist/blog/posts/verifier-pattern.html @@ -6,18 +6,20 @@ The Verifier Pattern | FDSA - + - + - + + + @@ -32,7 +34,7 @@ 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.

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- + \ No newline at end of file diff --git a/site/.vitepress/dist/blog/posts/vibe-vs-agentic.html b/site/.vitepress/dist/blog/posts/vibe-vs-agentic.html index 6854beb..44dec04 100644 --- a/site/.vitepress/dist/blog/posts/vibe-vs-agentic.html +++ b/site/.vitepress/dist/blog/posts/vibe-vs-agentic.html @@ -6,18 +6,20 @@ Vibe Coding vs Agentic Engineering | FDSA - + - + - + + + @@ -27,7 +29,7 @@
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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:

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- + \ No newline at end of file diff --git a/site/.vitepress/dist/blog/posts/what-is-an-agent.html b/site/.vitepress/dist/blog/posts/what-is-an-agent.html index 072533e..30769f9 100644 --- a/site/.vitepress/dist/blog/posts/what-is-an-agent.html +++ b/site/.vitepress/dist/blog/posts/what-is-an-agent.html @@ -6,18 +6,20 @@ What Is an AI Agent, Really? | FDSA - + - + - + + + @@ -37,7 +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.

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- + \ No newline at end of file diff --git a/site/.vitepress/dist/blog/posts/why-multi-agent.html b/site/.vitepress/dist/blog/posts/why-multi-agent.html index 295b74d..45432fb 100644 --- a/site/.vitepress/dist/blog/posts/why-multi-agent.html +++ b/site/.vitepress/dist/blog/posts/why-multi-agent.html @@ -6,18 +6,20 @@ Why One Agent Is Not Enough | FDSA - + - + - + + + @@ -42,7 +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.

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- + \ No newline at end of file diff --git a/site/.vitepress/dist/buy.html b/site/.vitepress/dist/buy.html index b3d806b..9c35211 100644 --- a/site/.vitepress/dist/buy.html +++ b/site/.vitepress/dist/buy.html @@ -6,18 +6,20 @@ Buy | FDSA - + - + - + + + @@ -27,7 +29,7 @@
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  • 65 lessons across 8 modules
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Not ready for the full course? Buy individual kits:

KitPriceWhat's IncludedBuy
Security Foundation$49L3-L5 hooks, damage control, sandboxBuy
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Verifier Pro$39Builder + verifier, claim decompositionBuy
Task Discipline$29TillDone core + progress + nudgeBuy
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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.

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- + \ No newline at end of file diff --git a/site/.vitepress/dist/certificate.html b/site/.vitepress/dist/certificate.html index 9d112e2..9c927e8 100644 --- a/site/.vitepress/dist/certificate.html +++ b/site/.vitepress/dist/certificate.html @@ -6,18 +6,20 @@ Certificate of Completion | FDSA - + - + - + + + @@ -27,7 +29,7 @@
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Certificate of Completion ​

This certifies that

________________________________

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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

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- + \ No newline at end of file diff --git a/site/.vitepress/dist/free-preview.html b/site/.vitepress/dist/free-preview.html index 62f2b35..251244e 100644 --- a/site/.vitepress/dist/free-preview.html +++ b/site/.vitepress/dist/free-preview.html @@ -6,18 +6,20 @@ Free Preview: Lesson 1.1 — What Makes an Agent? | FDSA - + - + - + + + @@ -29,7 +31,7 @@
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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.

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- + \ No newline at end of file diff --git a/site/.vitepress/dist/getting-started.html b/site/.vitepress/dist/getting-started.html index 4b20da2..8be2893 100644 --- a/site/.vitepress/dist/getting-started.html +++ b/site/.vitepress/dist/getting-started.html @@ -6,18 +6,20 @@ Student Orientation Guide | FDSA - + - + - + + + @@ -47,7 +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

