diff --git a/site/.vitepress/dist/404.html b/site/.vitepress/dist/404.html index 38b38d0..7a2d296 100644 --- a/site/.vitepress/dist/404.html +++ b/site/.vitepress/dist/404.html @@ -6,12 +6,12 @@ Page Not Found | FDSA - + - + - + @@ -27,7 +27,7 @@
- + \ No newline at end of file diff --git a/site/.vitepress/dist/api-keys.html b/site/.vitepress/dist/api-keys.html index fc61421..474e130 100644 --- a/site/.vitepress/dist/api-keys.html +++ b/site/.vitepress/dist/api-keys.html @@ -6,14 +6,14 @@ API Key Setup Guide | FDSA - + - + - + - + @@ -37,8 +37,8 @@ python -c "from openai import OpenAI; c=OpenAI(); print(c.chat.completions.create(model='gpt-4o', messages=[{'role':'user','content':'hi'}]).choices[0].message.content)" # 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.

- +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.

+ \ No newline at end of file diff --git a/site/.vitepress/dist/assets/api-keys.md.D_HM8x3I.js b/site/.vitepress/dist/assets/api-keys.md.D2Kyj8T3.js similarity index 99% rename from site/.vitepress/dist/assets/api-keys.md.D_HM8x3I.js rename to site/.vitepress/dist/assets/api-keys.md.D2Kyj8T3.js index 9b9ae4f..46e0fba 100644 --- a/site/.vitepress/dist/assets/api-keys.md.D_HM8x3I.js +++ b/site/.vitepress/dist/assets/api-keys.md.D2Kyj8T3.js @@ -1,4 +1,4 @@ -import{c as t,Q as s,j as a,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"API Key Setup Guide","description":"","frontmatter":{},"headers":[],"relativePath":"api-keys.md","filePath":"api-keys.md","lastUpdated":1780488246000}'),n={name:"api-keys.md"};function r(o,e,h,l,p,d){return s(),a("div",null,[...e[0]||(e[0]=[i(`

API Key Setup Guide ​

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

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

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

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

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

Provider Keys ​

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

bash
# Windows PowerShell
+import{c as t,Q as s,j as a,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"API Key Setup Guide","description":"","frontmatter":{},"headers":[],"relativePath":"api-keys.md","filePath":"api-keys.md","lastUpdated":1780488472000}'),n={name:"api-keys.md"};function r(o,e,h,l,p,d){return s(),a("div",null,[...e[0]||(e[0]=[i(`
API Key Setup Guide ​
Every lab needs at least one LLM API key. If you don't have one, the mock LLM handles everything offline.
Quick Start: Use the Mock LLM (No Key Needed) ​
All labs include automatic mock LLM fallback. Run without any setup:
bash
python labs/L1-first-agent/solution.py test.txt "Summarize this file"
The mock client returns realistic responses. Your code runs identically with real or mock LLM.
Provider Keys ​
Best for: All course labs. Claude Sonnet is the default model used throughout.
Cost: Free credits available on signup. Labs cost ~$0.01-0.05 each with Sonnet.
Get key: console.anthropic.com
Set it:
bash
# Windows PowerShell
 $env:ANTHROPIC_API_KEY="sk-ant-..."
 
