feat: filled gaps - instructor bio, email capture, free preview, deeper M7, better mock responses, realistic lab data

course/labs/L1-first-agent/test.txt

@@ -1,5 +1,17 @@
-This is a test file for Lab 1.7: Your First Agent.
+This is a sample log file from a production agent system running on 2026-05-15.
-It contains sample data for the agent to read and summarize.
+It contains session traces, tool calls, and timing data.
-Line 1: Hello world
+
-Line 2: The quick brown fox jumps over the lazy dog
+Session ID: sess_abc123
-Line 3: 42 is the answer to everything
+Date: 2026-05-15T14:30:00Z
+Model: claude-sonnet-4-20260501
+
+[14:30:01] AGENT INITIALIZED — Task: "Analyze the latest deployment log"
+[14:30:02] TOOL CALL: read_file(path="logs/deploy-2026-05-15.log")
+[14:30:03] TOOL RESULT: 847 bytes read
+[14:30:04] TOOL CALL: grep_search(pattern="ERROR|FAIL|CRITICAL")
+[14:30:05] TOOL RESULT: 3 matches found
+[14:30:06] TOOL CALL: read_file(path="logs/deploy-2026-05-15.log:42:50")
+[14:30:07] TOOL RESULT: 9 lines read
+[14:30:08] ANALYSIS: Found 2 warnings, 1 critical error
+[14:30:09] FINAL ANSWER: Deployment has a database connection issue
+[14:30:10] SESSION END — Total time: 9.2s, Total cost: $0.042

course/labs/L2-multi-tool/test.txt

@@ -0,0 +1,26 @@
+{
+  "app_name": "AgentMonitor",
+  "version": "2.4.1",
+  "environment": "production",
+  "features": {
+    "dashboard": true,
+    "alerts": true,
+    "scheduling": false,
+    "multi_tenant": true
+  },
+  "database": {
+    "host": "db-primary-01.example.com",
+    "port": 5432,
+    "pool_size": 20,
+    "timeout_ms": 5000
+  },
+  "api_keys": [
+    { "service": "openai", "key": "sk-...prod...", "quota": 1000000 },
+    { "service": "anthropic", "key": "sk-ant-...prod...", "quota": 500000 }
+  ],
+  "logging": {
+    "level": "INFO",
+    "format": "json",
+    "sink": "cloudwatch"
+  }
+}

course/labs/_shared/mock_llm.py

@@ -133,9 +133,17 @@ def _generate_text_response(prompt: str) -> str:
     
     prompt_lower = prompt.lower()
     
-    # File content responses
+    # Agent session log responses
+    if "session" in prompt_lower and ("agent" in prompt_lower or "tool call" in prompt_lower):
+        return "I've analyzed the session log. Here's what I found:\n\n- The agent completed 3 tool calls successfully\n- 1 critical error was detected (database connection timeout)\n- 2 warnings (deprecated API usage)\n- Total session cost: $0.042\n- Recommendation: Check the database connection pool settings"
+    
+    # File content responses - extract what the file is about
     if "contents of" in prompt_lower:
-        return "Based on the file contents, I can see the document contains sample data. The key information includes test records and example data points. What specific aspect would you like me to analyze?"
+        if "error" in prompt_lower or "FAIL" in prompt_lower:
+            return "I've analyzed the file. It contains session trace data from a production agent deployment. There are signs of a database connection issue that needs investigation."
+        if "deploy" in prompt_lower or "log" in prompt_lower:
+            return "I've reviewed the deployment log. The system ran 3 tool calls successfully with one critical error. The root cause appears to be a database timeout."
+        return "Based on the file contents, I can see it contains sample data with records and configuration details. The key patterns I identified are structured around standard formats."
     
     # Search result responses  
     if "search" in prompt_lower and ("result" in prompt_lower or "found" in prompt_lower):

site/.vitepress/dist/assets/app.DEGNa0gx.js

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site/.vitepress/dist/assets/free-preview.md.D4lOIST7.js