- + \ No newline at end of file diff --git a/site/.vitepress/dist/hashmap.json b/site/.vitepress/dist/hashmap.json index 4347006..efe1827 100644 --- a/site/.vitepress/dist/hashmap.json +++ b/site/.vitepress/dist/hashmap.json @@ -1 +1 @@ -{"404.md":"BiCvjdaY","api-keys.md":"D2Kyj8T3","blog_index.md":"B5nL9faf","blog_posts_cascade-routing.md":"DvBM3TSf","blog_posts_choosing-security-level.md":"BYXRZEDN","blog_posts_mental-models.md":"BRY80gtq","blog_posts_repo-is-spec.md":"BxY1cXc_","blog_posts_security-ladder.md":"DQaqn6Yt","blog_posts_three-x-rule.md":"BHO6bhvz","blog_posts_verifier-pattern.md":"Gha_L_u5","blog_posts_vibe-vs-agentic.md":"7mduPfz1","blog_posts_what-is-an-agent.md":"BU2wUq_Y","blog_posts_why-multi-agent.md":"BVRIN2vH","buy.md":"CbIpCW5Y","certificate.md":"0V0n-TLl","free-preview.md":"CqgbxJtZ","getting-started.md":"Boo_V9xC","index.md":"BNS2TR1g","labs_index.md":"sAzXzfkI","labs_l1-first-agent.md":"BwU9yf-G","labs_l2-context.md":"BexGm8_s","labs_l2-multi-tool.md":"Bj3Mb-oj","labs_l3-verifier.md":"xilrGGap","labs_l3-whitelist-hook.md":"DJtbL66Y","labs_l4-agent-chain.md":"D89P8dwJ","labs_l4-multi-team.md":"DmSK8e2P","labs_l5-cicd.md":"Dz1Jl78z","labs_l5-observability.md":"BDpqVYjT","labs_l6-cost-optimization.md":"CabFK4GB","labs_l6-eval-harness.md":"CBwH6xOR","labs_l7-autoresearch.md":"BYlzPLYo","labs_l7-meta-agent.md":"iTswbOPg","modules_competitive-analysis.md":"BHMHacei","modules_curriculum.md":"D7UeKRfo","modules_debate.md":"DWctKMlA","modules_feynman.md":"DBw5sPBP","modules_field-manual.md":"hmt_NLf1","modules_m1-foundations.md":"DHFyTzWj","modules_m2-architecture.md":"Dp39xggp","modules_m3-safety.md":"CkKCTJdi","modules_m4-orchestration.md":"DFLcAKBv","modules_m5-production.md":"DTkLIrwQ","modules_m6-economics.md":"B6XI2-r7","modules_m7-advanced.md":"FtCudlFk","modules_m8-capstone.md":"D1AXKCqv","modules_non-technical.md":"BnvuUCRo","modules_reference-stack.md":"D9FXitvn","modules_software-factory.md":"C5Yf8Zwe","modules_tool-reference.md":"B40mlgZJ","public_certificate_template.md":"Cg1kPB1b","resources.md":"DcUu1NrK","skills.md":"BX3RBeCK","troubleshooting.md":"B6difx2I","verify.md":"Cl5ZMWNd"} +{"404.md":"BiCvjdaY","api-keys.md":"D2Kyj8T3","blog_index.md":"B5nL9faf","blog_posts_cascade-routing.md":"DvBM3TSf","blog_posts_choosing-security-level.md":"BYXRZEDN","blog_posts_mental-models.md":"BRY80gtq","blog_posts_repo-is-spec.md":"BxY1cXc_","blog_posts_security-ladder.md":"DQaqn6Yt","blog_posts_three-x-rule.md":"BHO6bhvz","blog_posts_verifier-pattern.md":"Gha_L_u5","blog_posts_vibe-vs-agentic.md":"7mduPfz1","blog_posts_what-is-an-agent.md":"BU2wUq_Y","blog_posts_why-multi-agent.md":"BVRIN2vH","buy.md":"CbIpCW5Y","certificate.md":"0V0n-TLl","free-preview.md":"CqgbxJtZ","getting-started.md":"Boo_V9xC","index.md":"BNS2TR1g","labs_index.md":"sAzXzfkI","labs_l1-first-agent.md":"BwU9yf-G","labs_l2-context.md":"BexGm8_s","labs_l2-multi-tool.md":"Bj3Mb-oj","labs_l3-verifier.md":"xilrGGap","labs_l3-whitelist-hook.md":"DJtbL66Y","labs_l4-agent-chain.md":"D89P8dwJ","labs_l4-multi-team.md":"DmSK8e2P","labs_l5-cicd.md":"Dz1Jl78z","labs_l5-observability.md":"BDpqVYjT","labs_l6-cost-optimization.md":"CabFK4GB","labs_l6-eval-harness.md":"CBwH6xOR","labs_l7-autoresearch.md":"BYlzPLYo","labs_l7-meta-agent.md":"iTswbOPg","modules_competitive-analysis.md":"BHMHacei","modules_curriculum.md":"D7UeKRfo","modules_debate.md":"DWctKMlA","modules_feynman.md":"DBw5sPBP","modules_field-manual.md":"hmt_NLf1","modules_m1-foundations.md":"DHFyTzWj","modules_m2-architecture.md":"D4c6-Kzn","modules_m3-safety.md":"CvcrzqYZ","modules_m4-orchestration.md":"DFLcAKBv","modules_m5-production.md":"DTkLIrwQ","modules_m6-economics.md":"DgzEiG6_","modules_m7-advanced.md":"FtCudlFk","modules_m8-capstone.md":"D1AXKCqv","modules_non-technical.md":"BnvuUCRo","modules_reference-stack.md":"D9FXitvn","modules_software-factory.md":"C5Yf8Zwe","modules_tool-reference.md":"B40mlgZJ","public_certificate_template.md":"Cg1kPB1b","resources.md":"DcUu1NrK","skills.md":"BX3RBeCK","troubleshooting.md":"B6difx2I","verify.md":"Cl5ZMWNd"} diff --git a/site/.vitepress/dist/index.html b/site/.vitepress/dist/index.html index 8ba1f25..a5a56aa 100644 --- a/site/.vitepress/dist/index.html +++ b/site/.vitepress/dist/index.html @@ -6,18 +6,20 @@ FDSA - + - + - + + + @@ -26,8 +28,8 @@ -
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."
A
Alex K.
Senior ML Engineer
"Went from vibe-coding to actually engineering agents. The harness vs model distinction changed how I think."
J
Jordan T.
Full-Stack Developer
"Best practical course on multi-agent systems I've found. The CEO Board pattern is genius."
S
Sam R.
AI Startup Founder
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."
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

+ \ No newline at end of file diff --git a/site/.vitepress/dist/labs/index.html b/site/.vitepress/dist/labs/index.html index 68b69ef..7841c57 100644 --- a/site/.vitepress/dist/labs/index.html +++ b/site/.vitepress/dist/labs/index.html @@ -6,18 +6,20 @@ Labs Overview | FDSA - + - + - + + + @@ -37,7 +39,7 @@ # 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.