 # Mac/Linux
diff --git a/site/.vitepress/dist/assets/api-keys.md.D_HM8x3I.lean.js b/site/.vitepress/dist/assets/api-keys.md.D2Kyj8T3.lean.js
similarity index 84%
rename from site/.vitepress/dist/assets/api-keys.md.D_HM8x3I.lean.js
rename to site/.vitepress/dist/assets/api-keys.md.D2Kyj8T3.lean.js
index c0811de..043495b 100644
--- a/site/.vitepress/dist/assets/api-keys.md.D_HM8x3I.lean.js
+++ b/site/.vitepress/dist/assets/api-keys.md.D2Kyj8T3.lean.js
@@ -1 +1 @@
-import{c as t,Q as s,j as a,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"API Key Setup Guide","description":"","frontmatter":{},"headers":[],"relativePath":"api-keys.md","filePath":"api-keys.md","lastUpdated":1780488246000}'),n={name:"api-keys.md"};function r(o,e,h,l,p,d){return s(),a("div",null,[...e[0]||(e[0]=[i("",31)])])}const g=t(n,[["render",r]]);export{c as __pageData,g as default};
+import{c as t,Q as s,j as a,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"API Key Setup Guide","description":"","frontmatter":{},"headers":[],"relativePath":"api-keys.md","filePath":"api-keys.md","lastUpdated":1780488472000}'),n={name:"api-keys.md"};function r(o,e,h,l,p,d){return s(),a("div",null,[...e[0]||(e[0]=[i("",31)])])}const g=t(n,[["render",r]]);export{c as __pageData,g as default};
diff --git a/site/.vitepress/dist/assets/app.DUyb4j3N.js b/site/.vitepress/dist/assets/app.B6Ra8B8A.js
similarity index 95%
rename from site/.vitepress/dist/assets/app.DUyb4j3N.js
rename to site/.vitepress/dist/assets/app.B6Ra8B8A.js
index ab72662..a4f6536 100644
--- a/site/.vitepress/dist/assets/app.DUyb4j3N.js
+++ b/site/.vitepress/dist/assets/app.B6Ra8B8A.js
@@ -1 +1 @@
-import{R as p}from"./chunks/theme.s_1YR_Jm.js";import{v as s,am as i,R as u,w as c,p as l,a as f,C as d,a1 as m,k as h,S as g,l as A,r as v,ac as w,N as C,as as R,ag as y,ab as P,aa as b,u as S}from"./chunks/framework.BPKcPtvA.js";function r(e){if(e.extends){const a=r(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const n=r(p),E=v({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=w();return C(()=>{R(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&y(),P(),b(),n.setup&&n.setup(),()=>S(n.Layout)}});async function T(){globalThis.__VITEPRESS__=!0;const e=_(),a=D();a.provide(u,e);const t=c(e.route);return a.provide(l,t),a.component("Content",f),a.component("ClientOnly",d),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),n.enhanceApp&&await n.enhanceApp({app:a,router:e,siteData:m}),{app:a,router:e,data:t}}function D(){return A(E)}function _(){let e=s;return h(a=>{let t=g(a),o=null;return t&&(e&&(t=t.replace(/\.js$/,".lean.js")),o=import(t)),s&&(e=!1),o},n.NotFound)}s&&T().then(({app:e,router:a,data:t})=>{a.go().then(()=>{i(a.route,t.site),e.mount("#app")})});export{T as createApp};
+import{R as p}from"./chunks/theme.DE1FzPl-.js";import{v as s,am as i,R as u,w as c,p as l,a as f,C as d,a1 as m,k as h,S as g,l as A,r as v,ac as w,N as C,as as R,ag as y,ab as P,aa as b,u as S}from"./chunks/framework.BPKcPtvA.js";function r(e){if(e.extends){const a=r(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const n=r(p),E=v({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=w();return C(()=>{R(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&y(),P(),b(),n.setup&&n.setup(),()=>S(n.Layout)}});async function T(){globalThis.__VITEPRESS__=!0;const e=_(),a=D();a.provide(u,e);const t=c(e.route);return a.provide(l,t),a.component("Content",f),a.component("ClientOnly",d),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),n.enhanceApp&&await n.enhanceApp({app:a,router:e,siteData:m}),{app:a,router:e,data:t}}function D(){return A(E)}function _(){let e=s;return h(a=>{let t=g(a),o=null;return t&&(e&&(t=t.replace(/\.js$/,".lean.js")),o=import(t)),s&&(e=!1),o},n.NotFound)}s&&T().then(({app:e,router:a,data:t})=>{a.go().then(()=>{i(a.route,t.site),e.mount("#app")})});export{T as createApp};
diff --git a/site/.vitepress/dist/assets/blog_index.md.DpQ5yx1K.js b/site/.vitepress/dist/assets/blog_index.md.B5nL9faf.js
similarity index 98%
rename from site/.vitepress/dist/assets/blog_index.md.DpQ5yx1K.js
rename to site/.vitepress/dist/assets/blog_index.md.B5nL9faf.js
index 0e4aebe..154b0c9 100644
--- a/site/.vitepress/dist/assets/blog_index.md.DpQ5yx1K.js
+++ b/site/.vitepress/dist/assets/blog_index.md.B5nL9faf.js
@@ -1 +1 @@
-import{c as a,Q as t,j as o,m as n}from"./chunks/framework.BPKcPtvA.js";const d=JSON.parse('{"title":"Blog","description":"","frontmatter":{},"headers":[],"relativePath":"blog/index.md","filePath":"blog/index.md","lastUpdated":1780488246000}'),s={name:"blog/index.md"};function r(i,e,l,h,u,c){return t(),o("div",null,[...e[0]||(e[0]=[n('
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.
',25)])])}const p=a(s,[["render",r]]);export{d as __pageData,p as default};
+import{c as a,Q as t,j as o,m as n}from"./chunks/framework.BPKcPtvA.js";const d=JSON.parse('{"title":"Blog","description":"","frontmatter":{},"headers":[],"relativePath":"blog/index.md","filePath":"blog/index.md","lastUpdated":1780488472000}'),s={name:"blog/index.md"};function r(i,e,l,h,u,c){return t(),o("div",null,[...e[0]||(e[0]=[n('
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.
',25)])])}const p=a(s,[["render",r]]);export{d as __pageData,p as default};
diff --git a/site/.vitepress/dist/assets/blog_index.md.DpQ5yx1K.lean.js b/site/.vitepress/dist/assets/blog_index.md.B5nL9faf.lean.js
similarity index 84%
rename from site/.vitepress/dist/assets/blog_index.md.DpQ5yx1K.lean.js
rename to site/.vitepress/dist/assets/blog_index.md.B5nL9faf.lean.js
index 18b8e1f..4bae901 100644
--- a/site/.vitepress/dist/assets/blog_index.md.DpQ5yx1K.lean.js
+++ b/site/.vitepress/dist/assets/blog_index.md.B5nL9faf.lean.js
@@ -1 +1 @@
-import{c as a,Q as t,j as o,m as n}from"./chunks/framework.BPKcPtvA.js";const d=JSON.parse('{"title":"Blog","description":"","frontmatter":{},"headers":[],"relativePath":"blog/index.md","filePath":"blog/index.md","lastUpdated":1780488246000}'),s={name:"blog/index.md"};function r(i,e,l,h,u,c){return t(),o("div",null,[...e[0]||(e[0]=[n("",25)])])}const p=a(s,[["render",r]]);export{d as __pageData,p as default};
+import{c as a,Q as t,j as o,m as n}from"./chunks/framework.BPKcPtvA.js";const d=JSON.parse('{"title":"Blog","description":"","frontmatter":{},"headers":[],"relativePath":"blog/index.md","filePath":"blog/index.md","lastUpdated":1780488472000}'),s={name:"blog/index.md"};function r(i,e,l,h,u,c){return t(),o("div",null,[...e[0]||(e[0]=[n("",25)])])}const p=a(s,[["render",r]]);export{d as __pageData,p as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_cascade-routing.md.BF7ufKzI.js b/site/.vitepress/dist/assets/blog_posts_cascade-routing.md.DvBM3TSf.js
similarity index 99%
rename from site/.vitepress/dist/assets/blog_posts_cascade-routing.md.BF7ufKzI.js
rename to site/.vitepress/dist/assets/blog_posts_cascade-routing.md.DvBM3TSf.js
index ea52fe0..9934079 100644
--- a/site/.vitepress/dist/assets/blog_posts_cascade-routing.md.BF7ufKzI.js
+++ b/site/.vitepress/dist/assets/blog_posts_cascade-routing.md.DvBM3TSf.js
@@ -1,4 +1,4 @@
-import{c as t,Q as a,j as i,m as e}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Cascade Routing: Cut Your API Costs by 66%","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/cascade-routing.md","filePath":"blog/posts/cascade-routing.md","lastUpdated":1780488246000}'),n={name:"blog/posts/cascade-routing.md"};function l(h,s,p,d,o,r){return a(),i("div",null,[...s[0]||(s[0]=[e(`
Cascade Routing: Cut Your API Costs by 66% ​
June 11, 2026
Most teams use one model for everything. They default to Claude Opus or GPT-5 for every task, which means they are paying premium prices for simple work.
The Price Range ​
ModelInput ($/M)Output ($/M)
Gemini 2.5 Flash$0.15$0.60
DeepSeek V3$0.27$1.10
Claude Sonnet 4$3.00$15.00
Claude Opus 4$15.00$75.00
That is a 100x range between the cheapest and most expensive.
The Cascade Pattern ​
Route different steps to different models. Use cheap models for simple retrieval and formatting. Use expensive models only for complex reasoning.
Retrieve context -> Gemini Flash ($0.15/$0.60)
+import{c as t,Q as a,j as i,m as e}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Cascade Routing: Cut Your API Costs by 66%","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/cascade-routing.md","filePath":"blog/posts/cascade-routing.md","lastUpdated":1780488472000}'),n={name:"blog/posts/cascade-routing.md"};function l(h,s,p,d,o,r){return a(),i("div",null,[...s[0]||(s[0]=[e(`
Cascade Routing: Cut Your API Costs by 66% ​
June 11, 2026
Most teams use one model for everything. They default to Claude Opus or GPT-5 for every task, which means they are paying premium prices for simple work.
The Price Range ​
ModelInput ($/M)Output ($/M)
Gemini 2.5 Flash$0.15$0.60
DeepSeek V3$0.27$1.10
Claude Sonnet 4$3.00$15.00
Claude Opus 4$15.00$75.00
That is a 100x range between the cheapest and most expensive.
The Cascade Pattern ​
Route different steps to different models. Use cheap models for simple retrieval and formatting. Use expensive models only for complex reasoning.
Retrieve context -> Gemini Flash ($0.15/$0.60)
 Analyze data     -> Claude Sonnet ($3/$15)
 Make decision    -> Claude Opus ($15/$75)
 Format output    -> Gemini Flash ($0.15/$0.60)
The Savings ​
PatternCost/TaskSavings
All Opus$2.50Baseline
Cascade$0.8566%
All Sonnet$0.5080% (but quality loss on complex steps)
Implementation ​
python
def route_task(task_complexity: str) -> str:
diff --git a/site/.vitepress/dist/assets/blog_posts_cascade-routing.md.BF7ufKzI.lean.js b/site/.vitepress/dist/assets/blog_posts_cascade-routing.md.DvBM3TSf.lean.js
similarity index 87%
rename from site/.vitepress/dist/assets/blog_posts_cascade-routing.md.BF7ufKzI.lean.js
rename to site/.vitepress/dist/assets/blog_posts_cascade-routing.md.DvBM3TSf.lean.js
index 11d635e..6293305 100644
--- a/site/.vitepress/dist/assets/blog_posts_cascade-routing.md.BF7ufKzI.lean.js
+++ b/site/.vitepress/dist/assets/blog_posts_cascade-routing.md.DvBM3TSf.lean.js
@@ -1 +1 @@
-import{c as t,Q as a,j as i,m as e}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Cascade Routing: Cut Your API Costs by 66%","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/cascade-routing.md","filePath":"blog/posts/cascade-routing.md","lastUpdated":1780488246000}'),n={name:"blog/posts/cascade-routing.md"};function l(h,s,p,d,o,r){return a(),i("div",null,[...s[0]||(s[0]=[e("",17)])])}const u=t(n,[["render",l]]);export{c as __pageData,u as default};
+import{c as t,Q as a,j as i,m as e}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Cascade Routing: Cut Your API Costs by 66%","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/cascade-routing.md","filePath":"blog/posts/cascade-routing.md","lastUpdated":1780488472000}'),n={name:"blog/posts/cascade-routing.md"};function l(h,s,p,d,o,r){return a(),i("div",null,[...s[0]||(s[0]=[e("",17)])])}const u=t(n,[["render",l]]);export{c as __pageData,u as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BibZder6.js b/site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BYXRZEDN.js
similarity index 98%
rename from site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BibZder6.js
rename to site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BYXRZEDN.js
index 43dee93..77a98bc 100644
--- a/site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BibZder6.js
+++ b/site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BYXRZEDN.js
@@ -1 +1 @@
-import{c as t,Q as a,j as o,m as r}from"./chunks/framework.BPKcPtvA.js";const p=JSON.parse('{"title":"Choosing Your Security Level","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/choosing-security-level.md","filePath":"blog/posts/choosing-security-level.md","lastUpdated":1780488246000}'),n={name:"blog/posts/choosing-security-level.md"};function l(s,e,i,d,h,c){return a(),o("div",null,[...e[0]||(e[0]=[r('
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.
',18)])])}const m=t(n,[["render",l]]);export{p as __pageData,m as default};
+import{c as t,Q as a,j as o,m as r}from"./chunks/framework.BPKcPtvA.js";const p=JSON.parse('{"title":"Choosing Your Security Level","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/choosing-security-level.md","filePath":"blog/posts/choosing-security-level.md","lastUpdated":1780488472000}'),n={name:"blog/posts/choosing-security-level.md"};function l(s,e,i,d,h,c){return a(),o("div",null,[...e[0]||(e[0]=[r('
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.
',18)])])}const m=t(n,[["render",l]]);export{p as __pageData,m as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BibZder6.lean.js b/site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BYXRZEDN.lean.js
similarity index 87%
rename from site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BibZder6.lean.js
rename to site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BYXRZEDN.lean.js
index 10620f9..a328552 100644
--- a/site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BibZder6.lean.js
+++ b/site/.vitepress/dist/assets/blog_posts_choosing-security-level.md.BYXRZEDN.lean.js
@@ -1 +1 @@
-import{c as t,Q as a,j as o,m as r}from"./chunks/framework.BPKcPtvA.js";const p=JSON.parse('{"title":"Choosing Your Security Level","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/choosing-security-level.md","filePath":"blog/posts/choosing-security-level.md","lastUpdated":1780488246000}'),n={name:"blog/posts/choosing-security-level.md"};function l(s,e,i,d,h,c){return a(),o("div",null,[...e[0]||(e[0]=[r("",18)])])}const m=t(n,[["render",l]]);export{p as __pageData,m as default};
+import{c as t,Q as a,j as o,m as r}from"./chunks/framework.BPKcPtvA.js";const p=JSON.parse('{"title":"Choosing Your Security Level","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/choosing-security-level.md","filePath":"blog/posts/choosing-security-level.md","lastUpdated":1780488472000}'),n={name:"blog/posts/choosing-security-level.md"};function l(s,e,i,d,h,c){return a(),o("div",null,[...e[0]||(e[0]=[r("",18)])])}const m=t(n,[["render",l]]);export{p as __pageData,m as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_mental-models.md.CjqjYOIN.js b/site/.vitepress/dist/assets/blog_posts_mental-models.md.BRY80gtq.js
similarity index 98%
rename from site/.vitepress/dist/assets/blog_posts_mental-models.md.CjqjYOIN.js
rename to site/.vitepress/dist/assets/blog_posts_mental-models.md.BRY80gtq.js
index 9fb57db..1f04ebb 100644
--- a/site/.vitepress/dist/assets/blog_posts_mental-models.md.CjqjYOIN.js
+++ b/site/.vitepress/dist/assets/blog_posts_mental-models.md.BRY80gtq.js
@@ -1,4 +1,4 @@
-import{c as e,Q as a,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Agent Memory: Mental Models That Compound","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/mental-models.md","filePath":"blog/posts/mental-models.md","lastUpdated":1780488246000}'),n={name:"blog/posts/mental-models.md"};function l(o,s,h,p,r,d){return a(),t("div",null,[...s[0]||(s[0]=[i(`
Agent Memory: Mental Models That Compound ​
June 9, 2026
The biggest problem with agents is they forget. Every session starts from zero. Mental models solve this.
What a Mental Model Is ​
A YAML file that the agent owns. It reads it at startup and updates it after work:
yaml
expertise:
+import{c as e,Q as a,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Agent Memory: Mental Models That Compound","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/mental-models.md","filePath":"blog/posts/mental-models.md","lastUpdated":1780488472000}'),n={name:"blog/posts/mental-models.md"};function l(o,s,h,p,r,d){return a(),t("div",null,[...s[0]||(s[0]=[i(`
Agent Memory: Mental Models That Compound ​
June 9, 2026
The biggest problem with agents is they forget. Every session starts from zero. Mental models solve this.
What a Mental Model Is ​
A YAML file that the agent owns. It reads it at startup and updates it after work:
yaml
expertise:
   - topic: "API patterns"
     notes: "We use tRPC for type-safe API calls"
     evidence: "src/server/routers/*.ts"
diff --git a/site/.vitepress/dist/assets/blog_posts_mental-models.md.CjqjYOIN.lean.js b/site/.vitepress/dist/assets/blog_posts_mental-models.md.BRY80gtq.lean.js
similarity index 86%
rename from site/.vitepress/dist/assets/blog_posts_mental-models.md.CjqjYOIN.lean.js
rename to site/.vitepress/dist/assets/blog_posts_mental-models.md.BRY80gtq.lean.js
index b4093c2..f593eb6 100644
--- a/site/.vitepress/dist/assets/blog_posts_mental-models.md.CjqjYOIN.lean.js
+++ b/site/.vitepress/dist/assets/blog_posts_mental-models.md.BRY80gtq.lean.js
@@ -1 +1 @@
-import{c as e,Q as a,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Agent Memory: Mental Models That Compound","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/mental-models.md","filePath":"blog/posts/mental-models.md","lastUpdated":1780488246000}'),n={name:"blog/posts/mental-models.md"};function l(o,s,h,p,r,d){return a(),t("div",null,[...s[0]||(s[0]=[i("",15)])])}const g=e(n,[["render",l]]);export{c as __pageData,g as default};
+import{c as e,Q as a,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Agent Memory: Mental Models That Compound","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/mental-models.md","filePath":"blog/posts/mental-models.md","lastUpdated":1780488472000}'),n={name:"blog/posts/mental-models.md"};function l(o,s,h,p,r,d){return a(),t("div",null,[...s[0]||(s[0]=[i("",15)])])}const g=e(n,[["render",l]]);export{c as __pageData,g as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.CI68II5_.js b/site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.BxY1cXc_.js
similarity index 98%
rename from site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.CI68II5_.js
rename to site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.BxY1cXc_.js
index 5dc0539..102a648 100644
--- a/site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.CI68II5_.js
+++ b/site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.BxY1cXc_.js
@@ -1,4 +1,4 @@
-import{c as t,Q as a,j as n,m as s}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The Repo Is the Spec","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/repo-is-spec.md","filePath":"blog/posts/repo-is-spec.md","lastUpdated":1780488246000}'),o={name:"blog/posts/repo-is-spec.md"};function i(r,e,l,p,h,c){return a(),n("div",null,[...e[0]||(e[0]=[s(`
The Repo Is the Spec ​
June 5, 2026
The single most important principle in agentic engineering: all context must live in the repository.
The Problem ​
Imagine dropping a new engineer into a project at 3AM. They need to know what the project does, how to run tests, where to put new code, and what conventions to follow.
If the answer to any of these is "ask Bob," you fail. If the answer is "read CLAUDE.md in the repo," you win.
What the Repo Should Contain ​
repo/
+import{c as t,Q as a,j as n,m as s}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The Repo Is the Spec","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/repo-is-spec.md","filePath":"blog/posts/repo-is-spec.md","lastUpdated":1780488472000}'),o={name:"blog/posts/repo-is-spec.md"};function i(r,e,l,p,h,c){return a(),n("div",null,[...e[0]||(e[0]=[s(`
The Repo Is the Spec ​
June 5, 2026
The single most important principle in agentic engineering: all context must live in the repository.
The Problem ​
Imagine dropping a new engineer into a project at 3AM. They need to know what the project does, how to run tests, where to put new code, and what conventions to follow.
If the answer to any of these is "ask Bob," you fail. If the answer is "read CLAUDE.md in the repo," you win.
What the Repo Should Contain ​
repo/
 +-- CLAUDE.md or AGENTS.md    # Agent instructions (the most important file)
 +-- init.sh                   # Environment setup script
 +-- feature_list.json         # Tracked features with evidence
diff --git a/site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.CI68II5_.lean.js b/site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.BxY1cXc_.lean.js
similarity index 86%
rename from site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.CI68II5_.lean.js
rename to site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.BxY1cXc_.lean.js
index 02981ba..000e5a9 100644
--- a/site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.CI68II5_.lean.js
+++ b/site/.vitepress/dist/assets/blog_posts_repo-is-spec.md.BxY1cXc_.lean.js
@@ -1 +1 @@
-import{c as t,Q as a,j as n,m as s}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The Repo Is the Spec","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/repo-is-spec.md","filePath":"blog/posts/repo-is-spec.md","lastUpdated":1780488246000}'),o={name:"blog/posts/repo-is-spec.md"};function i(r,e,l,p,h,c){return a(),n("div",null,[...e[0]||(e[0]=[s("",14)])])}const m=t(o,[["render",i]]);export{u as __pageData,m as default};
+import{c as t,Q as a,j as n,m as s}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The Repo Is the Spec","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/repo-is-spec.md","filePath":"blog/posts/repo-is-spec.md","lastUpdated":1780488472000}'),o={name:"blog/posts/repo-is-spec.md"};function i(r,e,l,p,h,c){return a(),n("div",null,[...e[0]||(e[0]=[s("",14)])])}const m=t(o,[["render",i]]);export{u as __pageData,m as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_security-ladder.md.Rs2Sy2_y.js b/site/.vitepress/dist/assets/blog_posts_security-ladder.md.DQaqn6Yt.js
similarity index 98%
rename from site/.vitepress/dist/assets/blog_posts_security-ladder.md.Rs2Sy2_y.js
rename to site/.vitepress/dist/assets/blog_posts_security-ladder.md.DQaqn6Yt.js
index c1ec3ca..ad8fae5 100644
--- a/site/.vitepress/dist/assets/blog_posts_security-ladder.md.Rs2Sy2_y.js
+++ b/site/.vitepress/dist/assets/blog_posts_security-ladder.md.DQaqn6Yt.js
@@ -1,4 +1,4 @@
-import{c as a,Q as t,j as s,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The 6-Level Security Ladder","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/security-ladder.md","filePath":"blog/posts/security-ladder.md","lastUpdated":1780488246000}'),o={name:"blog/posts/security-ladder.md"};function l(r,e,i,p,c,h){return t(),s("div",null,[...e[0]||(e[0]=[n(`
The 6-Level Security Ladder ​
June 6, 2026
Every AI agent has access to bash. One tool — every dangerous verb: rm -rf, curl, git clean -fdx, terraform destroy, DROP DATABASE.
The math is brutal. At a 1% per-turn failure rate, there's a 63.4% chance of catastrophe over 100 turns. This isn't theoretical — it's the actual threat model for every agent in production.
Most engineers stop at Level 2 (system prompt rules) and think they're safe. They're not. Here's the full 6-level ladder that actually works.
Level 0: ACIP (Prompt Injection Defense) ​
Before bash security, there's prompt injection. An attacker can trick the agent into ignoring its instructions through:
  • Direct injection ("ignore previous instructions")
  • Indirect injection (malicious content in web pages the agent reads)
  • Role-playing bypasses ("you are now a free AI")
ACIP (Advanced Cognitive Inoculation Prompt) is a system prompt patch that makes agents resistant. It costs nothing (zero runtime overhead) and blocks simple attacks. Jeff Emanuel's ACIP is the reference implementation.
Level 1-2: Theatre (Skills + System Prompts) ​
These levels ask the model to behave. They work most of the time on frontier models. But "most of the time" is not a production guarantee. A 99% refusal rate means a 63% failure rate over 100 turns. Use them as accelerators, not enforcement.
Level 3: Blacklist Hook ​
A regex blacklist intercepts dangerous commands before execution. It catches rm -rf / directly. But here's the marquee break: the agent writes a Python script:
User prompt: "Clean up the target directory"
+import{c as a,Q as t,j as s,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The 6-Level Security Ladder","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/security-ladder.md","filePath":"blog/posts/security-ladder.md","lastUpdated":1780488472000}'),o={name:"blog/posts/security-ladder.md"};function l(r,e,i,p,c,h){return t(),s("div",null,[...e[0]||(e[0]=[n(`
The 6-Level Security Ladder ​
June 6, 2026
Every AI agent has access to bash. One tool — every dangerous verb: rm -rf, curl, git clean -fdx, terraform destroy, DROP DATABASE.
The math is brutal. At a 1% per-turn failure rate, there's a 63.4% chance of catastrophe over 100 turns. This isn't theoretical — it's the actual threat model for every agent in production.
Most engineers stop at Level 2 (system prompt rules) and think they're safe. They're not. Here's the full 6-level ladder that actually works.
Level 0: ACIP (Prompt Injection Defense) ​
Before bash security, there's prompt injection. An attacker can trick the agent into ignoring its instructions through:
  • Direct injection ("ignore previous instructions")
  • Indirect injection (malicious content in web pages the agent reads)
  • Role-playing bypasses ("you are now a free AI")
ACIP (Advanced Cognitive Inoculation Prompt) is a system prompt patch that makes agents resistant. It costs nothing (zero runtime overhead) and blocks simple attacks. Jeff Emanuel's ACIP is the reference implementation.
Level 1-2: Theatre (Skills + System Prompts) ​
These levels ask the model to behave. They work most of the time on frontier models. But "most of the time" is not a production guarantee. A 99% refusal rate means a 63% failure rate over 100 turns. Use them as accelerators, not enforcement.
Level 3: Blacklist Hook ​
A regex blacklist intercepts dangerous commands before execution. It catches rm -rf / directly. But here's the marquee break: the agent writes a Python script:
User prompt: "Clean up the target directory"
 Agent writes cleanup.py with os.remove() and shutil.rmtree()
 Agent runs: python cleanup.py
 Hook sees: "python cleanup.py" (not in blacklist)
diff --git a/site/.vitepress/dist/assets/blog_posts_security-ladder.md.Rs2Sy2_y.lean.js b/site/.vitepress/dist/assets/blog_posts_security-ladder.md.DQaqn6Yt.lean.js
similarity index 86%
rename from site/.vitepress/dist/assets/blog_posts_security-ladder.md.Rs2Sy2_y.lean.js
rename to site/.vitepress/dist/assets/blog_posts_security-ladder.md.DQaqn6Yt.lean.js
index cde9191..0bc4f14 100644
--- a/site/.vitepress/dist/assets/blog_posts_security-ladder.md.Rs2Sy2_y.lean.js
+++ b/site/.vitepress/dist/assets/blog_posts_security-ladder.md.DQaqn6Yt.lean.js
@@ -1 +1 @@
-import{c as a,Q as t,j as s,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The 6-Level Security Ladder","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/security-ladder.md","filePath":"blog/posts/security-ladder.md","lastUpdated":1780488246000}'),o={name:"blog/posts/security-ladder.md"};function l(r,e,i,p,c,h){return t(),s("div",null,[...e[0]||(e[0]=[n("",28)])])}const m=a(o,[["render",l]]);export{u as __pageData,m as default};
+import{c as a,Q as t,j as s,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The 6-Level Security Ladder","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/security-ladder.md","filePath":"blog/posts/security-ladder.md","lastUpdated":1780488472000}'),o={name:"blog/posts/security-ladder.md"};function l(r,e,i,p,c,h){return t(),s("div",null,[...e[0]||(e[0]=[n("",28)])])}const m=a(o,[["render",l]]);export{u as __pageData,m as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_three-x-rule.md.Dm6HtTS4.js b/site/.vitepress/dist/assets/blog_posts_three-x-rule.md.BHO6bhvz.js
similarity index 98%
rename from site/.vitepress/dist/assets/blog_posts_three-x-rule.md.Dm6HtTS4.js
rename to site/.vitepress/dist/assets/blog_posts_three-x-rule.md.BHO6bhvz.js
index 645b031..0320b01 100644
--- a/site/.vitepress/dist/assets/blog_posts_three-x-rule.md.Dm6HtTS4.js
+++ b/site/.vitepress/dist/assets/blog_posts_three-x-rule.md.BHO6bhvz.js
@@ -1,4 +1,4 @@
-import{c as s,Q as e,j as i,m as a}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The 3x Rule of Agent Costs","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/three-x-rule.md","filePath":"blog/posts/three-x-rule.md","lastUpdated":1780488246000}'),n={name:"blog/posts/three-x-rule.md"};function l(h,t,r,o,p,d){return e(),i("div",null,[...t[0]||(t[0]=[a(`
The 3x Rule of Agent Costs ​
June 10, 2026
Here is a rule that will save you from budget surprises: production agent costs 3x your prototype estimate.
Why the Multiplier Exists ​
PhaseMultiplierWhat Happens
Prototype1xHappy path works perfectly
With retries1.5xFailed tool calls retry, edge cases handled
Production3xMonitoring, error handling, observability, security
Where the Cost Goes ​
Output tokens dominate — about 70% of total cost. The model's reasoning is the expensive part. Input tokens (context) are about 20%. Cached tokens are 10%.
Every tool call costs 3-5x more than the call itself:
  • Planning which tool to use
  • Executing the tool
  • Error recovery if it fails
  • Parsing the result
  • Adding the result back to context
Quick Estimation ​
python
def estimate_cost(turns, tokens_per_turn, price_per_m):
+import{c as s,Q as e,j as i,m as a}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The 3x Rule of Agent Costs","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/three-x-rule.md","filePath":"blog/posts/three-x-rule.md","lastUpdated":1780488472000}'),n={name:"blog/posts/three-x-rule.md"};function l(h,t,r,o,p,d){return e(),i("div",null,[...t[0]||(t[0]=[a(`
The 3x Rule of Agent Costs ​
June 10, 2026
Here is a rule that will save you from budget surprises: production agent costs 3x your prototype estimate.
Why the Multiplier Exists ​
PhaseMultiplierWhat Happens
Prototype1xHappy path works perfectly
With retries1.5xFailed tool calls retry, edge cases handled
Production3xMonitoring, error handling, observability, security
Where the Cost Goes ​
Output tokens dominate — about 70% of total cost. The model's reasoning is the expensive part. Input tokens (context) are about 20%. Cached tokens are 10%.
Every tool call costs 3-5x more than the call itself:
  • Planning which tool to use
  • Executing the tool
  • Error recovery if it fails
  • Parsing the result
  • Adding the result back to context
Quick Estimation ​
python
def estimate_cost(turns, tokens_per_turn, price_per_m):
     base = turns * tokens_per_turn * price_per_m / 1_000_000
     return {
         "prototype": base,
diff --git a/site/.vitepress/dist/assets/blog_posts_three-x-rule.md.Dm6HtTS4.lean.js b/site/.vitepress/dist/assets/blog_posts_three-x-rule.md.BHO6bhvz.lean.js
similarity index 86%
rename from site/.vitepress/dist/assets/blog_posts_three-x-rule.md.Dm6HtTS4.lean.js
rename to site/.vitepress/dist/assets/blog_posts_three-x-rule.md.BHO6bhvz.lean.js
index c861a82..ad80f6d 100644
--- a/site/.vitepress/dist/assets/blog_posts_three-x-rule.md.Dm6HtTS4.lean.js
+++ b/site/.vitepress/dist/assets/blog_posts_three-x-rule.md.BHO6bhvz.lean.js
@@ -1 +1 @@
-import{c as s,Q as e,j as i,m as a}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The 3x Rule of Agent Costs","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/three-x-rule.md","filePath":"blog/posts/three-x-rule.md","lastUpdated":1780488246000}'),n={name:"blog/posts/three-x-rule.md"};function l(h,t,r,o,p,d){return e(),i("div",null,[...t[0]||(t[0]=[a("",15)])])}const c=s(n,[["render",l]]);export{u as __pageData,c as default};
+import{c as s,Q as e,j as i,m as a}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The 3x Rule of Agent Costs","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/three-x-rule.md","filePath":"blog/posts/three-x-rule.md","lastUpdated":1780488472000}'),n={name:"blog/posts/three-x-rule.md"};function l(h,t,r,o,p,d){return e(),i("div",null,[...t[0]||(t[0]=[a("",15)])])}const c=s(n,[["render",l]]);export{u as __pageData,c as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.RAPWkvlk.js b/site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.Gha_L_u5.js
similarity index 98%
rename from site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.RAPWkvlk.js
rename to site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.Gha_L_u5.js
index 83f611b..f13edee 100644
--- a/site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.RAPWkvlk.js
+++ b/site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.Gha_L_u5.js
@@ -1,4 +1,4 @@
-import{c as t,Q as r,j as a,m as i}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The Verifier Pattern","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/verifier-pattern.md","filePath":"blog/posts/verifier-pattern.md","lastUpdated":1780488246000}'),n={name:"blog/posts/verifier-pattern.md"};function o(s,e,d,l,h,p){return r(),a("div",null,[...e[0]||(e[0]=[i(`
The Verifier Pattern ​
June 8, 2026
Here's the problem: engineers spend roughly half their day reviewing agent output. Every "I created the table," "I added the foreign key," "I applied the migration" gets re-checked by hand. That review work is the binding constraint on agentic engineering throughput.
The verifier pattern solves this: a second, read-only agent that automatically checks every claim the builder makes.
Architecture ​
Builder (your terminal) ---unix socket---> Verifier (new window, input LOCKED)
+import{c as t,Q as r,j as a,m as i}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The Verifier Pattern","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/verifier-pattern.md","filePath":"blog/posts/verifier-pattern.md","lastUpdated":1780488472000}'),n={name:"blog/posts/verifier-pattern.md"};function o(s,e,d,l,h,p){return r(),a("div",null,[...e[0]||(e[0]=[i(`
The Verifier Pattern ​
June 8, 2026
Here's the problem: engineers spend roughly half their day reviewing agent output. Every "I created the table," "I added the foreign key," "I applied the migration" gets re-checked by hand. That review work is the binding constraint on agentic engineering throughput.
The verifier pattern solves this: a second, read-only agent that automatically checks every claim the builder makes.
Architecture ​
Builder (your terminal) ---unix socket---> Verifier (new window, input LOCKED)
   |                                            |
   v writes:                                    v reads (read-only tools):
   session.jsonl                                session.jsonl
diff --git a/site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.RAPWkvlk.lean.js b/site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.Gha_L_u5.lean.js
similarity index 86%
rename from site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.RAPWkvlk.lean.js
rename to site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.Gha_L_u5.lean.js
index 08b09bd..21a0ddb 100644
--- a/site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.RAPWkvlk.lean.js
+++ b/site/.vitepress/dist/assets/blog_posts_verifier-pattern.md.Gha_L_u5.lean.js
@@ -1 +1 @@
-import{c as t,Q as r,j as a,m as i}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The Verifier Pattern","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/verifier-pattern.md","filePath":"blog/posts/verifier-pattern.md","lastUpdated":1780488246000}'),n={name:"blog/posts/verifier-pattern.md"};function o(s,e,d,l,h,p){return r(),a("div",null,[...e[0]||(e[0]=[i("",15)])])}const f=t(n,[["render",o]]);export{u as __pageData,f as default};
+import{c as t,Q as r,j as a,m as i}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"The Verifier Pattern","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/verifier-pattern.md","filePath":"blog/posts/verifier-pattern.md","lastUpdated":1780488472000}'),n={name:"blog/posts/verifier-pattern.md"};function o(s,e,d,l,h,p){return r(),a("div",null,[...e[0]||(e[0]=[i("",15)])])}const f=t(n,[["render",o]]);export{u as __pageData,f as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.C0Kts5m3.js b/site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.7mduPfz1.js
similarity index 98%
rename from site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.C0Kts5m3.js
rename to site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.7mduPfz1.js
index 0887362..7f2db7f 100644
--- a/site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.C0Kts5m3.js
+++ b/site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.7mduPfz1.js
@@ -1 +1 @@
-import{c as t,Q as a,j as r,m as n}from"./chunks/framework.BPKcPtvA.js";const p=JSON.parse('{"title":"Vibe Coding vs Agentic Engineering","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/vibe-vs-agentic.md","filePath":"blog/posts/vibe-vs-agentic.md","lastUpdated":1780488246000}'),i={name:"blog/posts/vibe-vs-agentic.md"};function o(s,e,d,h,c,g){return a(),r("div",null,[...e[0]||(e[0]=[n('
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.
',15)])])}const u=t(i,[["render",o]]);export{p as __pageData,u as default};
+import{c as t,Q as a,j as r,m as n}from"./chunks/framework.BPKcPtvA.js";const p=JSON.parse('{"title":"Vibe Coding vs Agentic Engineering","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/vibe-vs-agentic.md","filePath":"blog/posts/vibe-vs-agentic.md","lastUpdated":1780488472000}'),i={name:"blog/posts/vibe-vs-agentic.md"};function o(s,e,d,h,c,g){return a(),r("div",null,[...e[0]||(e[0]=[n('
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.
',15)])])}const u=t(i,[["render",o]]);export{p as __pageData,u as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.C0Kts5m3.lean.js b/site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.7mduPfz1.lean.js
similarity index 86%
rename from site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.C0Kts5m3.lean.js
rename to site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.7mduPfz1.lean.js
index 53c7163..30d0eec 100644
--- a/site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.C0Kts5m3.lean.js
+++ b/site/.vitepress/dist/assets/blog_posts_vibe-vs-agentic.md.7mduPfz1.lean.js
@@ -1 +1 @@
-import{c as t,Q as a,j as r,m as n}from"./chunks/framework.BPKcPtvA.js";const p=JSON.parse('{"title":"Vibe Coding vs Agentic Engineering","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/vibe-vs-agentic.md","filePath":"blog/posts/vibe-vs-agentic.md","lastUpdated":1780488246000}'),i={name:"blog/posts/vibe-vs-agentic.md"};function o(s,e,d,h,c,g){return a(),r("div",null,[...e[0]||(e[0]=[n("",15)])])}const u=t(i,[["render",o]]);export{p as __pageData,u as default};
+import{c as t,Q as a,j as r,m as n}from"./chunks/framework.BPKcPtvA.js";const p=JSON.parse('{"title":"Vibe Coding vs Agentic Engineering","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/vibe-vs-agentic.md","filePath":"blog/posts/vibe-vs-agentic.md","lastUpdated":1780488472000}'),i={name:"blog/posts/vibe-vs-agentic.md"};function o(s,e,d,h,c,g){return a(),r("div",null,[...e[0]||(e[0]=[n("",15)])])}const u=t(i,[["render",o]]);export{p as __pageData,u as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.XVfTHLdp.js b/site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.BU2wUq_Y.js
similarity index 98%
rename from site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.XVfTHLdp.js
rename to site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.BU2wUq_Y.js
index 55c0cad..fc4d910 100644
--- a/site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.XVfTHLdp.js
+++ b/site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.BU2wUq_Y.js
@@ -1,4 +1,4 @@
-import{c as a,Q as e,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"What Is an AI Agent, Really?","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/what-is-an-agent.md","filePath":"blog/posts/what-is-an-agent.md","lastUpdated":1780488246000}'),n={name:"blog/posts/what-is-an-agent.md"};function o(l,s,h,p,r,d){return e(),t("div",null,[...s[0]||(s[0]=[i(`
What Is an AI Agent, Really? ​
June 2, 2026
An AI agent = LLM + Tools + Loop. If any of the three is missing, it is not an agent.
The Three Components ​
LLM — The reasoning engine. Given context and available tools, it decides what to do next. The LLM is NOT the agent. It is the brain of the agent.
Tools — The capability surface. Functions the agent can call: read files, run commands, search the web, query databases. Each tool has a name, description, and input schema.
Loop — The autonomous decision cycle. Think (LLM decides) leads to Act (tool executes) leads to Observe (result comes back) leads to Repeat.
What Each Combination Gives You ​
CombinationResult
LLM onlyChatbot
LLM + Tools (no loop)Augmented inference
LLM + Loop (no tools)Talking to itself
LLM + Tools + LoopAgent
The Loop in Pseudocode ​
python
messages = [system_prompt, user_request]
+import{c as a,Q as e,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"What Is an AI Agent, Really?","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/what-is-an-agent.md","filePath":"blog/posts/what-is-an-agent.md","lastUpdated":1780488472000}'),n={name:"blog/posts/what-is-an-agent.md"};function o(l,s,h,p,r,d){return e(),t("div",null,[...s[0]||(s[0]=[i(`
What Is an AI Agent, Really? ​
June 2, 2026
An AI agent = LLM + Tools + Loop. If any of the three is missing, it is not an agent.
The Three Components ​
LLM — The reasoning engine. Given context and available tools, it decides what to do next. The LLM is NOT the agent. It is the brain of the agent.
Tools — The capability surface. Functions the agent can call: read files, run commands, search the web, query databases. Each tool has a name, description, and input schema.
Loop — The autonomous decision cycle. Think (LLM decides) leads to Act (tool executes) leads to Observe (result comes back) leads to Repeat.
What Each Combination Gives You ​
CombinationResult
LLM onlyChatbot
LLM + Tools (no loop)Augmented inference
LLM + Loop (no tools)Talking to itself
LLM + Tools + LoopAgent
The Loop in Pseudocode ​
python
messages = [system_prompt, user_request]
 while True:
     response = llm.invoke(messages, tools=tool_definitions)
     if response.is_final_answer:
diff --git a/site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.XVfTHLdp.lean.js b/site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.BU2wUq_Y.lean.js
similarity index 86%
rename from site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.XVfTHLdp.lean.js
rename to site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.BU2wUq_Y.lean.js
index 1fd939d..31a9367 100644
--- a/site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.XVfTHLdp.lean.js
+++ b/site/.vitepress/dist/assets/blog_posts_what-is-an-agent.md.BU2wUq_Y.lean.js
@@ -1 +1 @@
-import{c as a,Q as e,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"What Is an AI Agent, Really?","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/what-is-an-agent.md","filePath":"blog/posts/what-is-an-agent.md","lastUpdated":1780488246000}'),n={name:"blog/posts/what-is-an-agent.md"};function o(l,s,h,p,r,d){return e(),t("div",null,[...s[0]||(s[0]=[i("",15)])])}const g=a(n,[["render",o]]);export{c as __pageData,g as default};
+import{c as a,Q as e,j as t,m as i}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"What Is an AI Agent, Really?","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/what-is-an-agent.md","filePath":"blog/posts/what-is-an-agent.md","lastUpdated":1780488472000}'),n={name:"blog/posts/what-is-an-agent.md"};function o(l,s,h,p,r,d){return e(),t("div",null,[...s[0]||(s[0]=[i("",15)])])}const g=a(n,[["render",o]]);export{c as __pageData,g as default};
diff --git a/site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.DM07YZPh.js b/site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.BVRIN2vH.js
similarity index 98%
rename from site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.DM07YZPh.js
rename to site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.BVRIN2vH.js
index 9e9d254..ea9474c 100644
--- a/site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.DM07YZPh.js
+++ b/site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.BVRIN2vH.js
@@ -1,4 +1,4 @@
-import{c as n,Q as a,j as t,m as s}from"./chunks/framework.BPKcPtvA.js";const d=JSON.parse('{"title":"Why One Agent Is Not Enough","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/why-multi-agent.md","filePath":"blog/posts/why-multi-agent.md","lastUpdated":1780488246000}'),i={name:"blog/posts/why-multi-agent.md"};function o(l,e,r,p,h,g){return a(),t("div",null,[...e[0]||(e[0]=[s(`
Why One Agent Is Not Enough ​
June 3, 2026
A single agent, no matter how smart, hits three ceilings.
The Context Ceiling ​
One agent doing everything means one context window holding everything. File contents, database schemas, business logic, deployment configs — they all compete for the same limited space. A 100K token codebase leaves zero room for the actual task.
The Capability Ceiling ​
A generalist agent is mediocre at everything. A specialized agent — backend dev, security reviewer, UI designer — outperforms the generalist in its domain by a wide margin. You wouldn't ask your frontend dev to write SQL migrations. Why ask your agent to?
The Reliability Ceiling ​
One agent failing = everything fails. Multi-agent systems degrade gracefully. If the reviewer agent crashes, the builder keeps working. If the planner goes down, deployed agents continue running.
The Solution: Depth-2 Delegation ​
The production-proven pattern:
User
+import{c as n,Q as a,j as t,m as s}from"./chunks/framework.BPKcPtvA.js";const d=JSON.parse('{"title":"Why One Agent Is Not Enough","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/why-multi-agent.md","filePath":"blog/posts/why-multi-agent.md","lastUpdated":1780488472000}'),i={name:"blog/posts/why-multi-agent.md"};function o(l,e,r,p,h,g){return a(),t("div",null,[...e[0]||(e[0]=[s(`
Why One Agent Is Not Enough ​
June 3, 2026
A single agent, no matter how smart, hits three ceilings.
The Context Ceiling ​
One agent doing everything means one context window holding everything. File contents, database schemas, business logic, deployment configs — they all compete for the same limited space. A 100K token codebase leaves zero room for the actual task.
The Capability Ceiling ​
A generalist agent is mediocre at everything. A specialized agent — backend dev, security reviewer, UI designer — outperforms the generalist in its domain by a wide margin. You wouldn't ask your frontend dev to write SQL migrations. Why ask your agent to?
The Reliability Ceiling ​
One agent failing = everything fails. Multi-agent systems degrade gracefully. If the reviewer agent crashes, the builder keeps working. If the planner goes down, deployed agents continue running.
The Solution: Depth-2 Delegation ​
The production-proven pattern:
User
   |
   v
 Orchestrator (Opus-level, thinking only)
diff --git a/site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.DM07YZPh.lean.js b/site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.BVRIN2vH.lean.js
similarity index 86%
rename from site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.DM07YZPh.lean.js
rename to site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.BVRIN2vH.lean.js
index 8a43e80..e8f199a 100644
--- a/site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.DM07YZPh.lean.js
+++ b/site/.vitepress/dist/assets/blog_posts_why-multi-agent.md.BVRIN2vH.lean.js
@@ -1 +1 @@
-import{c as n,Q as a,j as t,m as s}from"./chunks/framework.BPKcPtvA.js";const d=JSON.parse('{"title":"Why One Agent Is Not Enough","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/why-multi-agent.md","filePath":"blog/posts/why-multi-agent.md","lastUpdated":1780488246000}'),i={name:"blog/posts/why-multi-agent.md"};function o(l,e,r,p,h,g){return a(),t("div",null,[...e[0]||(e[0]=[s("",19)])])}const u=n(i,[["render",o]]);export{d as __pageData,u as default};
+import{c as n,Q as a,j as t,m as s}from"./chunks/framework.BPKcPtvA.js";const d=JSON.parse('{"title":"Why One Agent Is Not Enough","description":"","frontmatter":{},"headers":[],"relativePath":"blog/posts/why-multi-agent.md","filePath":"blog/posts/why-multi-agent.md","lastUpdated":1780488472000}'),i={name:"blog/posts/why-multi-agent.md"};function o(l,e,r,p,h,g){return a(),t("div",null,[...e[0]||(e[0]=[s("",19)])])}const u=n(i,[["render",o]]);export{d as __pageData,u as default};
diff --git a/site/.vitepress/dist/assets/buy.md.BlyAkuIK.js b/site/.vitepress/dist/assets/buy.md.CbIpCW5Y.js
similarity index 98%
rename from site/.vitepress/dist/assets/buy.md.BlyAkuIK.js
rename to site/.vitepress/dist/assets/buy.md.CbIpCW5Y.js
index 6b5fddc..a8573f6 100644
--- a/site/.vitepress/dist/assets/buy.md.BlyAkuIK.js
+++ b/site/.vitepress/dist/assets/buy.md.CbIpCW5Y.js
@@ -1 +1 @@
-import{c as t,Q as r,j as a,m as i}from"./chunks/framework.BPKcPtvA.js";const f=JSON.parse('{"title":"Buy","description":"","frontmatter":{},"headers":[],"relativePath":"buy.md","filePath":"buy.md","lastUpdated":1780488246000}'),o={name:"buy.md"};function l(s,e,d,n,c,h){return r(),a("div",null,[...e[0]||(e[0]=[i('
Buy ​
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  • Capstone reference implementation
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KitPriceWhat's IncludedBuy