@@ -0,0 +1,3 @@
+import{c as e,Q as a,j as s,m as n}from"./chunks/framework.BPKcPtvA.js";const p=JSON.parse('{"title":"Free Preview: Lesson 1.1 — What Makes an Agent?","description":"","frontmatter":{},"headers":[],"relativePath":"free-preview.md","filePath":"free-preview.md","lastUpdated":null}'),o={name:"free-preview.md"};function i(r,t,d,l,h,c){return a(),s("div",null,[...t[0]||(t[0]=[n(`<h1 id="free-preview-lesson-1-1-—-what-makes-an-agent" tabindex="-1">Free Preview: Lesson 1.1 — What Makes an Agent? <a class="header-anchor" href="#free-preview-lesson-1-1-—-what-makes-an-agent" aria-label="Permalink to &quot;Free Preview: Lesson 1.1 — What Makes an Agent?&quot;">​</a></h1><p>This is a sample lesson from <strong>Module 1: Foundations</strong> of the FDSA Agentic Engineering Course. Full course includes 65 lessons, 13 labs, and 20 skill kits.</p><hr><h2 id="lesson-1-1-what-makes-an-agent" tabindex="-1">Lesson 1.1: What Makes an Agent? <a class="header-anchor" href="#lesson-1-1-what-makes-an-agent" aria-label="Permalink to &quot;Lesson 1.1: What Makes an Agent?&quot;">​</a></h2><p><strong>Definition</strong>: An AI agent = LLM + Tools + Loop. Without any one of these three, it&#39;s not an agent.</p><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>Agent = LLM (reasoning engine)</span></span>
+<span class="line"><span>      + Tools (capability surface)</span></span>
+<span class="line"><span>      + Loop (autonomous decision cycle)</span></span></code></pre></div><ul><li>A single LLM call with no tools = chatbot</li><li>An LLM with tools but no loop = augmented inference</li><li>Tools + loop + LLM = agent (it can decide what to do next)</li></ul><h3 id="the-three-components" tabindex="-1">The Three Components <a class="header-anchor" href="#the-three-components" aria-label="Permalink to &quot;The Three Components&quot;">​</a></h3><p><strong>LLM</strong> — The reasoning engine. Given context + available tools, it decides which tool to call and with what parameters. The LLM is NOT the agent — it&#39;s the brain of the agent. Different models have different reasoning capabilities, but the core function is the same: given a situation and available actions, decide what to do.</p><p><strong>Tools</strong> — The capability surface. Functions the agent can call: read files, run commands, search the web, query databases, call APIs. Each tool has a name, description, and input schema. The tool surface defines what the agent CAN do — everything outside this surface is something the agent cannot do, no matter how smart the LLM is.</p><p><strong>Loop</strong> — The autonomous decision cycle. Think (LLM decides) → Act (tool executes) → Observe (result comes back) → Repeat. The loop is what makes it autonomous. Without a loop, you have a single decision. With a loop, you have an agent that can work toward a goal across multiple steps.</p><h3 id="why-this-matters" tabindex="-1">Why This Matters <a class="header-anchor" href="#why-this-matters" aria-label="Permalink to &quot;Why This Matters&quot;">​</a></h3><p>This definition is not academic. Every production agent failure I&#39;ve seen traces back to one of these three:</p><table tabindex="0"><thead><tr><th>Failure</th><th>Root Cause</th></tr></thead><tbody><tr><td>Agent does something unexpected</td><td>Loop didn&#39;t terminate correctly</td></tr><tr><td>Agent can&#39;t do the task</td><td>Tools are insufficient for the task</td></tr><tr><td>Agent makes bad decisions</td><td>LLM doesn&#39;t have enough context</td></tr><tr><td>Agent costs too much</td><td>Loop runs too many iterations</td></tr></tbody></table><p>If you understand these three components and how they interact, you can debug any agent system. If you don&#39;t, you&#39;re guessing.</p><hr><h2 id="what-you-ll-learn-in-the-full-course" tabindex="-1">What You&#39;ll Learn in the Full Course <a class="header-anchor" href="#what-you-ll-learn-in-the-full-course" aria-label="Permalink to &quot;What You&#39;ll Learn in the Full Course&quot;">​</a></h2><table tabindex="0"><thead><tr><th>Module</th><th>Topic</th><th>Lessons</th><th>Labs</th></tr></thead><tbody><tr><td>M1</td><td>Foundations</td><td>8 lessons</td><td>1 lab</td></tr><tr><td>M2</td><td>Agent Architecture</td><td>7 lessons</td><td>2 labs</td></tr><tr><td>M3</td><td>Safety &amp; Security</td><td>7 lessons</td><td>2 labs</td></tr><tr><td>M4</td><td>Multi-Agent Orchestration</td><td>11 lessons</td><td>2 labs</td></tr><tr><td>M5</td><td>Production Systems</td><td>10 lessons</td><td>2 labs</td></tr><tr><td>M6</td><td>Model Economics</td><td>7 lessons</td><td>2 labs</td></tr><tr><td>M7</td><td>Advanced Patterns</td><td>8 lessons</td><td>2 labs</td></tr><tr><td>M8</td><td>Capstone Project</td><td>—</td><td>Build &amp; deploy</td></tr></tbody></table><p><a href="/buy">Enroll Now — $97</a> · <a href="/modules/curriculum">View Full Curriculum</a></p><p><em>Note: This preview shows approximately 30% of a single lesson. Full lessons include code examples, diagrams, quiz questions, and lab exercises.</em></p>`,20)])])}const g=e(o,[["render",i]]);export{p as __pageData,g as default};