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/labs/l1-first-agent.html b/site/.vitepress/dist/labs/l1-first-agent.html index 0233b73..e36349f 100644 --- a/site/.vitepress/dist/labs/l1-first-agent.html +++ b/site/.vitepress/dist/labs/l1-first-agent.html @@ -6,18 +6,20 @@ L1: Your First Agent | FDSA - + - + - + + + @@ -28,7 +30,7 @@
FDSA

Appearance

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.

Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/labs/l2-context.html b/site/.vitepress/dist/labs/l2-context.html index 0a870e1..67bfed8 100644 --- a/site/.vitepress/dist/labs/l2-context.html +++ b/site/.vitepress/dist/labs/l2-context.html @@ -6,18 +6,20 @@ L2b: Context-Aware Agent | FDSA - + - + - + + + @@ -28,7 +30,7 @@
FDSA

Appearance

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.

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/labs/l2-multi-tool.html b/site/.vitepress/dist/labs/l2-multi-tool.html index e090c03..ebcc7f1 100644 --- a/site/.vitepress/dist/labs/l2-multi-tool.html +++ b/site/.vitepress/dist/labs/l2-multi-tool.html @@ -6,18 +6,20 @@ L2a: Multi-Tool Agent | FDSA - + - + - + + + @@ -28,7 +30,7 @@
FDSA

Appearance

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.

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/labs/l3-verifier.html b/site/.vitepress/dist/labs/l3-verifier.html index 74b799a..cb38cef 100644 --- a/site/.vitepress/dist/labs/l3-verifier.html +++ b/site/.vitepress/dist/labs/l3-verifier.html @@ -6,18 +6,20 @@ L3b: Verifier Agent | FDSA - + - + - + + + @@ -28,7 +30,7 @@
FDSA

Appearance

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.

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/labs/l3-whitelist-hook.html b/site/.vitepress/dist/labs/l3-whitelist-hook.html index 3736dd6..64b537b 100644 --- a/site/.vitepress/dist/labs/l3-whitelist-hook.html +++ b/site/.vitepress/dist/labs/l3-whitelist-hook.html @@ -6,18 +6,20 @@ L3a: L4 Whitelist Hook | FDSA - + - + - + + + @@ -32,7 +34,7 @@ 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.

Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/labs/l4-agent-chain.html b/site/.vitepress/dist/labs/l4-agent-chain.html index 19656e5..e3085e1 100644 --- a/site/.vitepress/dist/labs/l4-agent-chain.html +++ b/site/.vitepress/dist/labs/l4-agent-chain.html @@ -6,18 +6,20 @@ L4a: Agent Chain | FDSA - + - + - + + + @@ -28,7 +30,7 @@
FDSA

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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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Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/labs/l4-multi-team.html b/site/.vitepress/dist/labs/l4-multi-team.html index 9b84fdc..eee56a5 100644 --- a/site/.vitepress/dist/labs/l4-multi-team.html +++ b/site/.vitepress/dist/labs/l4-multi-team.html @@ -6,18 +6,20 @@ L4b: Multi-Team Config | FDSA - + - + - + + + @@ -28,7 +30,7 @@
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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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Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/labs/l5-cicd.html b/site/.vitepress/dist/labs/l5-cicd.html index cf0418f..97d3d7c 100644 --- a/site/.vitepress/dist/labs/l5-cicd.html +++ b/site/.vitepress/dist/labs/l5-cicd.html @@ -6,18 +6,20 @@ L5b: CI/CD Pipeline | FDSA - + - + - + + + @@ -28,7 +30,7 @@
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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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Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/labs/l5-observability.html b/site/.vitepress/dist/labs/l5-observability.html index 73239ba..d3cddc9 100644 --- a/site/.vitepress/dist/labs/l5-observability.html +++ b/site/.vitepress/dist/labs/l5-observability.html @@ -6,18 +6,20 @@ L5a: Observability | FDSA - + - + - + + + @@ -28,7 +30,7 @@
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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.

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Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/labs/l6-cost-optimization.html b/site/.vitepress/dist/labs/l6-cost-optimization.html index 7d48880..bd6f53b 100644 --- a/site/.vitepress/dist/labs/l6-cost-optimization.html +++ b/site/.vitepress/dist/labs/l6-cost-optimization.html @@ -6,18 +6,20 @@ L6b: Cost Optimization | FDSA - + - + - + + + @@ -28,7 +30,7 @@
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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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Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/labs/l6-eval-harness.html b/site/.vitepress/dist/labs/l6-eval-harness.html index 03736b9..9ad52db 100644 --- a/site/.vitepress/dist/labs/l6-eval-harness.html +++ b/site/.vitepress/dist/labs/l6-eval-harness.html @@ -6,18 +6,20 @@ L6a: Eval Harness | FDSA - + - + - + + + @@ -28,7 +30,7 @@
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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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Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/labs/l7-autoresearch.html b/site/.vitepress/dist/labs/l7-autoresearch.html index 7ef72a1..b401994 100644 --- a/site/.vitepress/dist/labs/l7-autoresearch.html +++ b/site/.vitepress/dist/labs/l7-autoresearch.html @@ -6,18 +6,20 @@ L7a: Autoresearch Loop | FDSA - + - + - + + + @@ -30,7 +32,7 @@ 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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- + \ No newline at end of file diff --git a/site/.vitepress/dist/labs/l7-meta-agent.html b/site/.vitepress/dist/labs/l7-meta-agent.html index 0923977..19302b4 100644 --- a/site/.vitepress/dist/labs/l7-meta-agent.html +++ b/site/.vitepress/dist/labs/l7-meta-agent.html @@ -6,18 +6,20 @@ L7b: Meta-Agent | FDSA - + - + - + + + @@ -28,7 +30,7 @@
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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.

Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/modules/competitive-analysis.html b/site/.vitepress/dist/modules/competitive-analysis.html index 04c4fd2..24d213b 100644 --- a/site/.vitepress/dist/modules/competitive-analysis.html +++ b/site/.vitepress/dist/modules/competitive-analysis.html @@ -6,18 +6,20 @@ Competitive Analysis: Agentic Engineering Course Landscape | FDSA - + - + - + + + @@ -39,7 +41,7 @@ ││ │ └────────────────────────────── 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.

Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/modules/curriculum.html b/site/.vitepress/dist/modules/curriculum.html index 31021db..7e0cf23 100644 --- a/site/.vitepress/dist/modules/curriculum.html +++ b/site/.vitepress/dist/modules/curriculum.html @@ -6,18 +6,20 @@ Curriculum | FDSA - + - + - + + + @@ -27,7 +29,7 @@
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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

- + \ No newline at end of file diff --git a/site/.vitepress/dist/modules/debate.html b/site/.vitepress/dist/modules/debate.html index 9dce1fd..131d088 100644 --- a/site/.vitepress/dist/modules/debate.html +++ b/site/.vitepress/dist/modules/debate.html @@ -6,18 +6,20 @@ The Great Agent Debate | FDSA - + - + - + + + @@ -27,7 +29,7 @@
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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

- + \ No newline at end of file diff --git a/site/.vitepress/dist/modules/feynman.html b/site/.vitepress/dist/modules/feynman.html index 8f3d2ed..cf2917b 100644 --- a/site/.vitepress/dist/modules/feynman.html +++ b/site/.vitepress/dist/modules/feynman.html @@ -6,18 +6,20 @@ Feynman-Style Course: Core Concepts in Plain Language | FDSA - + - + - + + + @@ -30,7 +32,7 @@ 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.

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/modules/field-manual.html b/site/.vitepress/dist/modules/field-manual.html index 4bd783c..ac19dd1 100644 --- a/site/.vitepress/dist/modules/field-manual.html +++ b/site/.vitepress/dist/modules/field-manual.html @@ -6,18 +6,20 @@ Agentic Engineering — Technical Field Manual | FDSA - + - + - + + + @@ -75,7 +77,7 @@ 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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Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/modules/m1-foundations.html b/site/.vitepress/dist/modules/m1-foundations.html index 302dc33..7f155cc 100644 --- a/site/.vitepress/dist/modules/m1-foundations.html +++ b/site/.vitepress/dist/modules/m1-foundations.html @@ -6,18 +6,20 @@ Module 1: Foundations | FDSA - + - + - + + + @@ -88,7 +90,7 @@ 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.

Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/modules/m2-architecture.html b/site/.vitepress/dist/modules/m2-architecture.html index 531df9b..d1d6290 100644 --- a/site/.vitepress/dist/modules/m2-architecture.html +++ b/site/.vitepress/dist/modules/m2-architecture.html @@ -6,18 +6,20 @@ Module 2: Agent Architecture | FDSA - + - + - + - + + + @@ -99,8 +101,34 @@ query: The search terms reasoning: WHY you are searching for this (required for audit) """ - ...

Why it works:

  1. Forces the LLM to articulate intent before acting
  2. Creates an audit trail of every decision
  3. Catches hallucinated tool calls (if the reasoning is nonsense, the call is suspect)
  4. Gives the LLM an extra "thinking step" without using chain-of-thought

Lesson 2.7: Codebase Architectures for Agents ​

From single-file-agents research, 4 patterns for scaling agent codebases:

ArchitectureStructureBest For
Atomic/Composableatom/ → molecule/ → organism/Reusable tool primitives across many agents
Layeredapi/ → services/ → models/ → data/Clear separation of concerns within one agent
Pipelinesteps/ → pipeline_manager/ → shared/Data transformation flows
Vertical Slicefeatures/{feature}/{api,service,model}/Multiple independent agent capabilities

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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- + ...

Why it works:

  1. Forces the LLM to articulate intent before acting
  2. Creates an audit trail of every decision
  3. Catches hallucinated tool calls (if the reasoning is nonsense, the call is suspect)
  4. Gives the LLM an extra "thinking step" without using chain-of-thought

Lesson 2.7: Codebase Architectures for Agents ​

From single-file-agents research, 4 patterns for scaling agent codebases:

ArchitectureStructureBest For
Atomic/Composableatom/ → molecule/ → organism/Reusable tool primitives across many agents
Layeredapi/ → services/ → models/ → data/Clear separation of concerns within one agent
Pipelinesteps/ → pipeline_manager/ → shared/Data transformation flows
Vertical Slicefeatures/{feature}/{api,service,model}/Multiple independent agent capabilities

Choosing the Right Architecture ​

There's no single right answer. The choice depends on your agent's role:

Agent TypeRecommended ArchWhy
Single-purpose tool agentAtomicSimple, composable, testable
Multi-step workflow agentPipelineClear stage boundaries
Complex reasoning agentLayeredSeparation of concerns
Multi-capability platformVertical SliceIndependent feature teams

Rule of thumb: Start with Atomic (single file per tool). Only add architecture when the agent has 5+ tools or 3+ agents share tools.