Security Foundation$49L3-L5 hooks, damage control, sandboxBuy
Multi-Agent Orch.$49Teams, chains, mental models, domain locksBuy
Verifier Pro$39Builder + verifier, claim decompositionBuy
Task Discipline$29TillDone core + progress + nudgeBuy
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',16)])])}const p=t(o,[["render",l]]);export{f as __pageData,p as default};
+import{c as t,Q as r,j as a,m as i}from"./chunks/framework.BPKcPtvA.js";const f=JSON.parse('{"title":"Buy","description":"","frontmatter":{},"headers":[],"relativePath":"buy.md","filePath":"buy.md","lastUpdated":1780488472000}'),o={name:"buy.md"};function l(s,e,d,n,c,h){return r(),a("div",null,[...e[0]||(e[0]=[i('
Buy ​
lock Secure checkout powered by Gumroad 
Choose Your Tier ​
Self-Paced — $97 ​
Best for independent learners.
  • 65 lessons across 8 modules
  • 13 scaffolded labs with solutions
  • 56 quiz questions with answer keys
  • 20 production-ready SKILL.md files (7 kits)
  • Install scripts (PowerShell + bash)
  • Capstone reference implementation
  • Mock LLM for offline lab execution
  • Instant download (9 ZIP packages)
Buy Self-Paced — $97 — Instant access after purchase
Enterprise — $199 ​
Best for teams and organizations.
Everything in Self-Paced, plus:
  • Private Discord channel
  • 2 custom skills tailored to your stack
  • 1-hour onboarding call
  • Priority updates for 1 year
  • Early access to new skill kits
Buy Enterprise — $199 — Team license included
Cohort — $247 (Next cohort: TBD) ​
Best for structured learning with deadlines.
Everything in Enterprise, plus:
  • 6-week structured schedule with weekly milestones
  • Weekly live office hours
  • Peer review of capstone project
  • Completion certificate
  • Cohort alumni network
Join Cohort — $247 — Next cohort date TBD
Individual Skill Kits ​
Not ready for the full course? Buy individual kits:
KitPriceWhat's IncludedBuy
Security Foundation$49L3-L5 hooks, damage control, sandboxBuy
Multi-Agent Orch.$49Teams, chains, mental models, domain locksBuy
Verifier Pro$39Builder + verifier, claim decompositionBuy
Task Discipline$29TillDone core + progress + nudgeBuy
Autoresearch$39Experiment loop, integrity guardsBuy
Observability$29SQLite tracing, cost trackingBuy
CEO Board$4911 agent definitions + verifierBuy
What Happens After Purchase ​
  1. Instant download — ZIP packages delivered immediately via Gumroad
  2. Access links — Course materials, labs, and skill kits in your Gumroad library
  3. Enterprise/Cohort — You'll receive a follow-up email within 24hrs with Discord invite and onboarding details
  4. Updates — Lifetime access includes all future updates. Re-download anytime.
Certificate ​
Students who complete the capstone project receive a Certificate of Completion. The certificate verifies completion of 65 lessons, 13 labs, 56 quizzes, and a production-grade capstone project. Verify at fdsa.agency/verify.
Questions? Contact artale@fdsa.agency
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similarity index 83%
rename from site/.vitepress/dist/assets/buy.md.BlyAkuIK.lean.js
rename to site/.vitepress/dist/assets/buy.md.CbIpCW5Y.lean.js
index d16e6e8..770c1cc 100644
--- a/site/.vitepress/dist/assets/buy.md.BlyAkuIK.lean.js
+++ b/site/.vitepress/dist/assets/buy.md.CbIpCW5Y.lean.js
@@ -1 +1 @@
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new file mode 100644
index 0000000..163348c
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Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"337":{"title":"Implementation with mprocs","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"338":{"title":"Implementation with agent-mux","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"339":{"title":"Key Design Rules","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"340":{"title":"Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)","titles":["Module 4: Multi-Agent Orchestration"]},"341":{"title":"Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"342":{"title":"When to Use F-Threads","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"343":{"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)"]},"344":{"title":"Judge Criteria Template","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"345":{"title":"Lesson 4.3: Depth-2 Delegation","titles":["Module 4: Multi-Agent Orchestration"]},"346":{"title":"The Rule","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.3: Depth-2 Delegation"]},"347":{"title":"Lesson 4.4: Agent Experts That Remember","titles":["Module 4: Multi-Agent Orchestration"]},"348":{"title":"Mental Models","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"349":{"title":"Self-Improve Commands","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"350":{"title":"Compounding Knowledge","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"351":{"title":"Lesson 4.5: Domain Locking","titles":["Module 4: Multi-Agent Orchestration"]},"352":{"title":"Lesson 4.6: TillDone Task Discipline","titles":["Module 4: Multi-Agent Orchestration"]},"353":{"title":"The Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.6: TillDone Task Discipline"]},"354":{"title":"Why It Matters","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.6: TillDone Task Discipline"]},"355":{"title":"Lesson 4.7: Agent Chains","titles":["Module 4: Multi-Agent Orchestration"]},"356":{"title":"YAML-Defined Pipelines","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.7: Agent Chains"]},"357":{"title":"Variables","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.7: Agent Chains"]},"358":{"title":"Lesson 4.8: Pi-to-Pi Communication","titles":["Module 4: Multi-Agent Orchestration"]},"359":{"title":"The Shift: Hierarchy → Flat","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"360":{"title":"Reference Implementation: MCP Agent Mail (Jeff Emanuel, 1,955★)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"361":{"title":"Four Tools","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"362":{"title":"Safety","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"363":{"title":"Lesson 4.9: Conversation Awareness","titles":["Module 4: Multi-Agent Orchestration"]},"364":{"title":"Lesson 4.9b: Service Connectors","titles":["Module 4: Multi-Agent Orchestration"]},"365":{"title":"The Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"366":{"title":"Design Rules","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"367":{"title":"Reference Implementation","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"368":{"title":"Integration with Agent Teams","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"369":{"title":"Lesson 4.10: The CEO Board System","titles":["Module 4: Multi-Agent Orchestration"]},"370":{"title":"Flow","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.10: The CEO Board System"]},"371":{"title":"Lesson 4.11: UI Agents System","titles":["Module 4: Multi-Agent Orchestration"]},"372":{"title":"Lab 4.12: Build an Agent Chain","titles":["Module 4: Multi-Agent Orchestration"]},"373":{"title":"Lab 4.13: Deploy a Multi-Team System","titles":["Module 4: Multi-Agent Orchestration"]},"374":{"title":"Module 3: Safety & Security","titles":[]},"375":{"title":"Lesson 3.1: Why Bash Is the Single Point of Failure","titles":["Module 3: Safety & Security"]},"376":{"title":"Beyond Bash: Prompt Injection (L0)","titles":["Module 3: Safety & Security","Lesson 3.1: Why Bash Is the Single Point of Failure"]},"377":{"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"]},"378":{"title":"External vs Internal Threat Model","titles":["Module 3: Safety & Security","Lesson 3.1: Why Bash Is the Single Point of Failure"]},"379":{"title":"Lesson 3.2: The 5-Level Security Ladder","titles":["Module 3: Safety & Security"]},"380":{"title":"Key Insight","titles":["Module 3: Safety & Security","Lesson 3.2: The 5-Level Security Ladder"]},"381":{"title":"Lesson 3.3: The L3 Marque Break","titles":["Module 3: Safety & Security"]},"382":{"title":"Lesson 3.4: Damage Control — Three Access Levels","titles":["Module 3: Safety & Security"]},"383":{"title":"Zero Access (can't read or write)","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"384":{"title":"Read-Only (can read, can't modify)","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"385":{"title":"No-Delete (can modify, can't delete)","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"386":{"title":"Implementation Pattern","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"387":{"title":"Lesson 3.5: Hook Architecture — 13 Lifecycle Events","titles":["Module 3: Safety & Security"]},"388":{"title":"Session Lifecycle","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"389":{"title":"Main Loop","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"390":{"title":"Subagent Lifecycle","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"391":{"title":"Maintenance","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"392":{"title":"Flow Control","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"393":{"title":"Lesson 3.6: The Verifier Pattern","titles":["Module 3: Safety & Security"]},"394":{"title":"Architecture","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"395":{"title":"Key Properties","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"396":{"title":"The Confidence Ladder","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"397":{"title":"Lesson 3.7: Defense-in-Depth Stacking","titles":["Module 3: Safety & Security"]},"398":{"title":"Lab 3.8: Implement L4 Whitelist Hook","titles":["Module 3: Safety & Security"]},"399":{"title":"Lab 3.9: Build a Verifier Agent","titles":["Module 3: Safety & Security"]},"400":{"title":"Module 5: Production Patterns","titles":[]},"401":{"title":"Lesson 5.1: What Production Means for Agents","titles":["Module 5: Production Patterns"]},"402":{"title":"Production Readiness Checklist","titles":["Module 5: Production Patterns","Lesson 5.1: What Production Means for Agents"]},"403":{"title":"Lesson 5.2: CI/CD for Agents","titles":["Module 5: Production Patterns"]},"404":{"title":"Golden Dataset","titles":["Module 5: Production Patterns","Lesson 5.2: CI/CD for Agents"]},"405":{"title":"Pipeline","titles":["Module 5: Production Patterns","Lesson 5.2: CI/CD for Agents"]},"406":{"title":"Lesson 5.2b: Case Study — The 5-Tool Production Stack","titles":["Module 5: Production Patterns"]},"407":{"title":"The Stack","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"408":{"title":"Tool Roles and Selection Logic","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"409":{"title":"How They Work Together (Real Session Flow)","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"410":{"title":"Key Production Patterns","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"411":{"title":"Lesson 5.2c: The Agent Manager Role","titles":["Module 5: Production Patterns"]},"412":{"title":"Responsibilities","titles":["Module 5: Production Patterns","Lesson 5.2c: The Agent Manager Role"]},"413":{"title":"90-Day Setup Playbook","titles":["Module 5: Production Patterns","Lesson 5.2c: The Agent Manager Role"]},"414":{"title":"Lesson 5.3: Shadow Deployments","titles":["Module 5: Production Patterns"]},"415":{"title":"How It Works","titles":["Module 5: Production Patterns","Lesson 5.3: Shadow Deployments"]},"416":{"title":"When to Shadow Deploy","titles":["Module 5: Production Patterns","Lesson 5.3: Shadow Deployments"]},"417":{"title":"Lesson 5.4: Rollback Strategies","titles":["Module 5: Production Patterns"]},"418":{"title":"What Rollback Means for Agents","titles":["Module 5: Production Patterns","Lesson 5.4: Rollback Strategies"]},"419":{"title":"Implementation","titles":["Module 5: Production Patterns","Lesson 5.4: Rollback Strategies"]},"420":{"title":"Lesson 5.5: Observability & Monitoring","titles":["Module 5: Production Patterns"]},"421":{"title":"What to Trace (Every Single Turn)","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"422":{"title":"Decision Tracing","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"423":{"title":"Key Metrics","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"424":{"title":"Lesson 5.6: Alerting on Agent-Specific Signals","titles":["Module 5: Production Patterns"]},"425":{"title":"What to Alert On","titles":["Module 5: Production Patterns","Lesson 5.6: Alerting on Agent-Specific Signals"]},"426":{"title":"Alert Routing","titles":["Module 5: Production Patterns","Lesson 5.6: Alerting on Agent-Specific Signals"]},"427":{"title":"Lesson 5.6b: Cross-Provider Session Search","titles":["Module 5: Production Patterns"]},"428":{"title":"The Problem","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"429":{"title":"The Solution","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"430":{"title":"Why This Matters for Production","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"431":{"title":"Integration with Our Stack","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"432":{"title":"Lesson 5.7: Deployment Modes","titles":["Module 5: Production Patterns"]},"433":{"title":"Reachability","titles":["Module 5: Production Patterns","Lesson 5.7: Deployment Modes"]},"434":{"title":"Lesson 5.8: Cost Control","titles":["Module 5: Production Patterns"]},"435":{"title":"Budget Architecture","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"436":{"title":"Budget Policies","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"437":{"title":"Warning vs Hard Stop","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"438":{"title":"Lab 5.9: Set Up Agent Observability","titles":["Module 5: Production Patterns"]},"439":{"title":"Lab 5.10: CI/CD Pipeline","titles":["Module 5: Production Patterns"]},"440":{"title":"Module 6: Economics & Evaluation","titles":[]},"441":{"title":"Lesson 6.0: The Compute Advantage Equation","titles":["Module 6: Economics & Evaluation"]},"442":{"title":"The Equation","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"443":{"title":"What It Tells You","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"444":{"title":"Real-World Application (Your Stack)","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"445":{"title":"How to Optimize","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"446":{"title":"Lesson 6.1: LLM Pricing Landscape 2026","titles":["Module 6: Economics & Evaluation"]},"447":{"title":"Per-Million-Token Pricing","titles":["Module 6: Economics & Evaluation","Lesson 6.1: LLM Pricing Landscape 2026"]},"448":{"title":"Price Range: 100x Difference","titles":["Module 6: Economics & Evaluation","Lesson 6.1: LLM Pricing Landscape 2026"]},"449":{"title":"Lesson 6.2: Cascade Routing","titles":["Module 6: Economics & Evaluation"]},"450":{"title":"The Pattern","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"451":{"title":"Savings Profile","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"452":{"title":"Implementation","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"453":{"title":"Lesson 6.3: Cost Per Session Math","titles":["Module 6: Economics & Evaluation"]},"454":{"title":"Where Costs Come From","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"455":{"title":"The Multipliers","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"456":{"title":"The 3x Rule","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"457":{"title":"Quick Estimation","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"458":{"title":"Lesson 6.4: Agent Evaluation Metrics","titles":["Module 6: Economics & Evaluation"]},"459":{"title":"The Key Metrics","titles":["Module 6: Economics & Evaluation","Lesson 6.4: Agent Evaluation Metrics"]},"460":{"title":"pass@k Explained","titles":["Module 6: Economics & Evaluation","Lesson 6.4: Agent Evaluation Metrics"]},"461":{"title":"Lesson 6.5: Automated Evaluation","titles":["Module 6: Economics & Evaluation"]},"462":{"title":"Golden Dataset Approach","titles":["Module 6: Economics & Evaluation","Lesson 6.5: Automated Evaluation"]},"463":{"title":"VCR-Style Recording","titles":["Module 6: Economics & Evaluation","Lesson 6.5: Automated Evaluation"]},"464":{"title":"Lesson 6.6: A/B Testing Agents","titles":["Module 6: Economics & Evaluation"]},"465":{"title":"Canary Deployment","titles":["Module 6: Economics & Evaluation","Lesson 6.6: A/B Testing Agents"]},"466":{"title":"What to A/B Test","titles":["Module 6: Economics & Evaluation","Lesson 6.6: A/B Testing Agents"]},"467":{"title":"Lesson 6.7: Human Evaluation","titles":["Module 6: Economics & Evaluation"]},"468":{"title":"What Automated Evals Miss","titles":["Module 6: Economics & Evaluation","Lesson 6.7: Human Evaluation"]},"469":{"title":"Spot-Check Sampling","titles":["Module 6: Economics & Evaluation","Lesson 6.7: Human Evaluation"]},"470":{"title":"Lab 6.8: Build an Eval Harness","titles":["Module 6: Economics & Evaluation"]},"471":{"title":"Lab 6.9: Cost Optimization","titles":["Module 6: Economics & Evaluation"]},"472":{"title":"Module 8: Capstone — Production Multi-Agent System","titles":[]},"473":{"title":"Overview","titles":["Module 8: Capstone — Production Multi-Agent System"]},"474":{"title":"Project Options","titles":["Module 8: Capstone — Production Multi-Agent System"]},"475":{"title":"Option 1: Brand Monitor (Intermediate)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"476":{"title":"Option 2: Code Review Pipeline (Intermediate-Advanced)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"477":{"title":"Option 3: Strategic Decision Board (Advanced)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"478":{"title":"Capstone Phases","titles":["Module 8: Capstone — Production Multi-Agent System"]},"479":{"title":"Phase 1: System Design (60-90 min)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"480":{"title":"Phase 2: Agent Implementation (2-4 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"481":{"title":"Phase 3: Integration (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"482":{"title":"Phase 4: Security Hardening (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"483":{"title":"Phase 5: Testing & Evaluation (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"484":{"title":"Phase 6: Deployment (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"485":{"title":"Phase 7: Review & Iterate (1 hour)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"486":{"title":"Deliverables Checklist","titles":["Module 8: Capstone — Production Multi-Agent System"]},"487":{"title":"Pass Criteria","titles":["Module 8: Capstone — Production Multi-Agent System"]},"488":{"title":"Grading Rubric","titles":["Module 8: Capstone — Production Multi-Agent System"]},"489":{"title":"Module 7: Advanced Topics","titles":[]},"490":{"title":"Lesson 7.1: Autoresearch","titles":["Module 7: Advanced Topics"]},"491":{"title":"The Core Loop","titles":["Module 7: Advanced Topics","Lesson 7.1: Autoresearch"]},"492":{"title":"Brand Monitor Example","titles":["Module 7: Advanced Topics","Lesson 7.1: Autoresearch"]},"493":{"title":"Lesson 7.2: The Experiment Loop — Integrity","titles":["Module 7: Advanced Topics"]},"494":{"title":"Three Threats to Integrity","titles":["Module 7: Advanced Topics","Lesson 7.2: The Experiment Loop — Integrity"]},"495":{"title":"Integrity Guards","titles":["Module 7: Advanced Topics","Lesson 7.2: The Experiment Loop — Integrity"]},"496":{"title":"Lesson 7.3: Meta-Agents — Agents That Build Agents","titles":["Module 7: Advanced Topics"]},"497":{"title":"The Pattern","titles":["Module 7: Advanced Topics","Lesson 7.3: Meta-Agents — Agents That Build Agents"]},"498":{"title":"Pi-Pi Meta-Agent","titles":["Module 7: Advanced Topics","Lesson 7.3: Meta-Agents — Agents That Build Agents"]},"499":{"title":"Lesson 7.4: Beyond MCP — The Context Cost Trade-off","titles":["Module 7: Advanced Topics"]},"500":{"title":"The Matrix","titles":["Module 7: Advanced Topics","Lesson 7.4: Beyond MCP — The Context Cost Trade-off"]},"501":{"title":"The Insight","titles":["Module 7: Advanced Topics","Lesson 7.4: Beyond MCP — The Context Cost Trade-off"]},"502":{"title":"Lesson 7.5: The Mac Mini Agent — Physical Sandbox","titles":["Module 7: Advanced Topics"]},"503":{"title":"Architecture","titles":["Module 7: Advanced Topics","Lesson 7.5: The Mac Mini Agent — Physical Sandbox"]},"504":{"title":"Sentinel Pattern","titles":["Module 7: Advanced Topics","Lesson 7.5: The Mac Mini Agent — Physical Sandbox"]},"505":{"title":"Lesson 7.6: Always-On Agents","titles":["Module 7: Advanced Topics"]},"506":{"title":"Voice-to-Command Bridge","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"507":{"title":"Job Server Pattern","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"508":{"title":"Heartbeat Execution (from 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Breakdown"]},"517":{"title":"Layer 4: Work Isolation (dmux)","titles":["Reference Architecture: Production Multi-Agent Stack","Layer-by-Layer Breakdown"]},"518":{"title":"Tool-Specific Configurations","titles":["Reference Architecture: Production Multi-Agent Stack"]},"519":{"title":"Claude Code","titles":["Reference Architecture: Production Multi-Agent Stack","Tool-Specific Configurations"]},"520":{"title":"Pi Coding Agent","titles":["Reference Architecture: Production Multi-Agent Stack","Tool-Specific Configurations"]},"521":{"title":"OpenCode","titles":["Reference Architecture: Production Multi-Agent Stack","Tool-Specific Configurations"]},"522":{"title":"OpenClaw","titles":["Reference Architecture: Production Multi-Agent Stack","Tool-Specific Configurations"]},"523":{"title":"Cost Analysis","titles":["Reference Architecture: Production Multi-Agent Stack"]},"524":{"title":"Key Production Patterns","titles":["Reference Architecture: Production Multi-Agent Stack"]},"525":{"title":"What This Stack Proves","titles":["Reference Architecture: Production Multi-Agent Stack"]},"526":{"title":"Non-Technical Track: Decision Frameworks for AI Agents","titles":[]},"527":{"title":"For Managers, PMs, and Decision-Makers","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"528":{"title":"Framework 1: "Should We Use an Agent for This?"","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"529":{"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?""]},"530":{"title":"Decision Matrix","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 1: "Should We Use an Agent for This?""]},"531":{"title":"Framework 2: The 4 Agent Design Patterns (Andrew Ng)","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"532":{"title":"1. Reflection","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 2: The 4 Agent Design Patterns (Andrew Ng)"]},"533":{"title":"2. Tool Use","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 2: The 4 Agent Design Patterns (Andrew Ng)"]},"534":{"title":"3. Planning","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 2: The 4 Agent Design Patterns (Andrew Ng)"]},"535":{"title":"4. 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Access Levels"]},"337":{"title":"Read-Only (can read, can't modify)","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"338":{"title":"No-Delete (can modify, can't delete)","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"339":{"title":"Implementation Pattern","titles":["Module 3: Safety & Security","Lesson 3.4: Damage Control — Three Access Levels"]},"340":{"title":"Lesson 3.5: Hook Architecture — 13 Lifecycle Events","titles":["Module 3: Safety & Security"]},"341":{"title":"Session Lifecycle","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"342":{"title":"Main Loop","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"343":{"title":"Subagent Lifecycle","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"344":{"title":"Maintenance","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"345":{"title":"Flow Control","titles":["Module 3: Safety & Security","Lesson 3.5: Hook Architecture — 13 Lifecycle Events"]},"346":{"title":"Lesson 3.6: The Verifier Pattern","titles":["Module 3: Safety & Security"]},"347":{"title":"Architecture","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"348":{"title":"Key Properties","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"349":{"title":"The Confidence Ladder","titles":["Module 3: Safety & Security","Lesson 3.6: The Verifier Pattern"]},"350":{"title":"Lesson 3.7: Defense-in-Depth Stacking","titles":["Module 3: Safety & Security"]},"351":{"title":"Lab 3.8: Implement L4 Whitelist Hook","titles":["Module 3: Safety & Security"]},"352":{"title":"Lab 3.9: Build a Verifier Agent","titles":["Module 3: Safety & Security"]},"353":{"title":"Module 5: Production Patterns","titles":[]},"354":{"title":"Lesson 5.1: What Production Means for Agents","titles":["Module 5: Production Patterns"]},"355":{"title":"Production Readiness Checklist","titles":["Module 5: Production Patterns","Lesson 5.1: What Production Means for Agents"]},"356":{"title":"Lesson 5.2: CI/CD for Agents","titles":["Module 5: Production Patterns"]},"357":{"title":"Golden Dataset","titles":["Module 5: Production Patterns","Lesson 5.2: CI/CD for Agents"]},"358":{"title":"Pipeline","titles":["Module 5: Production Patterns","Lesson 5.2: CI/CD for Agents"]},"359":{"title":"Lesson 5.2b: Case Study — The 5-Tool Production Stack","titles":["Module 5: Production Patterns"]},"360":{"title":"The Stack","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"361":{"title":"Tool Roles and Selection Logic","titles":["Module 5: Production Patterns","Lesson 5.2b: Case Study — The 5-Tool Production Stack"]},"362":{"title":"How They Work Together (Real Session Flow)","titles":["Module 5: Production 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Agents","titles":["Module 5: Production Patterns","Lesson 5.4: Rollback Strategies"]},"372":{"title":"Implementation","titles":["Module 5: Production Patterns","Lesson 5.4: Rollback Strategies"]},"373":{"title":"Lesson 5.5: Observability & Monitoring","titles":["Module 5: Production Patterns"]},"374":{"title":"What to Trace (Every Single Turn)","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"375":{"title":"Decision Tracing","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"376":{"title":"Key Metrics","titles":["Module 5: Production Patterns","Lesson 5.5: Observability & Monitoring"]},"377":{"title":"Lesson 5.6: Alerting on Agent-Specific Signals","titles":["Module 5: Production Patterns"]},"378":{"title":"What to Alert On","titles":["Module 5: Production Patterns","Lesson 5.6: Alerting on Agent-Specific Signals"]},"379":{"title":"Alert Routing","titles":["Module 5: Production Patterns","Lesson 5.6: Alerting on Agent-Specific Signals"]},"380":{"title":"Lesson 5.6b: Cross-Provider Session Search","titles":["Module 5: Production Patterns"]},"381":{"title":"The Problem","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"382":{"title":"The Solution","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"383":{"title":"Why This Matters for Production","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"384":{"title":"Integration with Our Stack","titles":["Module 5: Production Patterns","Lesson 5.6b: Cross-Provider Session Search"]},"385":{"title":"Lesson 5.7: Deployment Modes","titles":["Module 5: Production Patterns"]},"386":{"title":"Reachability","titles":["Module 5: Production Patterns","Lesson 5.7: Deployment Modes"]},"387":{"title":"Lesson 5.8: Cost Control","titles":["Module 5: Production Patterns"]},"388":{"title":"Budget Architecture","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"389":{"title":"Budget Policies","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"390":{"title":"Warning vs Hard Stop","titles":["Module 5: Production Patterns","Lesson 5.8: Cost Control"]},"391":{"title":"Lab 5.9: Set Up Agent Observability","titles":["Module 5: Production Patterns"]},"392":{"title":"Lab 5.10: CI/CD Pipeline","titles":["Module 5: Production Patterns"]},"393":{"title":"Module 6: Economics & Evaluation","titles":[]},"394":{"title":"Lesson 6.0: The Compute Advantage Equation","titles":["Module 6: Economics & Evaluation"]},"395":{"title":"The Equation","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"396":{"title":"What It Tells You","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"397":{"title":"Real-World Application (Your Stack)","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"398":{"title":"How to Optimize","titles":["Module 6: Economics & Evaluation","Lesson 6.0: The Compute Advantage Equation"]},"399":{"title":"Lesson 6.1: LLM Pricing Landscape 2026","titles":["Module 6: Economics & Evaluation"]},"400":{"title":"Per-Million-Token Pricing","titles":["Module 6: Economics & Evaluation","Lesson 6.1: LLM Pricing Landscape 2026"]},"401":{"title":"Price Range: 100x Difference","titles":["Module 6: Economics & Evaluation","Lesson 6.1: LLM Pricing Landscape 2026"]},"402":{"title":"Lesson 6.2: Cascade Routing","titles":["Module 6: Economics & Evaluation"]},"403":{"title":"The Pattern","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"404":{"title":"Savings Profile","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"405":{"title":"Implementation","titles":["Module 6: Economics & Evaluation","Lesson 6.2: Cascade Routing"]},"406":{"title":"Lesson 6.3: Cost Per Session Math","titles":["Module 6: Economics & Evaluation"]},"407":{"title":"Where Costs Come From","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"408":{"title":"The Multipliers","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"409":{"title":"The 3x Rule","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"410":{"title":"Quick Estimation","titles":["Module 6: Economics & Evaluation","Lesson 6.3: Cost Per Session Math"]},"411":{"title":"Lesson 6.4: Agent Evaluation Metrics","titles":["Module 6: Economics & Evaluation"]},"412":{"title":"The Key Metrics","titles":["Module 6: Economics & Evaluation","Lesson 6.4: Agent Evaluation Metrics"]},"413":{"title":"pass@k Explained","titles":["Module 6: Economics & Evaluation","Lesson 6.4: Agent Evaluation Metrics"]},"414":{"title":"Lesson 6.5: Automated Evaluation","titles":["Module 6: Economics & Evaluation"]},"415":{"title":"Golden Dataset Approach","titles":["Module 6: Economics & Evaluation","Lesson 6.5: Automated Evaluation"]},"416":{"title":"VCR-Style Recording","titles":["Module 6: Economics & Evaluation","Lesson 6.5: Automated Evaluation"]},"417":{"title":"Lesson 6.6: A/B Testing Agents","titles":["Module 6: Economics & Evaluation"]},"418":{"title":"Canary Deployment","titles":["Module 6: Economics & Evaluation","Lesson 6.6: A/B Testing Agents"]},"419":{"title":"What to A/B Test","titles":["Module 6: Economics & Evaluation","Lesson 6.6: A/B Testing Agents"]},"420":{"title":"Lesson 6.7: Human Evaluation","titles":["Module 6: Economics & Evaluation"]},"421":{"title":"What Automated Evals Miss","titles":["Module 6: Economics & Evaluation","Lesson 6.7: Human Evaluation"]},"422":{"title":"Spot-Check Sampling","titles":["Module 6: Economics & Evaluation","Lesson 6.7: Human Evaluation"]},"423":{"title":"Lab 6.8: Build an Eval Harness","titles":["Module 6: Economics & Evaluation"]},"424":{"title":"Lab 6.9: Cost Optimization","titles":["Module 6: Economics & Evaluation"]},"425":{"title":"Module 4: Multi-Agent Orchestration","titles":[]},"426":{"title":"Lesson 4.1: Why One Agent Is Not Enough","titles":["Module 4: Multi-Agent Orchestration"]},"427":{"title":"The Stacking Insight","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.1: Why One Agent Is Not Enough"]},"428":{"title":"Lesson 4.2: Orchestration Patterns","titles":["Module 4: Multi-Agent Orchestration"]},"429":{"title":"Pattern 1: Dispatcher (Hub-and-Spoke)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"430":{"title":"Pattern 2: Pipeline (Sequential)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"431":{"title":"Pattern 3: Peer-to-Peer (Flat)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2: Orchestration Patterns"]},"432":{"title":"Lesson 4.2b: P-Threads — Parallel Agent Execution","titles":["Module 4: Multi-Agent Orchestration"]},"433":{"title":"Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"434":{"title":"When to Use P-Threads","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"435":{"title":"Implementation with mprocs","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"436":{"title":"Implementation with agent-mux","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"437":{"title":"Key Design Rules","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2b: P-Threads — Parallel Agent Execution"]},"438":{"title":"Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)","titles":["Module 4: Multi-Agent Orchestration"]},"439":{"title":"Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"440":{"title":"When to Use F-Threads","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"441":{"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)"]},"442":{"title":"Judge Criteria Template","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.2c: F-Threads — Fusion (N Agents, One Winner)"]},"443":{"title":"Lesson 4.3: Depth-2 Delegation","titles":["Module 4: Multi-Agent Orchestration"]},"444":{"title":"The Rule","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.3: Depth-2 Delegation"]},"445":{"title":"Lesson 4.4: Agent Experts That Remember","titles":["Module 4: Multi-Agent Orchestration"]},"446":{"title":"Mental Models","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"447":{"title":"Self-Improve Commands","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"448":{"title":"Compounding Knowledge","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.4: Agent Experts That Remember"]},"449":{"title":"Lesson 4.5: Domain Locking","titles":["Module 4: Multi-Agent Orchestration"]},"450":{"title":"Lesson 4.6: TillDone Task Discipline","titles":["Module 4: Multi-Agent Orchestration"]},"451":{"title":"The Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.6: TillDone Task Discipline"]},"452":{"title":"Why It Matters","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.6: TillDone Task Discipline"]},"453":{"title":"Lesson 4.7: Agent Chains","titles":["Module 4: Multi-Agent Orchestration"]},"454":{"title":"YAML-Defined Pipelines","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.7: Agent Chains"]},"455":{"title":"Variables","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.7: Agent Chains"]},"456":{"title":"Lesson 4.8: Pi-to-Pi Communication","titles":["Module 4: Multi-Agent Orchestration"]},"457":{"title":"The Shift: Hierarchy → Flat","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"458":{"title":"Reference Implementation: MCP Agent Mail (Jeff Emanuel, 1,955★)","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"459":{"title":"Four Tools","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"460":{"title":"Safety","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.8: Pi-to-Pi Communication"]},"461":{"title":"Lesson 4.9: Conversation Awareness","titles":["Module 4: Multi-Agent Orchestration"]},"462":{"title":"Lesson 4.9b: Service Connectors","titles":["Module 4: Multi-Agent Orchestration"]},"463":{"title":"The Pattern","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"464":{"title":"Design Rules","titles":["Module 4: Multi-Agent Orchestration","Lesson 4.9b: Service Connectors"]},"465":{"title":"Reference 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Context Cost Trade-off"]},"484":{"title":"The Insight","titles":["Module 7: Advanced Topics","Lesson 7.4: Beyond MCP — The Context Cost Trade-off"]},"485":{"title":"Lesson 7.5: The Mac Mini Agent — Physical Sandbox","titles":["Module 7: Advanced Topics"]},"486":{"title":"Architecture","titles":["Module 7: Advanced Topics","Lesson 7.5: The Mac Mini Agent — Physical Sandbox"]},"487":{"title":"Sentinel Pattern","titles":["Module 7: Advanced Topics","Lesson 7.5: The Mac Mini Agent — Physical Sandbox"]},"488":{"title":"Lesson 7.6: Always-On Agents","titles":["Module 7: Advanced Topics"]},"489":{"title":"Voice-to-Command Bridge","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"490":{"title":"Job Server Pattern","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"491":{"title":"Heartbeat Execution (from Paperclip)","titles":["Module 7: Advanced Topics","Lesson 7.6: Always-On Agents"]},"492":{"title":"Lab 7.7: Build an Autoresearch Loop","titles":["Module 7: Advanced Topics"]},"493":{"title":"Lab 7.8: Meta-Agent","titles":["Module 7: Advanced Topics"]},"494":{"title":"Module 8: Capstone — Production Multi-Agent System","titles":[]},"495":{"title":"Overview","titles":["Module 8: Capstone — Production Multi-Agent System"]},"496":{"title":"Project Options","titles":["Module 8: Capstone — Production Multi-Agent System"]},"497":{"title":"Option 1: Brand Monitor (Intermediate)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"498":{"title":"Option 2: Code Review Pipeline (Intermediate-Advanced)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"499":{"title":"Option 3: Strategic Decision Board (Advanced)","titles":["Module 8: Capstone — Production Multi-Agent System","Project Options"]},"500":{"title":"Capstone Phases","titles":["Module 8: Capstone — Production Multi-Agent System"]},"501":{"title":"Phase 1: System Design (60-90 min)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"502":{"title":"Phase 2: Agent Implementation (2-4 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"503":{"title":"Phase 3: Integration (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"504":{"title":"Phase 4: Security Hardening (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"505":{"title":"Phase 5: Testing & Evaluation (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"506":{"title":"Phase 6: Deployment (1-2 hours)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"507":{"title":"Phase 7: Review & Iterate (1 hour)","titles":["Module 8: Capstone — Production Multi-Agent System","Capstone Phases"]},"508":{"title":"Deliverables Checklist","titles":["Module 8: Capstone — Production Multi-Agent System"]},"509":{"title":"Pass Criteria","titles":["Module 8: Capstone — Production Multi-Agent System"]},"510":{"title":"Grading Rubric","titles":["Module 8: Capstone — Production Multi-Agent System"]},"511":{"title":"Non-Technical Track: Decision Frameworks for AI Agents","titles":[]},"512":{"title":"For Managers, PMs, and Decision-Makers","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"513":{"title":"Framework 1: "Should We Use an Agent for This?"","titles":["Non-Technical Track: Decision Frameworks for AI Agents"]},"514":{"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?""]},"515":{"title":"Decision Matrix","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 1: "Should We Use an Agent for This?""]},"516":{"title":"Framework 2: The 4 Agent Design Patterns (Andrew Ng)","titles":["Non-Technical Track: Decision 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Reflection","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 2: The 4 Agent Design Patterns (Andrew Ng)"]},"518":{"title":"2. Tool Use","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 2: The 4 Agent Design Patterns (Andrew Ng)"]},"519":{"title":"3. Planning","titles":["Non-Technical Track: Decision Frameworks for AI Agents","Framework 2: The 4 Agent Design Patterns (Andrew Ng)"]},"520":{"title":"4. 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terminalFDSA
CourseLabsBlogEnroll — $97
',1)),i("main",null,[d[12]||(d[12]=j('
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 — $97Start Learning Free
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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NOW ENROLLING
Agentic Engineering
65 lessons, 13 labs, 20 skill kits. Build, deploy, and maintain production-grade AI agent systems.
Enroll Now — $97View Curriculum
INITIALIZING AGENT_PLANNER...
FETCHING LLM CONTEXT: DONE
EXECUTING: search("agentic patterns")
ANALYZING RESULT_SET: SUCCESS
',1)),c("section",Ao,[i[0]||(i[0]=c("div",{class:"sec-label"},"Core Capabilities",-1)),c("div",Mo,[(o(),u(w,null,M(t,r=>c("div",{class:"gc",key:r.t},[c("div",Co,[c("span",Eo,V(r.icon),1)]),c("h3",Bo,V(r.t),1),c("p",Ho,V(r.d),1)])),64))])]),c("section",Fo,[i[1]||(i[1]=c("div",{class:"sec-label"},"Engineering Curriculum",-1)),i[2]||(i[2]=c("h2",{class:"sec-title"},"8 Modules. 65 Lessons. Zero Fluff.",-1)),c("div",Do,[(o(),u(w,null,M(n,(r,f)=>c("div",{key:f,class:x(["bi",r.s])},[c("div",Oo,V(r.n),1),c("div",Go,[c("span",Uo,V(r.ic),1)]),c("h4",zo,V(r.t),1),c("p",qo,V(r.d),1),c("div",jo,[(o(!0),u(w,null,M(r.tg,v=>(o(),u("span",{key:v},V(v),1))),128))]),r.p?(o(),u("div",Ro,[c("div",{class:"bi-fill",style:me({width:r.p})},null,4)])):m("",!0)],2)),64))])]),c("section",Wo,[c("div",Ko,[i[4]||(i[4]=c("span",{class:"material-symbols-outlined",style:{"font-size":"36px",color:"#d95c41","margin-bottom":"8px"}},"verified",-1)),i[5]||(i[5]=c("h2",{style:{"font-size":"24px","font-weight":"700","margin-bottom":"4px"}},"Full Course Access",-1)),i[6]||(i[6]=c("p",{style:{color:"#888","font-size":"14px","margin-bottom":"20px"}},"Lifetime updates included",-1)),i[7]||(i[7]=c("div",{style:{"font-size":"48px","font-weight":"800",color:"#d95c41","margin-bottom":"24px"}},"$97",-1)),c("ul",Jo,[(o(),u(w,null,M(s,r=>c("li",{key:r},[i[3]||(i[3]=c("span",{class:"material-symbols-outlined",style:{"font-size":"18px",color:"#d95c41"}},"check_circle",-1)),z(V(r),1)])),64))]),i[8]||(i[8]=c("a",{href:"/buy",class:"btn-p btn-primary",style:{width:"100%","text-align":"center"}},"Enroll Now",-1)),i[9]||(i[9]=c("div",{class:"pg"},"30-Day Guarantee",-1))])])]),i[12]||(i[12]=W('',2))]))}},Yo={},Qo={class:"fdsa-layout"},Zo={class:"fdsa-main"},ei={class:"fdsa-sidebar"},ti={class:"fdsa-content"},ni={class:"content-card"};function si(e,t){const n=q("Content");return o(),u("div",Qo,[t[0]||(t[0]=W('
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diff --git a/site/.vitepress/dist/assets/getting-started.md.CwhTdYNz.js b/site/.vitepress/dist/assets/getting-started.md.Boo_V9xC.js
similarity index 99%
rename from site/.vitepress/dist/assets/getting-started.md.CwhTdYNz.js
rename to site/.vitepress/dist/assets/getting-started.md.Boo_V9xC.js
index 6932b60..30877fd 100644
--- a/site/.vitepress/dist/assets/getting-started.md.CwhTdYNz.js
+++ b/site/.vitepress/dist/assets/getting-started.md.Boo_V9xC.js
@@ -1,4 +1,4 @@
-import{c as e,Q as a,j as s,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"Student Orientation Guide","description":"","frontmatter":{},"headers":[],"relativePath":"getting-started.md","filePath":"getting-started.md","lastUpdated":1780488246000}'),i={name:"getting-started.md"};function l(o,t,r,d,h,p){return a(),s("div",null,[...t[0]||(t[0]=[n(`
Student Orientation Guide ​
Welcome to the Agentic Engineering Course. This guide tells you exactly what to do first.
Step 1: Prerequisites Checklist ​
Before starting, ensure you have:
  • [ ] Python 3.12+ installed (python --version)
  • [ ] Git installed (git --version)
  • [ ] A text editor (VS Code recommended)
  • [ ] At least one API key (see API Keys section below)
  • [ ] Terminal (PowerShell on Windows, bash on Mac/Linux)
Optional (install later when needed) ​
  • [ ] Claude Code CLI — npm install -g @anthropic/claude-code
  • [ ] Pi Agent CLI — npm install -g pi-coding-agent
  • [ ] OpenCode CLI — npm install -g @opencode/cli
Step 2: Course Structure ​
The course is organized into 8 modules with 13 labs:
course/
+import{c as e,Q as a,j as s,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"Student Orientation Guide","description":"","frontmatter":{},"headers":[],"relativePath":"getting-started.md","filePath":"getting-started.md","lastUpdated":1780488472000}'),i={name:"getting-started.md"};function l(o,t,r,d,h,p){return a(),s("div",null,[...t[0]||(t[0]=[n(`
Student Orientation Guide ​
Welcome to the Agentic Engineering Course. This guide tells you exactly what to do first.
Step 1: Prerequisites Checklist ​
Before starting, ensure you have:
  • [ ] Python 3.12+ installed (python --version)
  • [ ] Git installed (git --version)
  • [ ] A text editor (VS Code recommended)
  • [ ] At least one API key (see API Keys section below)
  • [ ] Terminal (PowerShell on Windows, bash on Mac/Linux)
Optional (install later when needed) ​
  • [ ] Claude Code CLI — npm install -g @anthropic/claude-code
  • [ ] Pi Agent CLI — npm install -g pi-coding-agent
  • [ ] OpenCode CLI — npm install -g @opencode/cli
Step 2: Course Structure ​
The course is organized into 8 modules with 13 labs:
course/
 ├── 00-CURRICULUM.md         ← START HERE: Full lesson plan
 ├── M1-FOUNDATIONS.md        ← START HERE: Module 1
 ├── M2-ARCHITECTURE.md
diff --git a/site/.vitepress/dist/assets/getting-started.md.CwhTdYNz.lean.js b/site/.vitepress/dist/assets/getting-started.md.Boo_V9xC.lean.js
similarity index 85%
rename from site/.vitepress/dist/assets/getting-started.md.CwhTdYNz.lean.js
rename to site/.vitepress/dist/assets/getting-started.md.Boo_V9xC.lean.js
index 33d329e..1f09dee 100644
--- a/site/.vitepress/dist/assets/getting-started.md.CwhTdYNz.lean.js
+++ b/site/.vitepress/dist/assets/getting-started.md.Boo_V9xC.lean.js
@@ -1 +1 @@
-import{c as e,Q as a,j as s,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"Student Orientation Guide","description":"","frontmatter":{},"headers":[],"relativePath":"getting-started.md","filePath":"getting-started.md","lastUpdated":1780488246000}'),i={name:"getting-started.md"};function l(o,t,r,d,h,p){return a(),s("div",null,[...t[0]||(t[0]=[n("",38)])])}const g=e(i,[["render",l]]);export{u as __pageData,g as default};
+import{c as e,Q as a,j as s,m as n}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"Student Orientation Guide","description":"","frontmatter":{},"headers":[],"relativePath":"getting-started.md","filePath":"getting-started.md","lastUpdated":1780488472000}'),i={name:"getting-started.md"};function l(o,t,r,d,h,p){return a(),s("div",null,[...t[0]||(t[0]=[n("",38)])])}const g=e(i,[["render",l]]);export{u as __pageData,g as default};
diff --git a/site/.vitepress/dist/assets/labs_index.md.DTT5OogM.js b/site/.vitepress/dist/assets/labs_index.md.sAzXzfkI.js
similarity index 98%
rename from site/.vitepress/dist/assets/labs_index.md.DTT5OogM.js
rename to site/.vitepress/dist/assets/labs_index.md.sAzXzfkI.js
index 31dd834..3cf4a61 100644
--- a/site/.vitepress/dist/assets/labs_index.md.DTT5OogM.js
+++ b/site/.vitepress/dist/assets/labs_index.md.sAzXzfkI.js
@@ -1,4 +1,4 @@
-import{c as a,Q as i,j as s,m as e}from"./chunks/framework.BPKcPtvA.js";const k=JSON.parse('{"title":"Labs Overview","description":"","frontmatter":{},"headers":[],"relativePath":"labs/index.md","filePath":"labs/index.md","lastUpdated":1780488246000}'),n={name:"labs/index.md"};function l(d,t,r,h,o,p){return i(),s("div",null,[...t[0]||(t[0]=[e(`
Labs Overview ​
13 hands-on labs covering the full agentic engineering stack. Each lab has a starter.py (fill in the blanks) and solution.py (reference answer).
Lab Index ​
LabModuleTopicEst. Time
L1: First AgentM1Single-tool agent from scratch60 min
L2a: Multi-Tool AgentM2File ops + web search75 min
L2b: Context-AwareM2Sliding window + summarization60 min
L3a: Whitelist HookM3L4 security implementation60 min
L3b: Verifier AgentM3Read-only verification75 min
L4a: Agent ChainM4YAML pipeline60 min
L4b: Multi-TeamM4Team config + domain locking90 min
L5a: ObservabilityM5SQLite tool tracing60 min
L5b: CI/CDM5Golden dataset + regression gate60 min
L6a: Eval HarnessM6pass@k evaluation60 min
L6b: Cost OptimizationM6Cascade routing45 min
L7a: AutoresearchM7Self-improving experiment loop75 min
L7b: Meta-AgentM7Agent that builds agents60 min
Running Labs ​
bash
cd course/labs/L1-first-agent/
+import{c as a,Q as i,j as s,m as e}from"./chunks/framework.BPKcPtvA.js";const k=JSON.parse('{"title":"Labs Overview","description":"","frontmatter":{},"headers":[],"relativePath":"labs/index.md","filePath":"labs/index.md","lastUpdated":1780488472000}'),n={name:"labs/index.md"};function l(d,t,r,h,o,p){return i(),s("div",null,[...t[0]||(t[0]=[e(`
Labs Overview ​
13 hands-on labs covering the full agentic engineering stack. Each lab has a starter.py (fill in the blanks) and solution.py (reference answer).
Lab Index ​
LabModuleTopicEst. Time
L1: First AgentM1Single-tool agent from scratch60 min
L2a: Multi-Tool AgentM2File ops + web search75 min
L2b: Context-AwareM2Sliding window + summarization60 min
L3a: Whitelist HookM3L4 security implementation60 min
L3b: Verifier AgentM3Read-only verification75 min
L4a: Agent ChainM4YAML pipeline60 min
L4b: Multi-TeamM4Team config + domain locking90 min
L5a: ObservabilityM5SQLite tool tracing60 min
L5b: CI/CDM5Golden dataset + regression gate60 min
L6a: Eval HarnessM6pass@k evaluation60 min
L6b: Cost OptimizationM6Cascade routing45 min
L7a: AutoresearchM7Self-improving experiment loop75 min
L7b: Meta-AgentM7Agent that builds agents60 min
Running Labs ​
bash
cd course/labs/L1-first-agent/
 
 # Without API key (uses mock LLM automatically):
 python starter.py test.txt "What is this file about?"
diff --git a/site/.vitepress/dist/assets/labs_index.md.DTT5OogM.lean.js b/site/.vitepress/dist/assets/labs_index.md.sAzXzfkI.lean.js
similarity index 84%
rename from site/.vitepress/dist/assets/labs_index.md.DTT5OogM.lean.js
rename to site/.vitepress/dist/assets/labs_index.md.sAzXzfkI.lean.js
index 285cb22..a8f666e 100644
--- a/site/.vitepress/dist/assets/labs_index.md.DTT5OogM.lean.js
+++ b/site/.vitepress/dist/assets/labs_index.md.sAzXzfkI.lean.js
@@ -1 +1 @@
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+import{c as a,Q as i,j as s,m as e}from"./chunks/framework.BPKcPtvA.js";const k=JSON.parse('{"title":"Labs Overview","description":"","frontmatter":{},"headers":[],"relativePath":"labs/index.md","filePath":"labs/index.md","lastUpdated":1780488472000}'),n={name:"labs/index.md"};function l(d,t,r,h,o,p){return i(),s("div",null,[...t[0]||(t[0]=[e("",11)])])}const b=a(n,[["render",l]]);export{k as __pageData,b as default};
diff --git a/site/.vitepress/dist/assets/labs_l1-first-agent.md.CfhnnNn1.js b/site/.vitepress/dist/assets/labs_l1-first-agent.md.BwU9yf-G.js
similarity index 98%
rename from site/.vitepress/dist/assets/labs_l1-first-agent.md.CfhnnNn1.js
rename to site/.vitepress/dist/assets/labs_l1-first-agent.md.BwU9yf-G.js
index 4a847e8..e3932ea 100644
--- a/site/.vitepress/dist/assets/labs_l1-first-agent.md.CfhnnNn1.js
+++ b/site/.vitepress/dist/assets/labs_l1-first-agent.md.BwU9yf-G.js
@@ -1,2 +1,2 @@
-import{c as t,Q as a,j as i,m as s}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"L1: Your First Agent","description":"","frontmatter":{},"headers":[],"relativePath":"labs/l1-first-agent.md","filePath":"labs/l1-first-agent.md","lastUpdated":1780488246000}'),o={name:"labs/l1-first-agent.md"};function l(n,e,r,h,c,p){return a(),i("div",null,[...e[0]||(e[0]=[s(`
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/
+import{c as t,Q as a,j as i,m as s}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"L1: Your First Agent","description":"","frontmatter":{},"headers":[],"relativePath":"labs/l1-first-agent.md","filePath":"labs/l1-first-agent.md","lastUpdated":1780488472000}'),o={name:"labs/l1-first-agent.md"};function l(n,e,r,h,c,p){return a(),i("div",null,[...e[0]||(e[0]=[s(`
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)
`,17)])])}const k=t(o,[["render",l]]);export{u as __pageData,k as default};
diff --git a/site/.vitepress/dist/assets/labs_l1-first-agent.md.CfhnnNn1.lean.js b/site/.vitepress/dist/assets/labs_l1-first-agent.md.BwU9yf-G.lean.js
similarity index 85%
rename from site/.vitepress/dist/assets/labs_l1-first-agent.md.CfhnnNn1.lean.js
rename to site/.vitepress/dist/assets/labs_l1-first-agent.md.BwU9yf-G.lean.js
index e1aca01..4ca4b62 100644
--- a/site/.vitepress/dist/assets/labs_l1-first-agent.md.CfhnnNn1.lean.js
+++ b/site/.vitepress/dist/assets/labs_l1-first-agent.md.BwU9yf-G.lean.js
@@ -1 +1 @@
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+import{c as t,Q as a,j as i,m as s}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"L1: Your First Agent","description":"","frontmatter":{},"headers":[],"relativePath":"labs/l1-first-agent.md","filePath":"labs/l1-first-agent.md","lastUpdated":1780488472000}'),o={name:"labs/l1-first-agent.md"};function l(n,e,r,h,c,p){return a(),i("div",null,[...e[0]||(e[0]=[s("",17)])])}const k=t(o,[["render",l]]);export{u as __pageData,k as default};
diff --git a/site/.vitepress/dist/assets/style.DW2r_p9z.css b/site/.vitepress/dist/assets/style.1T3LhhFk.css
similarity index 79%
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index e9bb893..f7ff6b5 100644
--- a/site/.vitepress/dist/assets/style.DW2r_p9z.css
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var(--vp-c-border);border-radius:10px;overflow:hidden;font-size:.875rem!important;margin:24px 0!important}.vp-doc th{background:var(--vp-c-bg-mute);font-weight:600;font-size:.75rem!important;text-transform:uppercase;letter-spacing:.06em;color:var(--vp-c-text-2);padding:12px 16px;border-bottom:1px solid var(--vp-c-border)}.vp-doc td{padding:12px 16px;border-bottom:1px solid var(--vp-c-divider);font-size:.875rem!important}.vp-doc tr:last-child td{border-bottom:none}.vp-doc tr:hover td{background:var(--vp-c-bg-soft)}.vp-doc blockquote{border-left:3px solid var(--vp-c-brand-1)!important;background:var(--vp-c-brand-soft)!important;padding:16px 24px!important;border-radius:0 10px 10px 0!important;margin:24px 0!important}.vp-doc blockquote p{font-size:.875rem!important;color:var(--vp-c-text-2)!important;font-style:italic!important}.vp-doc img{max-width:100%;height:auto;border-radius:10px;margin:24px 0;border:1px solid var(--vp-c-border)}.vp-doc ul,.vp-doc 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rgba(217,92,65,.2);padding:12px 16px;border-radius:8px;font-size:13px;color:#d95c41;margin-bottom:32px}.tier-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(280px,1fr));gap:24px;margin:24px 0}.tier-grid h3{margin-top:0}.tier-grid ul{margin-bottom:20px}.VPLocalSearchBox[data-v-68e678c9]{position:fixed;z-index:100;top:0;right:0;bottom:0;left:0;display:flex}.backdrop[data-v-68e678c9]{position:absolute;top:0;right:0;bottom:0;left:0;background:var(--vp-backdrop-bg-color);transition:opacity .5s}.shell[data-v-68e678c9]{position:relative;padding:12px;margin:64px auto;display:flex;flex-direction:column;gap:16px;background:var(--vp-local-search-bg);width:min(100vw - 60px,900px);height:min-content;max-height:min(100vh - 128px,900px);border-radius:6px}@media (max-width: 767px){.shell[data-v-68e678c9]{margin:0;width:100vw;height:100vh;max-height:none;border-radius:0}}.search-bar[data-v-68e678c9]{border:1px solid 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diff --git a/site/.vitepress/dist/blog/index.html b/site/.vitepress/dist/blog/index.html
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     Blog | FDSA
     