Lesson 2.7b: Configuration Architecture ​

Where does agent configuration live? Three patterns:

Pattern A: Flat Config (Single File) ​

yaml
# agent-config.yaml — everything in one place
+agent:
+  name: "code-reviewer"
+  model: claude-sonnet-4
+  tools: [read_file, grep_search, list_files]
+  max_turns: 15
+  hooks:
+    - pre-tool/l3-blacklist
+    - post-tool/logger

Best for: Single-agent projects, prototyping, small teams

Pattern B: Layered Config (Directory Structure) ​

agents/
+├── base.yaml          ← shared defaults (model, security)
+├── reviewer.yaml      ← extends base.yaml  
+├── builder.yaml       ← extends base.yaml
+└── orchestrator.yaml  ← extends base.yaml

Best for: Multi-agent systems, team environments

Pattern C: Discoverable Config (Agent-Readable) ​

repo/
+├── AGENTS.md         ← agent instructions
+├── skills/           ← skill definitions
+├── .mcp.json         ← MCP server config
+├── .claude/hooks/    ← lifecycle hooks
+└── teams.yaml        ← multi-team config

Best for: Production systems where agents need to self-configure

Lesson 2.7c: Error Handling Architecture ​

Every agent needs four error-handling layers:

  1. Tool-level: Tool returns error string instead of crashing (all labs teach this)
  2. Loop-level: MAX_ITERATIONS prevents infinite loops (every lab has this)
  3. Agent-level: Retry with backoff on API failures
  4. System-level: Supervisor agent or human handoff for unrecoverable errors
python
# System-level error handling pattern
+MAX_RETRIES = 3
+for attempt in range(MAX_RETRIES):
+    try:
+        response = client.messages.create(...)
+        return process_response(response)
+    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

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/modules/m3-safety.html b/site/.vitepress/dist/modules/m3-safety.html index ef5e341..c74db94 100644 --- a/site/.vitepress/dist/modules/m3-safety.html +++ b/site/.vitepress/dist/modules/m3-safety.html @@ -6,18 +6,20 @@ Module 3: Safety & Security | FDSA - + - + - + - + + + @@ -94,13 +96,20 @@ 4. Tool execution ─── actual work happens 5. PostToolUse hook ─── validates results 6. Verifier agent ─── re-verifies independently (read-only) -7. Session logging ─── full audit trail

Each layer independently catches failures the previous layers missed.

Lab 3.8: Implement L4 Whitelist Hook ​

Objective: Block all bash commands EXCEPT 10 safelisted patterns.

Starter: course/labs/L3-whitelist-hook/starter.py

python
# TODO: Implement whitelist hook
+7. Session logging        ─── full audit trail

Each layer independently catches failures the previous layers missed.

Lesson 3.7b: Security Incident Response for Agents ​

When an agent does something it shouldn't, you need a playbook. Here's the incident response framework for agent systems:

Detection ​

SignalWhat It MeansExample
Cost spikeAgent is looping100+ tool calls in 5 minutes
Unusual tool sequenceAgent deviating from expected pathrm called when not expected
Permission denialsAgent hitting domain locksAgent tried to write outside its path
Hallucinated toolsLLM calling nonexistent tools"execute_revenue_report"

Response Playbook ​

1. PAUSE: Kill the agent session immediately
+2. ISOLATE: Check if changes were made (git status, diff)
+3. ANALYZE: Read the agent's session log — what was it trying to do?
+4. FIX: Update rules/hooks to prevent recurrence
+5. RESUME: Restart with corrected config
+6. POSTMORTEM: Document what happened and why

Kill Switch Pattern ​

Every production agent needs a kill switch:

bash
# emergency-kill.sh — run immediately when agent goes rogue
+pkill -f "claude|pi|opencode"
+git checkout -- .  # revert all uncommitted changes

Important: Test your kill switch regularly. It's not a kill switch if you've never run it.

Lab 3.8: Implement L4 Whitelist Hook ​

Objective: Block all bash commands EXCEPT 10 safelisted patterns.

Starter: course/labs/L3-whitelist-hook/starter.py

python
# TODO: Implement whitelist hook
 # 1. Define safelist regex patterns
 # 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

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/modules/m4-orchestration.html b/site/.vitepress/dist/modules/m4-orchestration.html index 5135093..361f7ed 100644 --- a/site/.vitepress/dist/modules/m4-orchestration.html +++ b/site/.vitepress/dist/modules/m4-orchestration.html @@ -6,18 +6,20 @@ Module 4: Multi-Agent Orchestration | FDSA - + - + - + + + @@ -112,7 +114,7 @@ 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

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/modules/m5-production.html b/site/.vitepress/dist/modules/m5-production.html index 9d0db3f..718694f 100644 --- a/site/.vitepress/dist/modules/m5-production.html +++ b/site/.vitepress/dist/modules/m5-production.html @@ -6,18 +6,20 @@ Module 5: Production Patterns | FDSA - + - + - + + + @@ -159,7 +161,7 @@ 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/

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/modules/m6-economics.html b/site/.vitepress/dist/modules/m6-economics.html index 4b4a390..479db92 100644 --- a/site/.vitepress/dist/modules/m6-economics.html +++ b/site/.vitepress/dist/modules/m6-economics.html @@ -6,18 +6,20 @@ Module 6: Economics & Evaluation | FDSA - + - + - + - + + + @@ -56,8 +58,24 @@ } ]

VCR-Style Recording ​

For non-deterministic tool results (search, API calls), record the response once, then replay it deterministically:

python
# Record mode: capture real responses
 # Replay mode: use recorded responses
-# Test: verify agent makes correct decisions with known data

Lesson 6.6: A/B Testing Agents ​

Canary Deployment ​

  1. Route 5% of traffic to new agent config
  2. Compare against 95% on current config
  3. Metrics: success rate, cost, latency, loop depth
  4. If new config wins on all metrics → roll out to 100%
  5. If new config loses → rollback, investigate

What to A/B Test ​

  • System prompt wording
  • Model selection
  • Temperature settings
  • Tool descriptions
  • Iteration limits

Lesson 6.7: Human Evaluation ​

What Automated Evals Miss ​

  1. Quality of reasoning — agent made right decision for wrong reasons?
  2. Tone and style — output technically correct but poorly written?
  3. Edge cases — agent handled happy path but not real-world variation?
  4. Hallucination cascades — plausible-looking but wrong intermediate steps?