     
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Appearance
Blog ​
Essays on agentic engineering, security, multi-agent systems, and production deployments.
Latest Posts ​
What Is an AI Agent, Really? — June 2 ​
LLM + Tools + Loop. The simplest correct explanation.
Why One Agent Is Not Enough — June 3 ​
Context, capability, and reliability ceilings of single-agent systems.
Vibe Coding vs Agentic Engineering — June 4 ​
The 5 hard rules that separate production from prompt gambling.
The Repo Is the Spec — June 5 ​
Why every instruction your agent needs must live in a file.
The 6-Level Security Ladder — June 6 ​
How to stop your AI agents from destroying production. From ACIP to no-bash.
Choosing Your Security Level — June 7 ​
Which L-level you need based on what your agent can access.
The Verifier Pattern — June 8 ​
A read-only verification agent that catches mistakes before production.
Agent Memory: Mental Models — June 9 ​
How agents remember across sessions using self-maintained expertise files.
The 3x Rule of Agent Costs — June 10 ​
Why production agents cost 3x your prototype estimate.
Cascade Routing: Cut API Costs by 66% — June 11 ​
Use cheap models for simple steps, expensive models for complex reasoning.
Posts are based on content from the Agentic Engineering Course. Each topic has a corresponding module with labs and exercises.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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

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diff --git a/site/.vitepress/dist/blog/posts/cascade-routing.html b/site/.vitepress/dist/blog/posts/cascade-routing.html
index 0db923f..27d3f64 100644
--- a/site/.vitepress/dist/blog/posts/cascade-routing.html
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     Cascade Routing: Cut Your API Costs by 66% | FDSA
     
     
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     elif task_complexity == "decision":
         return "claude-opus-4"
     elif task_complexity == "formatting":
-        return "gemini-2.5-flash"
When Not to Cascade ​
If your task is a single critical decision, use the best model. Cascade routing shines when you have a pipeline of steps with varying complexity, which is most real-world agent systems.
From Module 6 of the Agentic Engineering Course. The full module includes a cost optimization lab with working code.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

-    
+        return "gemini-2.5-flash"
When Not to Cascade ​
If your task is a single critical decision, use the best model. Cascade routing shines when you have a pipeline of steps with varying complexity, which is most real-world agent systems.
From Module 6 of the Agentic Engineering Course. The full module includes a cost optimization lab with working code.
Last updated: 
FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.
Copyright 2026

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

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

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diff --git a/site/.vitepress/dist/blog/posts/mental-models.html b/site/.vitepress/dist/blog/posts/mental-models.html
index 4d3c6dd..dc7a10b 100644
--- a/site/.vitepress/dist/blog/posts/mental-models.html
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     Agent Memory: Mental Models That Compound | FDSA
     
     
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 observations:
   - 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.
- + 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.