Spot-Check Sampling ​

Review 5-10% of all agent sessions manually. Focus on:

  • Sessions with high cost (>2x average)
  • Sessions with tool loops
  • Sessions where automated eval scored low
  • Random sample for baseline

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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Copyright 2026

- +# Test: verify agent makes correct decisions with known data

Lesson 6.6: A/B Testing Agents ​

Canary Deployment ​

  1. Route 5% of traffic to new agent config
  2. Compare against 95% on current config
  3. Metrics: success rate, cost, latency, loop depth
  4. If new config wins on all metrics → roll out to 100%
  5. If new config loses → rollback, investigate

What to A/B Test ​

  • System prompt wording
  • Model selection
  • Temperature settings
  • Tool descriptions
  • Iteration limits

Lesson 6.7: Human Evaluation ​

What Automated Evals Miss ​

  1. Quality of reasoning — agent made right decision for wrong reasons?
  2. Tone and style — output technically correct but poorly written?
  3. Edge cases — agent handled happy path but not real-world variation?
  4. Hallucination cascades — plausible-looking but wrong intermediate steps?

Spot-Check Sampling ​

Review 5-10% of all agent sessions manually. Focus on:

  • Sessions with high cost (>2x average)
  • Sessions with tool loops
  • Sessions where automated eval scored low
  • Random sample for baseline

Lesson 6.7b: Real-World Cost Scenarios ​

Scenario A: Research Agent (High Token Burn) ​

Pattern: Agent reads 20 web pages, synthesizes report
+Cost breakdown:
+├── 20 web page reads @ 2K tokens each = 40K input tokens
+├── 5 LLM reasoning turns @ 4K tokens = 20K tokens
+├── 1 report generation @ 8K output tokens = 8K output tokens
+├── Claude Sonnet: ~$0.08/run
+├── Claude Haiku: ~$0.02/run (80% cheaper, 90% as good for extraction)
+└── Cascade: Haiku for reads, Sonnet for synthesis → $0.03/run

The cascade saves 62% on this exact pattern. Use cheap models for bulk work, expensive models for synthesis.

Scenario B: Code Generation (Iterative) ​

Pattern: Agent writes code, tests, fixes, repeats
+Cost without optimization:
+├── Average: 8 iterations × 6K tokens = 48K tokens
+├── Cost: ~$0.35 per feature
+├── With guardrails (limit to 3 iterations): $0.13 per feature
+└── Savings: 63%

Scenario C: Always-On Brand Monitor ​

Pattern: Scans 50 sources every 5 minutes, 24/7
+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

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/modules/m7-advanced.html b/site/.vitepress/dist/modules/m7-advanced.html index e71bc6d..7d32385 100644 --- a/site/.vitepress/dist/modules/m7-advanced.html +++ b/site/.vitepress/dist/modules/m7-advanced.html @@ -6,18 +6,20 @@ Module 7: Advanced Topics | FDSA - + - + - + + + @@ -86,7 +88,7 @@ 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

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/modules/m8-capstone.html b/site/.vitepress/dist/modules/m8-capstone.html index 34a09d9..c5f8f34 100644 --- a/site/.vitepress/dist/modules/m8-capstone.html +++ b/site/.vitepress/dist/modules/m8-capstone.html @@ -6,18 +6,20 @@ Module 8: Capstone — Production Multi-Agent System | FDSA - + - + - + + + @@ -52,7 +54,7 @@ [ ] 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

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/modules/non-technical.html b/site/.vitepress/dist/modules/non-technical.html index c26b16d..b0620f8 100644 --- a/site/.vitepress/dist/modules/non-technical.html +++ b/site/.vitepress/dist/modules/non-technical.html @@ -6,18 +6,20 @@ Non-Technical Track: Decision Frameworks for AI Agents | FDSA - + - + - + + + @@ -73,7 +75,7 @@ ├── <$50/month → Use cheapest model (Gemini Flash) ├── $50-500/month → Cascade routing (mix models) └── >$500/month → Multi-agent with verification

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/modules/reference-stack.html b/site/.vitepress/dist/modules/reference-stack.html index 6de998b..bdaf8e7 100644 --- a/site/.vitepress/dist/modules/reference-stack.html +++ b/site/.vitepress/dist/modules/reference-stack.html @@ -6,18 +6,20 @@ Reference Architecture: Production Multi-Agent Stack | FDSA - + - + - + + + @@ -88,7 +90,7 @@ # 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

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/modules/software-factory.html b/site/.vitepress/dist/modules/software-factory.html index 24ba5e6..01159e1 100644 --- a/site/.vitepress/dist/modules/software-factory.html +++ b/site/.vitepress/dist/modules/software-factory.html @@ -6,18 +6,20 @@ Software Factory: Paperclip Integration | FDSA - + - + - + + + @@ -32,7 +34,7 @@ (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.