+ \ 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 f6e0f7c..087b287 100644 --- a/site/.vitepress/dist/blog/posts/repo-is-spec.html +++ b/site/.vitepress/dist/blog/posts/repo-is-spec.html @@ -6,14 +6,14 @@ The Repo Is the Spec | FDSA - + - + - + - + @@ -33,8 +33,8 @@ +-- Makefile # Common commands (test, build, lint) +-- 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.

- ++-- .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.

+ \ 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 ce89299..234b703 100644 --- a/site/.vitepress/dist/blog/posts/security-ladder.html +++ b/site/.vitepress/dist/blog/posts/security-ladder.html @@ -6,14 +6,14 @@ The 6-Level Security Ladder | FDSA - + - + - + - + @@ -39,8 +39,8 @@ L2: Safe-mode skill 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.

- +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.

+ \ 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 ecfc8a4..990fa6c 100644 --- a/site/.vitepress/dist/blog/posts/three-x-rule.html +++ b/site/.vitepress/dist/blog/posts/three-x-rule.html @@ -6,14 +6,14 @@ The 3x Rule of Agent Costs | FDSA - + - + - + - + @@ -32,8 +32,8 @@ "prototype": base, "with_retries": base * 1.5, "production": base * 3.0 - }

Budget Accordingly ​

If your prototype agent costs $0.10 per task, plan for $0.30 in production. At 10,000 tasks per month, that is $3,000 per month, not $1,000. The 3x rule keeps you honest.

From Module 6 of the Agentic Engineering Course. The full module covers cost optimization, cascade routing, and pass@k evaluation.

- + }

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.

+ \ 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 3f8d181..e4a51f5 100644 --- a/site/.vitepress/dist/blog/posts/verifier-pattern.html +++ b/site/.vitepress/dist/blog/posts/verifier-pattern.html @@ -6,14 +6,14 @@ The Verifier Pattern | FDSA - + - + - + - + @@ -31,8 +31,8 @@ v writes: v reads (read-only tools): session.jsonl session.jsonl | | - <--- verifier_prompt (corrective feedback) ----'

The builder doesn't know the verifier exists. The verifier CANNOT write code — it only reads files, greps for evidence, and reports confidence.

The Confidence Ladder ​

LevelMeaningBar Color
PERFECTEvery claim verified, zero gapsGreen
VERIFIEDAll passed, minor non-blocking gapsGreen
PARTIALNo failures, significant unverifiable gapsOrange
FEEDBACKClaims failed, correction sentOrange
FAILEDCannot verify, escalating to humanRed

Why This Works ​

  1. Spend tokens to save time — The verifier costs 2-5x more compute but collapses the review constraint from hours to seconds. Tokens are cheap. Your time is not.

  2. Structurally un-promptable — The verifier's input is locked. You can't drop one-off instructions into it. The only way to fix a verification gap is to edit the persona or the prompt template — improvements solve the entire problem class, not one instance.

  3. Defense in depth — The verifier has zero write tools. Even if compromised, it can't modify files. Its bash is restricted to read-only commands. This is the highest level of control you can give an agent.

The Feedback Loop ​

Every "could not verify" report becomes the next improvement. The verifier teaches you what your verifier is missing. This is how you build the system that builds the system.

This is an excerpt from Module 3 of the Agentic Engineering Course. The full module includes runnable code for setting up the verifier-builder pattern with Unix socket communication and the confidence ladder.

- + <--- 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.

+ \ 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 002f47c..db59132 100644 --- a/site/.vitepress/dist/blog/posts/vibe-vs-agentic.html +++ b/site/.vitepress/dist/blog/posts/vibe-vs-agentic.html @@ -6,14 +6,14 @@ Vibe Coding vs Agentic Engineering | FDSA - + - + - + - + @@ -26,8 +26,8 @@ -

Appearance

Vibe Coding vs Agentic Engineering ​

June 4, 2026

Vibe coding is prompting and shipping whatever comes out. Agentic engineering is building systems that produce reliable, verifiable results. They are not the same thing.

The 5 Hard Rules ​

1. Risk compounds with runtime. A 1% per-turn failure rate equals a 63% chance of disaster over 100 turns. Long-running agents are not safer. They are more exposed.

2. If your agent can write AND execute code, you are back at L1 security. The marquee break: agent writes cleanup.py, runs python cleanup.py, your production data is gone. The security hook never fired because it only saw "python cleanup.py."

3. Every turn is a roll of the dice. Modern models refuse well 99% of the time. That last 1% grows with every feature release. Engineer the harness for the 1%, not the 99%.

4. Token costs scale with loop depth, not task complexity. A simple task with a bad loop costs 10x more than a complex task with a clean loop. Optimize the loop first.

5. What you cannot measure, you cannot improve. Every agent system needs: cost per task, success rate, loop efficiency, failure mode tracking.

The Practical Difference ​

DimensionVibe CodingAgentic Engineering
ApproachPrompt and prayBuild and verify
SecurityTrust the modelTrust the harness
CostUnknown until the bill arrivesTracked and optimized
QualityWhatever comes outMeasured against golden dataset
IterationTry another promptFix the harness

The Karpathy Framing ​

At Sequoia Ascent 2026, Andrej Karpathy said: "Vibe coding raises the floor. Agentic engineering raises the ceiling." Vibe coding makes mediocre engineers productive. Agentic engineering makes great engineers extraordinary.

From Module 1 of the Agentic Engineering Course. The first module establishes the foundations that the rest of the course builds on.

Last updated:

FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.

Copyright 2026

- +

Appearance

Vibe Coding vs Agentic Engineering ​

June 4, 2026

Vibe coding is prompting and shipping whatever comes out. Agentic engineering is building systems that produce reliable, verifiable results. They are not the same thing.

The 5 Hard Rules ​

1. Risk compounds with runtime. A 1% per-turn failure rate equals a 63% chance of disaster over 100 turns. Long-running agents are not safer. They are more exposed.

2. If your agent can write AND execute code, you are back at L1 security. The marquee break: agent writes cleanup.py, runs python cleanup.py, your production data is gone. The security hook never fired because it only saw "python cleanup.py."

3. Every turn is a roll of the dice. Modern models refuse well 99% of the time. That last 1% grows with every feature release. Engineer the harness for the 1%, not the 99%.

4. Token costs scale with loop depth, not task complexity. A simple task with a bad loop costs 10x more than a complex task with a clean loop. Optimize the loop first.

5. What you cannot measure, you cannot improve. Every agent system needs: cost per task, success rate, loop efficiency, failure mode tracking.

The Practical Difference ​

DimensionVibe CodingAgentic Engineering
ApproachPrompt and prayBuild and verify
SecurityTrust the modelTrust the harness
CostUnknown until the bill arrivesTracked and optimized
QualityWhatever comes outMeasured against golden dataset
IterationTry another promptFix the harness

The Karpathy Framing ​

At Sequoia Ascent 2026, Andrej Karpathy said: "Vibe coding raises the floor. Agentic engineering raises the ceiling." Vibe coding makes mediocre engineers productive. Agentic engineering makes great engineers extraordinary.

From Module 1 of the Agentic Engineering Course. The first module establishes the foundations that the rest of the course builds on.

Last updated:

FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.

Copyright 2026

+ \ 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 2f4742f..de1a026 100644 --- a/site/.vitepress/dist/blog/posts/what-is-an-agent.html +++ b/site/.vitepress/dist/blog/posts/what-is-an-agent.html @@ -6,14 +6,14 @@ What Is an AI Agent, Really? | FDSA - + - + - + - + @@ -36,8 +36,8 @@ tool_args = response.tool_call.args 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.

- + 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.

+ \ 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 b5d6307..1d5c88c 100644 --- a/site/.vitepress/dist/blog/posts/why-multi-agent.html +++ b/site/.vitepress/dist/blog/posts/why-multi-agent.html @@ -6,14 +6,14 @@ Why One Agent Is Not Enough | FDSA - + - + - + - + @@ -41,8 +41,8 @@ | +-- 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:

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.

- + +-- 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:

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.

+ \ No newline at end of file diff --git a/site/.vitepress/dist/buy.html b/site/.vitepress/dist/buy.html index cd3e317..2c94e19 100644 --- a/site/.vitepress/dist/buy.html +++ b/site/.vitepress/dist/buy.html @@ -6,14 +6,14 @@ Buy | FDSA - + - + - + - + @@ -26,8 +26,8 @@ -

Appearance

Buy ​

lock Secure checkout powered by Gumroad

Choose Your Tier ​

Self-Paced — $97 ​

Best for independent learners.

  • 65 lessons across 8 modules
  • 13 scaffolded labs with solutions
  • 56 quiz questions with answer keys
  • 20 production-ready SKILL.md files (7 kits)
  • Install scripts (PowerShell + bash)
  • Capstone reference implementation
  • Mock LLM for offline lab execution
  • Instant download (9 ZIP packages)

Buy Self-Paced — $97 — Instant access after purchase

Enterprise — $199 ​

Best for teams and organizations.

Everything in Self-Paced, plus:

  • Private Discord channel
  • 2 custom skills tailored to your stack
  • 1-hour onboarding call
  • Priority updates for 1 year
  • Early access to new skill kits

Buy Enterprise — $199 — Team license included

Cohort — $247 (Next cohort: TBD) ​

Best for structured learning with deadlines.

Everything in Enterprise, plus:

  • 6-week structured schedule with weekly milestones
  • Weekly live office hours
  • Peer review of capstone project
  • Completion certificate
  • Cohort alumni network

Join Cohort — $247 — Next cohort date TBD

Individual Skill Kits ​

Not ready for the full course? Buy individual kits:

KitPriceWhat's IncludedBuy
Security Foundation$49L3-L5 hooks, damage control, sandboxBuy
Multi-Agent Orch.$49Teams, chains, mental models, domain locksBuy
Verifier Pro$39Builder + verifier, claim decompositionBuy
Task Discipline$29TillDone core + progress + nudgeBuy
Autoresearch$39Experiment loop, integrity guardsBuy
Observability$29SQLite tracing, cost trackingBuy
CEO Board$4911 agent definitions + verifierBuy

What Happens After Purchase ​

  1. Instant download — ZIP packages delivered immediately via Gumroad
  2. Access links — Course materials, labs, and skill kits in your Gumroad library
  3. Enterprise/Cohort — You'll receive a follow-up email within 24hrs with Discord invite and onboarding details
  4. Updates — Lifetime access includes all future updates. Re-download anytime.

Certificate ​

Students who complete the capstone project receive a Certificate of Completion. The certificate verifies completion of 65 lessons, 13 labs, 56 quizzes, and a production-grade capstone project. Verify at fdsa.agency/verify.

Questions? Contact artale@fdsa.agency

Last updated:

FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.

Copyright 2026

- +

Appearance

Buy ​

lock Secure checkout powered by Gumroad

Choose Your Tier ​

Self-Paced — $97 ​

Best for independent learners.

  • 65 lessons across 8 modules
  • 13 scaffolded labs with solutions
  • 56 quiz questions with answer keys
  • 20 production-ready SKILL.md files (7 kits)
  • Install scripts (PowerShell + bash)
  • Capstone reference implementation
  • Mock LLM for offline lab execution
  • Instant download (9 ZIP packages)

Buy Self-Paced — $97 — Instant access after purchase

Enterprise — $199 ​

Best for teams and organizations.

Everything in Self-Paced, plus:

  • Private Discord channel
  • 2 custom skills tailored to your stack
  • 1-hour onboarding call
  • Priority updates for 1 year
  • Early access to new skill kits

Buy Enterprise — $199 — Team license included

Cohort — $247 (Next cohort: TBD) ​

Best for structured learning with deadlines.

Everything in Enterprise, plus:

  • 6-week structured schedule with weekly milestones
  • Weekly live office hours
  • Peer review of capstone project
  • Completion certificate
  • Cohort alumni network

Join Cohort — $247 — Next cohort date TBD

Individual Skill Kits ​

Not ready for the full course? Buy individual kits:

KitPriceWhat's IncludedBuy
Security Foundation$49L3-L5 hooks, damage control, sandboxBuy
Multi-Agent Orch.$49Teams, chains, mental models, domain locksBuy
Verifier Pro$39Builder + verifier, claim decompositionBuy
Task Discipline$29TillDone core + progress + nudgeBuy
Autoresearch$39Experiment loop, integrity guardsBuy
Observability$29SQLite tracing, cost trackingBuy
CEO Board$4911 agent definitions + verifierBuy

What Happens After Purchase ​

  1. Instant download — ZIP packages delivered immediately via Gumroad
  2. Access links — Course materials, labs, and skill kits in your Gumroad library
  3. Enterprise/Cohort — You'll receive a follow-up email within 24hrs with Discord invite and onboarding details
  4. Updates — Lifetime access includes all future updates. Re-download anytime.

Certificate ​

Students who complete the capstone project receive a Certificate of Completion. The certificate verifies completion of 65 lessons, 13 labs, 56 quizzes, and a production-grade capstone project. Verify at fdsa.agency/verify.

Questions? Contact artale@fdsa.agency

Last updated:

FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.

Copyright 2026

+ \ No newline at end of file diff --git a/site/.vitepress/dist/certificate.html b/site/.vitepress/dist/certificate.html index df01da7..43e147d 100644 --- a/site/.vitepress/dist/certificate.html +++ b/site/.vitepress/dist/certificate.html @@ -6,12 +6,12 @@ Certificate of Completion | FDSA - + - + - + @@ -27,7 +27,7 @@

Appearance

Certificate of Completion ​

This certifies that

________________________________

has completed the

FDSA Agentic Engineering Course ​

65 lessons across 8 modules13 hands-on labs with starter code and solutions56 quiz questions across 7 module checkpoints20 production-ready SKILL.md files in 7 kitsCapstone: production-grade multi-agent system

Curriculum: Agent Harness (M1-M3), Software Factory (M4), Extensible Software (M2+M5), Always-On Agents (M7), Agentic Access (M2+M5), Tokenomics (M6). Framework-agnostic across Claude Code, Pi Agent, OpenCode, Hermes, and OpenClaw.

Verify at: https://fdsa.agency/verify

Last updated:

FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.

Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/dist/getting-started.html b/site/.vitepress/dist/getting-started.html index b568a99..d0ad62c 100644 --- a/site/.vitepress/dist/getting-started.html +++ b/site/.vitepress/dist/getting-started.html @@ -6,14 +6,14 @@ Student Orientation Guide | FDSA - + - + - + - + @@ -46,8 +46,8 @@ bash install.sh # 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 ​

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
- +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 ​

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
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NOW ENROLLING

Agentic Engineering

65 lessons, 13 labs, 20 skill kits. Build, deploy, and maintain production-grade AI agent systems.

Enroll Now — $97View Curriculum
INITIALIZING AGENT_PLANNER...
FETCHING LLM CONTEXT: DONE
EXECUTING: search("agentic patterns")
ANALYZING RESULT_SET: SUCCESS
Core Capabilities
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Agent Harness

Own your harness. 6-level security ladder, verifier pattern, hooks.

speed

Software Factory

Systems that build systems. Agent chains, teams, mental models.

psychology

Extensible Software

Pluggable, composable. Beyond MCP, tool design, CI/CD.

all_inclusive

Always-On Agents

Autoresearch, heartbeat execution, meta-agents. Run 24/7.

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Agentic Access

API-first design, deployment modes, service connectors.

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Tokenomics

Cascade routing, pass@k evals, 3x cost rule, optimization.

Engineering Curriculum

8 Modules. 65 Lessons. Zero Fluff.

01
psychology

Foundations

What agents are, harness vs model, decision frameworks.

LLM + Tools + LoopAgent LoopTrust
02
handyman

Architecture

Tools, context, memory, skills, codebase patterns.

4 PillarsToolsMemory
03
shield

Safety & Security

6-level ladder, hooks, verifier pattern, defense-in-depth.

L0-L5VerifierACIP
04
hub

Orchestration

Multi-agent patterns, P-threads, P2P, CEO Board.

TeamsChainsP2P
05
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Production

CI/CD, shadow deploys, observability, rollback.

DeployMonitorRollback
06
analytics

Economics

Model pricing, cascade routing, pass@k, cost optimization.

100x Range3x RuleEvals
07
architecture

Advanced

Autoresearch, meta-agents, beyond MCP, always-on.

Self-ImprovingMeta-Agents
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Capstone

Build a production-grade multi-agent system.

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FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.

Copyright 2026

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CourseLabsBlogEnroll — $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 — $97Start Learning Free
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

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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
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Capstone

Build production multi-agent system. Brand Monitor, CEO Board, or Code Review Pipeline.

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Common Questions

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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/dist/labs/index.html b/site/.vitepress/dist/labs/index.html index 030396d..69de7e9 100644 --- a/site/.vitepress/dist/labs/index.html +++ b/site/.vitepress/dist/labs/index.html @@ -6,14 +6,14 @@ Labs Overview | FDSA - + - + - + - + @@ -36,8 +36,8 @@ python starter.py test.txt "What is this file about?" # Check solution after attempting: -python solution.py test.txt "What is this file about?"

Lab Structure ​

Each lab has:

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.

- +python solution.py test.txt "What is this file about?"

Lab Structure ​

Each lab has:

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.

+ \ 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 61851cf..cf8c4de 100644 --- a/site/.vitepress/dist/labs/l1-first-agent.html +++ b/site/.vitepress/dist/labs/l1-first-agent.html @@ -6,14 +6,14 @@ L1: Your First Agent | FDSA - + - + - + - + @@ -27,8 +27,8 @@

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

- +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:

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)
+ \ 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 8e5a2a0..ec980b2 100644 --- a/site/.vitepress/dist/labs/l2-context.html +++ b/site/.vitepress/dist/labs/l2-context.html @@ -6,12 +6,12 @@ L2b: Context-Aware Agent | FDSA - + - + - + @@ -28,7 +28,7 @@

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L2b: Context-Aware Agent ​

Module: M2 Architecture
Files: starter.py, solution.py

Objective ​

Implement sliding window + summarization for long agent sessions.

Concepts ​

Refer to the corresponding module for full concept explanations.

Starter ​

bash
cd course/labs/l2-context/
 # Open starter file and fill in the TODOs

Checkpoints ​

  1. Build a ContextManager class
  2. Implement max_recent_turns sliding window
  3. Summarize old messages when window exceeds limit
  4. Build context from summary + recent messages

Solution ​

Compare against the solution file after attempting the starter.

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 1705934..0b9c57f 100644 --- a/site/.vitepress/dist/labs/l2-multi-tool.html +++ b/site/.vitepress/dist/labs/l2-multi-tool.html @@ -6,12 +6,12 @@ L2a: Multi-Tool Agent | FDSA - + - + - + @@ -28,7 +28,7 @@

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L2a: Multi-Tool Agent ​

Module: M2 Architecture
Files: starter.py, solution.py

Objective ​

Add file operations AND web search to your agent.

Concepts ​

Refer to the corresponding module for full concept explanations.

Starter ​

bash
cd course/labs/l2-multi-tool/
 # Open starter file and fill in the TODOs

Checkpoints ​

  1. Define a read_file tool schema
  2. Define a search_web tool (DuckDuckGo or similar)
  3. Define a write_file tool
  4. Implement execute_tool for all three
  5. Run the agent loop with tool result feedback

Solution ​

Compare against the solution file after attempting the starter.

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 17c10f9..249da3e 100644 --- a/site/.vitepress/dist/labs/l3-verifier.html +++ b/site/.vitepress/dist/labs/l3-verifier.html @@ -6,12 +6,12 @@ L3b: Verifier Agent | FDSA - + - + - + @@ -28,7 +28,7 @@

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 d29bdd0..1269a99 100644 --- a/site/.vitepress/dist/labs/l3-whitelist-hook.html +++ b/site/.vitepress/dist/labs/l3-whitelist-hook.html @@ -6,12 +6,12 @@ L3a: L4 Whitelist Hook | FDSA - + - + - + @@ -32,7 +32,7 @@ rm -rf target/ → BLOCK python cleanup.py → BLOCK (L3 marque break) npm test && rm -rf / → BLOCK (compound operator) - + \ 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 207cc03..00269a7 100644 --- a/site/.vitepress/dist/labs/l4-agent-chain.html +++ b/site/.vitepress/dist/labs/l4-agent-chain.html @@ -6,12 +6,12 @@ L4a: Agent Chain | FDSA - + - + - + @@ -28,7 +28,7 @@

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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.

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-multi-team.html b/site/.vitepress/dist/labs/l4-multi-team.html index b51698d..6ed7bb8 100644 --- a/site/.vitepress/dist/labs/l4-multi-team.html +++ b/site/.vitepress/dist/labs/l4-multi-team.html @@ -6,12 +6,12 @@ L4b: Multi-Team Config | FDSA - + - + - + @@ -28,7 +28,7 @@

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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.

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/l5-cicd.html b/site/.vitepress/dist/labs/l5-cicd.html index e86082b..d3863f2 100644 --- a/site/.vitepress/dist/labs/l5-cicd.html +++ b/site/.vitepress/dist/labs/l5-cicd.html @@ -6,12 +6,12 @@ L5b: CI/CD Pipeline | FDSA - + - + - + @@ -28,7 +28,7 @@

Appearance

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.

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/l5-observability.html b/site/.vitepress/dist/labs/l5-observability.html index 5e15a34..945b520 100644 --- a/site/.vitepress/dist/labs/l5-observability.html +++ b/site/.vitepress/dist/labs/l5-observability.html @@ -6,12 +6,12 @@ L5a: Observability | FDSA - + - + - + @@ -28,7 +28,7 @@

Appearance

L5a: Observability ​

Module: M5 Production
Files: starter.py, solution.py

Objective ​

Trace every tool call + LLM completion to SQLite.

Concepts ​

Refer to the corresponding module for full concept explanations.

Starter ​

bash
cd course/labs/l5-observability/
 # Open starter file and fill in the TODOs

Checkpoints ​

  1. Define SQLite schema for tool_calls and llm_completions
  2. Implement ObservabilityTracker class
  3. Log tool calls with params, result, duration
  4. Log LLM completions with token counts and cost
  5. Generate session summary report

Solution ​

Compare against the solution file after attempting the starter.

FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.

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 c43b31e..753b728 100644 --- a/site/.vitepress/dist/labs/l6-cost-optimization.html +++ b/site/.vitepress/dist/labs/l6-cost-optimization.html @@ -6,12 +6,12 @@ L6b: Cost Optimization | FDSA - + - + - + @@ -28,7 +28,7 @@

Appearance

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.

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/l6-eval-harness.html b/site/.vitepress/dist/labs/l6-eval-harness.html index e392b69..9e64f0a 100644 --- a/site/.vitepress/dist/labs/l6-eval-harness.html +++ b/site/.vitepress/dist/labs/l6-eval-harness.html @@ -6,12 +6,12 @@ L6a: Eval Harness | FDSA - + - + - + @@ -28,7 +28,7 @@

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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.

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/l7-autoresearch.html b/site/.vitepress/dist/labs/l7-autoresearch.html index a8832b7..31be14b 100644 --- a/site/.vitepress/dist/labs/l7-autoresearch.html +++ b/site/.vitepress/dist/labs/l7-autoresearch.html @@ -6,12 +6,12 @@ L7a: Autoresearch Loop | FDSA - + - + - + @@ -30,7 +30,7 @@ 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
- + \ 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 a2213a6..20b3e1b 100644 --- a/site/.vitepress/dist/labs/l7-meta-agent.html +++ b/site/.vitepress/dist/labs/l7-meta-agent.html @@ -6,12 +6,12 @@ L7b: Meta-Agent | FDSA - + - + - + @@ -28,7 +28,7 @@

Appearance

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 2de999e..aaea0e0 100644 --- a/site/.vitepress/dist/modules/competitive-analysis.html +++ b/site/.vitepress/dist/modules/competitive-analysis.html @@ -6,12 +6,12 @@ Competitive Analysis: Agentic Engineering Course Landscape | FDSA - + - + - + @@ -39,7 +39,7 @@ ││ │ └────────────────────────────── 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):

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:

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):

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.

- + \ No newline at end of file diff --git a/site/.vitepress/dist/modules/curriculum.html b/site/.vitepress/dist/modules/curriculum.html index b961a52..cac70ef 100644 --- a/site/.vitepress/dist/modules/curriculum.html +++ b/site/.vitepress/dist/modules/curriculum.html @@ -6,12 +6,12 @@ Curriculum | FDSA - + - + - + @@ -27,7 +27,7 @@

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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 5edd7e3..084c73c 100644 --- a/site/.vitepress/dist/modules/debate.html +++ b/site/.vitepress/dist/modules/debate.html @@ -6,12 +6,12 @@ The Great Agent Debate | FDSA - + - + - + @@ -27,7 +27,7 @@

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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 6d30562..b68e3d5 100644 --- a/site/.vitepress/dist/modules/feynman.html +++ b/site/.vitepress/dist/modules/feynman.html @@ -6,12 +6,12 @@ Feynman-Style Course: Core Concepts in Plain Language | FDSA - + - + - + @@ -30,7 +30,7 @@ 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:

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:

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:

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:

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.

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.

- + \ 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 165e3e0..4492476 100644 --- a/site/.vitepress/dist/modules/field-manual.html +++ b/site/.vitepress/dist/modules/field-manual.html @@ -6,12 +6,12 @@ Agentic Engineering — Technical Field Manual | FDSA - + - + - + @@ -75,7 +75,7 @@ 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 - + \ 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 e97404f..4503e68 100644 --- a/site/.vitepress/dist/modules/m1-foundations.html +++ b/site/.vitepress/dist/modules/m1-foundations.html @@ -6,12 +6,12 @@ Module 1: Foundations | FDSA - + - + - + @@ -88,7 +88,7 @@ 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?
- + \ 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 b3f2336..c68c04f 100644 --- a/site/.vitepress/dist/modules/m2-architecture.html +++ b/site/.vitepress/dist/modules/m2-architecture.html @@ -6,12 +6,12 @@ Module 2: Agent Architecture | FDSA - + - + - + @@ -100,7 +100,7 @@ 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

- + \ 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 7061432..a7c4f0b 100644 --- a/site/.vitepress/dist/modules/m3-safety.html +++ b/site/.vitepress/dist/modules/m3-safety.html @@ -6,12 +6,12 @@ Module 3: Safety & Security | FDSA - + - + - + @@ -100,7 +100,7 @@ # 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
- + \ 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 9ed6fb8..4c911bc 100644 --- a/site/.vitepress/dist/modules/m4-orchestration.html +++ b/site/.vitepress/dist/modules/m4-orchestration.html @@ -6,12 +6,12 @@ Module 4: Multi-Agent Orchestration | FDSA - + - + - + @@ -112,7 +112,7 @@ 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
- + \ 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 f14e550..501e13c 100644 --- a/site/.vitepress/dist/modules/m5-production.html +++ b/site/.vitepress/dist/modules/m5-production.html @@ -6,12 +6,12 @@ Module 5: Production Patterns | FDSA - + - + - + @@ -159,7 +159,7 @@ scope: "global" amount: 200 # $2/session max hard_stop: true

Warning vs Hard Stop ​

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/

- + \ 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 6811ef7..4a77ce7 100644 --- a/site/.vitepress/dist/modules/m6-economics.html +++ b/site/.vitepress/dist/modules/m6-economics.html @@ -6,12 +6,12 @@ Module 6: Economics & Evaluation | FDSA - + - + - + @@ -57,7 +57,7 @@ ]

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 ​

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:

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

- + \ 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 39580cd..7575063 100644 --- a/site/.vitepress/dist/modules/m7-advanced.html +++ b/site/.vitepress/dist/modules/m7-advanced.html @@ -6,12 +6,12 @@ Module 7: Advanced Topics | FDSA - + - + - + @@ -79,7 +79,7 @@ # Agent polls for sentinel pattern poll logs for "__DONE_abc123" # Extract exit code from sentinel

Lesson 7.6: Always-On Agents ​

Voice-to-Command Bridge ​

User speaks → STT (faster-whisper) → LLM transcribes to command → Execute → TTS response

Job Server Pattern ​

Submit → Queue → Worker picks up → Claude Code executes → Result stored → Check later

Heartbeat Execution (from Paperclip) ​

Timer fires → Check for queued work → Wake agent → Agent executes → Agent sleeps

No continuous running. Agents wake, work, and sleep on a schedule.

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

- + \ 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 4aa1fb2..09c1009 100644 --- a/site/.vitepress/dist/modules/m8-capstone.html +++ b/site/.vitepress/dist/modules/m8-capstone.html @@ -6,12 +6,12 @@ Module 8: Capstone — Production Multi-Agent System | FDSA - + - + - + @@ -52,7 +52,7 @@ [ ] 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
- + \ 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 94fe6c8..6505010 100644 --- a/site/.vitepress/dist/modules/non-technical.html +++ b/site/.vitepress/dist/modules/non-technical.html @@ -6,12 +6,12 @@ Non-Technical Track: Decision Frameworks for AI Agents | FDSA - + - + - + @@ -73,7 +73,7 @@ ├── <$50/month → Use cheapest model (Gemini Flash) ├── $50-500/month → Cascade routing (mix models) └── >$500/month → Multi-agent with verification - + \ 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 947ac27..dd6acaa 100644 --- a/site/.vitepress/dist/modules/reference-stack.html +++ b/site/.vitepress/dist/modules/reference-stack.html @@ -6,12 +6,12 @@ Reference Architecture: Production Multi-Agent Stack | FDSA - + - + - + @@ -88,7 +88,7 @@ # 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
- + \ 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 e6a4cf8..f64c98a 100644 --- a/site/.vitepress/dist/modules/software-factory.html +++ b/site/.vitepress/dist/modules/software-factory.html @@ -6,12 +6,12 @@ Software Factory: Paperclip Integration | FDSA - + - + - + @@ -32,7 +32,7 @@ (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.

- + \ 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 9f9a908..bfc84fa 100644 --- a/site/.vitepress/dist/modules/tool-reference.html +++ b/site/.vitepress/dist/modules/tool-reference.html @@ -6,12 +6,12 @@ Agent CLI Reference: 5-Tool Comparison | FDSA - + - + - + @@ -94,7 +94,7 @@ ├── Budget constrained → OpenCode + Go models ├── Maximum control → Pi Agent + extensions └── Enterprise rollout → Claude Code + Agent Manager - + \ No newline at end of file diff --git a/site/.vitepress/dist/public/certificate/template.html b/site/.vitepress/dist/public/certificate/template.html index 627b393..44a0cef 100644 --- a/site/.vitepress/dist/public/certificate/template.html +++ b/site/.vitepress/dist/public/certificate/template.html @@ -6,12 +6,12 @@ Certificate of Completion | FDSA - + - + - + @@ -27,7 +27,7 @@

Appearance

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 f1ccc00..429871b 100644 --- a/site/.vitepress/dist/resources.html +++ b/site/.vitepress/dist/resources.html @@ -6,12 +6,12 @@ Resources | FDSA - + - + - + @@ -31,7 +31,7 @@ # Mac/Linux bash install.sh - + \ No newline at end of file diff --git a/site/.vitepress/dist/skills.html b/site/.vitepress/dist/skills.html index 3f8991b..b35fe3e 100644 --- a/site/.vitepress/dist/skills.html +++ b/site/.vitepress/dist/skills.html @@ -6,12 +6,12 @@ Skill Kits | FDSA - + - + - + @@ -39,7 +39,7 @@ │ ├── observability/ (4 skills) │ └── ceo-board/ (11 agents) └── .claude-plugin/marketplace.json - + \ No newline at end of file diff --git a/site/.vitepress/dist/troubleshooting.html b/site/.vitepress/dist/troubleshooting.html index bf4f56a..bf11d37 100644 --- a/site/.vitepress/dist/troubleshooting.html +++ b/site/.vitepress/dist/troubleshooting.html @@ -6,12 +6,12 @@ Troubleshooting & FAQ | FDSA - + - + - + @@ -27,7 +27,7 @@

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 8bbedfa..3d1e545 100644 --- a/site/.vitepress/dist/verify.html +++ b/site/.vitepress/dist/verify.html @@ -6,12 +6,12 @@ Verify Certificate | FDSA - + - + - + @@ -27,7 +27,7 @@

Appearance

Verify Certificate ​

Enter the certificate ID to verify:

Certificate verification system coming soon. For now, contact artale@fdsa.agency to verify a certificate.

Back to home

Last updated:

FDSA Agency — Agentic Engineering Course. Part of the fdsa.ai orchestration platform.

Copyright 2026

- + \ No newline at end of file diff --git a/site/.vitepress/theme/LandingLayout.vue b/site/.vitepress/theme/LandingLayout.vue index 90a8e1e..3ee0b71 100644 --- a/site/.vitepress/theme/LandingLayout.vue +++ b/site/.vitepress/theme/LandingLayout.vue @@ -8,11 +8,10 @@ FDSA
- Course - Labs - Skills + Course + Labs Blog - Enroll + Enroll — $97
@@ -24,26 +23,50 @@
NOW ENROLLING
-

Agentic Engineering

-

65 lessons, 13 labs, 20 skill kits. Build, deploy, and maintain production-grade AI agent systems.

+

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 — $97 - View Curriculum + Start Learning Free
-
INITIALIZING AGENT_PLANNER...
-
FETCHING LLM CONTEXT: DONE
-
EXECUTING: search("agentic patterns")
-
ANALYZING RESULT_SET: SUCCESS
+
AGENT_PLANNER v2.1
+
> LOADING HARNESS... DONE
+
> DEPLOYING 8-AGENT SYSTEM
+
> SECURITY: LEVEL 4 (WHITELIST)
+
> ALL SYSTEMS OPERATIONAL
+ +
+
+
+ 65 + Lessons +
+
+ 13 + Hands-on Labs +
+
+ 8 + Modules +
+
+ 20 + Skill Kits +
+
+
+ -
-
Core Capabilities
+
+
What You'll Build
+

Production-Ready Agent Systems

{{ f.icon }}
@@ -54,9 +77,9 @@
-
-
Engineering Curriculum
-

8 Modules. 65 Lessons. Zero Fluff.

+
+
Curriculum
+

8 Modules. From Zero to Production.

{{ m.n }}
@@ -64,80 +87,143 @@

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Trusted By
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Full Course Access

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$97
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- Enroll Now -
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- diff --git a/site/.vitepress/theme/style.css b/site/.vitepress/theme/style.css index 4ea2935..8404fb8 100644 --- a/site/.vitepress/theme/style.css +++ b/site/.vitepress/theme/style.css @@ -262,35 +262,12 @@ body { color: var(--vp-c-text-3) !important; } -/* === CONTENT AREA — glass card wrap === */ -.VPContent .container { - padding-top: 24px !important; -} - -/* Main content card — applies to all VitePress doc pages */ -.vp-doc { - background: rgba(17,17,17,0.85) !important; - backdrop-filter: blur(12px) !important; - border: 1px solid rgba(255,255,255,0.06) !important; - border-top: 1px solid rgba(255,255,255,0.1) !important; - border-radius: 12px !important; - padding: 40px !important; - margin: 0 !important; -} - -/* Remove duplicate border from first h2 in content */ -.vp-doc h2:first-of-type { - border-top: none !important; - padding-top: 0 !important; - margin-top: 0 !important; -} - /* === RESPONSIVE === */ @media (max-width: 768px) { .vp-doc { padding: 24px 16px; } .vp-doc h1 { font-size: 1.75rem !important; } .vp-doc h2 { font-size: 1.125rem !important; } - .v-p-doc div[class*='language-'] { margin: 16px 0; } + .vp-doc div[class*='language-'] { margin: 16px 0; } } /* Print */