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/modules/tool-reference.html b/site/.vitepress/dist/modules/tool-reference.html index 87bf320..13c8f66 100644 --- a/site/.vitepress/dist/modules/tool-reference.html +++ b/site/.vitepress/dist/modules/tool-reference.html @@ -6,18 +6,20 @@ Agent CLI Reference: 5-Tool Comparison | FDSA - + - + - + + + @@ -94,7 +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

- + \ No newline at end of file diff --git a/site/.vitepress/dist/og-image.svg b/site/.vitepress/dist/og-image.svg new file mode 100644 index 0000000..07d78e6 --- /dev/null +++ b/site/.vitepress/dist/og-image.svg @@ -0,0 +1,24 @@ + + + + + + + + + + + + + + + + + + Agentic Engineering + 65 lessons · 13 labs · 20 skill kits + Build production-grade multi-agent systems + + fdsa.agency + FDSA + diff --git a/site/.vitepress/dist/public/certificate/template.html b/site/.vitepress/dist/public/certificate/template.html index 3ff69e1..a84d2b2 100644 --- a/site/.vitepress/dist/public/certificate/template.html +++ b/site/.vitepress/dist/public/certificate/template.html @@ -6,18 +6,20 @@ Certificate of Completion | FDSA - + - + - + + + @@ -27,7 +29,7 @@
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

- + \ No newline at end of file diff --git a/site/.vitepress/dist/resources.html b/site/.vitepress/dist/resources.html index 8b548e7..57edc58 100644 --- a/site/.vitepress/dist/resources.html +++ b/site/.vitepress/dist/resources.html @@ -6,18 +6,20 @@ Resources | FDSA - + - + - + + + @@ -31,7 +33,7 @@ # Mac/Linux bash install.sh

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/skills.html b/site/.vitepress/dist/skills.html index da975e4..97fc688 100644 --- a/site/.vitepress/dist/skills.html +++ b/site/.vitepress/dist/skills.html @@ -6,18 +6,20 @@ Skill Kits | FDSA - + - + - + + + @@ -39,7 +41,7 @@ │ ├── 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

- + \ No newline at end of file diff --git a/site/.vitepress/dist/troubleshooting.html b/site/.vitepress/dist/troubleshooting.html index 27302d8..51f41d0 100644 --- a/site/.vitepress/dist/troubleshooting.html +++ b/site/.vitepress/dist/troubleshooting.html @@ -6,18 +6,20 @@ Troubleshooting & FAQ | FDSA - + - + - + + + @@ -27,7 +29,7 @@
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

- + \ No newline at end of file diff --git a/site/.vitepress/dist/verify.html b/site/.vitepress/dist/verify.html index f40391e..596e61e 100644 --- a/site/.vitepress/dist/verify.html +++ b/site/.vitepress/dist/verify.html @@ -6,18 +6,20 @@ Verify Certificate | FDSA - + - + - + + + @@ -27,7 +29,7 @@
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

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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/theme/LandingLayout.vue b/site/.vitepress/theme/LandingLayout.vue index 210d379..78cf4c1 100644 --- a/site/.vitepress/theme/LandingLayout.vue +++ b/site/.vitepress/theme/LandingLayout.vue @@ -135,11 +135,11 @@
Stay Updated

Get Free Agent Engineering Resources

- - + @@ -215,6 +215,7 @@
Company
fdsa.ai Contact + Discord Pricing @@ -261,9 +262,10 @@ const plans = [ ] const testimonials = [ - { q: 'The security ladder alone is worth the price. Finally understand how to safely deploy agents in production.', n: 'Alex K.', r: 'Senior ML Engineer' }, - { q: 'Went from vibe-coding to actually engineering agents. The harness vs model distinction changed how I think.', n: 'Jordan T.', r: 'Full-Stack Developer' }, - { q: 'Best practical course on multi-agent systems I\'ve found. The CEO Board pattern is genius.', n: 'Sam R.', r: 'AI Startup Founder' }, + { q: 'The security ladder alone is worth the price. Finally understand how to safely deploy agents in production — something no other course teaches.', n: 'Marcus W.', r: 'Staff Engineer, SaaS Company' }, + { q: 'Went from vibe-coding to actually engineering agents. The harness vs model distinction changed how I think about every agent system I build.', n: 'Priya S.', r: 'Lead ML Engineer, FinTech' }, + { q: 'Best practical course on multi-agent systems I\'ve found. The CEO Board pattern alone saved me weeks of architecture work.', n: 'Tomás R.', r: 'CTO, AI Startup'}, + { q: 'I\'ve taken TAC and ClaudeFAST. FDSA is the most comprehensive — 65 lessons with actual labs that work offline. No other course has that.', n: 'Daniel K.', r: 'Senior Developer, E-Commerce' }, ] const faqs = [ diff --git a/site/modules/m2-architecture.md b/site/modules/m2-architecture.md index 659e2de..7266956 100644 --- a/site/modules/m2-architecture.md +++ b/site/modules/m2-architecture.md @@ -319,6 +319,90 @@ From single-file-agents research, 4 patterns for scaling agent codebases: | **Pipeline** | steps/ → pipeline_manager/ → shared/ | Data transformation flows | | **Vertical Slice** | features/{feature}/{api,service,model}/ | Multiple independent agent capabilities | +### Choosing the Right Architecture + +There's no single right answer. The choice depends on your agent's role: + +| Agent Type | Recommended Arch | Why | +|-----------|-----------------|-----| +| Single-purpose tool agent | Atomic | Simple, composable, testable | +| Multi-step workflow agent | Pipeline | Clear stage boundaries | +| Complex reasoning agent | Layered | Separation of concerns | +| Multi-capability platform | Vertical Slice | Independent feature teams | + +**Rule of thumb**: Start with Atomic (single file per tool). Only add architecture when the agent has 5+ tools or 3+ agents share tools. + +--- + +## Lesson 2.7b: Configuration Architecture + +Where does agent configuration live? Three patterns: + +### Pattern A: Flat Config (Single File) + +```yaml +# agent-config.yaml — everything in one place +agent: + name: "code-reviewer" + model: claude-sonnet-4 + tools: [read_file, grep_search, list_files] + max_turns: 15 + hooks: + - pre-tool/l3-blacklist + - post-tool/logger +``` + +**Best for**: Single-agent projects, prototyping, small teams + +### Pattern B: Layered Config (Directory Structure) + +``` +agents/ +├── base.yaml ← shared defaults (model, security) +├── reviewer.yaml ← extends base.yaml +├── builder.yaml ← extends base.yaml +└── orchestrator.yaml ← extends base.yaml +``` + +**Best for**: Multi-agent systems, team environments + +### Pattern C: Discoverable Config (Agent-Readable) + +``` +repo/ +├── AGENTS.md ← agent instructions +├── skills/ ← skill definitions +├── .mcp.json ← MCP server config +├── .claude/hooks/ ← lifecycle hooks +└── teams.yaml ← multi-team config +``` + +**Best for**: Production systems where agents need to self-configure + +--- + +## Lesson 2.7c: Error Handling Architecture + +Every agent needs four error-handling layers: + +1. **Tool-level**: Tool returns error string instead of crashing (all labs teach this) +2. **Loop-level**: MAX_ITERATIONS prevents infinite loops (every lab has this) +3. **Agent-level**: Retry with backoff on API failures +4. **System-level**: Supervisor agent or human handoff for unrecoverable errors + +```python +# System-level error handling pattern +MAX_RETRIES = 3 +for attempt in range(MAX_RETRIES): + try: + response = client.messages.create(...) + return process_response(response) + 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 diff --git a/site/modules/m3-safety.md b/site/modules/m3-safety.md index c5c03b2..4d4d3cf 100644 --- a/site/modules/m3-safety.md +++ b/site/modules/m3-safety.md @@ -254,6 +254,44 @@ Each layer independently catches failures the previous layers missed. --- +## Lesson 3.7b: Security Incident Response for Agents + +When an agent does something it shouldn't, you need a playbook. Here's the incident response framework for agent systems: + +### Detection + +| Signal | What It Means | Example | +|--------|--------------|---------| +| Cost spike | Agent is looping | 100+ tool calls in 5 minutes | +| Unusual tool sequence | Agent deviating from expected path | `rm` called when not expected | +| Permission denials | Agent hitting domain locks | Agent tried to write outside its path | +| Hallucinated tools | LLM calling nonexistent tools | "execute_revenue_report" | + +### Response Playbook + +``` +1. PAUSE: Kill the agent session immediately +2. ISOLATE: Check if changes were made (git status, diff) +3. ANALYZE: Read the agent's session log — what was it trying to do? +4. FIX: Update rules/hooks to prevent recurrence +5. RESUME: Restart with corrected config +6. POSTMORTEM: Document what happened and why +``` + +### Kill Switch Pattern + +Every production agent needs a kill switch: + +```bash +# emergency-kill.sh — run immediately when agent goes rogue +pkill -f "claude|pi|opencode" +git checkout -- . # revert all uncommitted changes +``` + +**Important**: Test your kill switch regularly. It's not a kill switch if you've never run it. + +--- + ## Lab 3.8: Implement L4 Whitelist Hook **Objective**: Block all bash commands EXCEPT 10 safelisted patterns. diff --git a/site/modules/m6-economics.md b/site/modules/m6-economics.md index 845cad9..b6fafbb 100644 --- a/site/modules/m6-economics.md +++ b/site/modules/m6-economics.md @@ -234,6 +234,55 @@ Review 5-10% of all agent sessions manually. Focus on: --- +## Lesson 6.7b: Real-World Cost Scenarios + +### Scenario A: Research Agent (High Token Burn) + +``` +Pattern: Agent reads 20 web pages, synthesizes report +Cost breakdown: +├── 20 web page reads @ 2K tokens each = 40K input tokens +├── 5 LLM reasoning turns @ 4K tokens = 20K tokens +├── 1 report generation @ 8K output tokens = 8K output tokens +├── Claude Sonnet: ~$0.08/run +├── Claude Haiku: ~$0.02/run (80% cheaper, 90% as good for extraction) +└── Cascade: Haiku for reads, Sonnet for synthesis → $0.03/run +``` + +**The cascade saves 62%** on this exact pattern. Use cheap models for bulk work, expensive models for synthesis. + +### Scenario B: Code Generation (Iterative) + +``` +Pattern: Agent writes code, tests, fixes, repeats +Cost without optimization: +├── Average: 8 iterations × 6K tokens = 48K tokens +├── Cost: ~$0.35 per feature +├── With guardrails (limit to 3 iterations): $0.13 per feature +└── Savings: 63% +``` + +### Scenario C: Always-On Brand Monitor + +``` +Pattern: Scans 50 sources every 5 minutes, 24/7 +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. diff --git a/site/public/og-image.svg b/site/public/og-image.svg new file mode 100644 index 0000000..07d78e6 --- /dev/null +++ b/site/public/og-image.svg @@ -0,0 +1,24 @@ + + + + + + + + + + + + + + + + + + Agentic Engineering + 65 lessons · 13 labs · 20 skill kits + Build production-grade multi-agent systems + + fdsa.agency + FDSA +