site/.vitepress/dist/assets/free-preview.md.D4lOIST7.lean.js

@@ -0,0 +1 @@
+import{c as e,Q as a,j as s,m as n}from"./chunks/framework.BPKcPtvA.js";const p=JSON.parse('{"title":"Free Preview: Lesson 1.1 — What Makes an Agent?","description":"","frontmatter":{},"headers":[],"relativePath":"free-preview.md","filePath":"free-preview.md","lastUpdated":null}'),o={name:"free-preview.md"};function i(r,t,d,l,h,c){return a(),s("div",null,[...t[0]||(t[0]=[n("",20)])])}const g=e(o,[["render",i]]);export{p as __pageData,g as default};

site/.vitepress/dist/assets/modules_m7-advanced.md.DoGMJ54l.js

@@ -1,4 +1,4 @@
-import{c as a,Q as i,j as e,m as t}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Module 7: Advanced Topics","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m7-advanced.md","filePath":"modules/m7-advanced.md","lastUpdated":1780488246000}'),n={name:"modules/m7-advanced.md"};function l(p,s,h,r,o,d){return i(),e("div",null,[...s[0]||(s[0]=[t(`<h1 id="module-7-advanced-topics" tabindex="-1">Module 7: Advanced Topics <a class="header-anchor" href="#module-7-advanced-topics" aria-label="Permalink to &quot;Module 7: Advanced Topics&quot;">​</a></h1><h2 id="lesson-7-1-autoresearch" tabindex="-1">Lesson 7.1: Autoresearch <a class="header-anchor" href="#lesson-7-1-autoresearch" aria-label="Permalink to &quot;Lesson 7.1: Autoresearch&quot;">​</a></h2><h3 id="the-core-loop" tabindex="-1">The Core Loop <a class="header-anchor" href="#the-core-loop" aria-label="Permalink to &quot;The Core Loop&quot;">​</a></h3><p>Agents that improve themselves. The experiment loop:</p><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>1. Run: Execute the agent with current configuration</span></span>
+import{c as a,Q as i,j as e,m as t}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Module 7: Advanced Topics","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m7-advanced.md","filePath":"modules/m7-advanced.md","lastUpdated":1780488246000}'),n={name:"modules/m7-advanced.md"};function l(h,s,p,r,o,d){return i(),e("div",null,[...s[0]||(s[0]=[t(`<h1 id="module-7-advanced-topics" tabindex="-1">Module 7: Advanced Topics <a class="header-anchor" href="#module-7-advanced-topics" aria-label="Permalink to &quot;Module 7: Advanced Topics&quot;">​</a></h1><h2 id="lesson-7-1-autoresearch" tabindex="-1">Lesson 7.1: Autoresearch <a class="header-anchor" href="#lesson-7-1-autoresearch" aria-label="Permalink to &quot;Lesson 7.1: Autoresearch&quot;">​</a></h2><h3 id="the-core-loop" tabindex="-1">The Core Loop <a class="header-anchor" href="#the-core-loop" aria-label="Permalink to &quot;The Core Loop&quot;">​</a></h3><p>Agents that improve themselves. The experiment loop:</p><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>1. Run: Execute the agent with current configuration</span></span>
 <span class="line"><span>2. Measure: Collect metrics (latency, cost, success rate)</span></span>
 <span class="line"><span>3. Log: Record experiment results to JSONL</span></span>
 <span class="line"><span>4. Decide: Keep if improvement, discard if regression</span></span>
@@ -50,4 +50,11 @@ import{c as a,Q as i,j as e,m as t}from"./chunks/framework.BPKcPtvA.js";const c=
 <span class="line"></span>
 <span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># Agent polls for sentinel pattern</span></span>
 <span class="line"><span style="--shiki-light:#6F42C1;--shiki-dark:#B392F0;">poll</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;"> logs</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;"> for</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;"> &quot;__DONE_abc123&quot;</span></span>
-<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># Extract exit code from sentinel</span></span></code></pre></div><hr><h2 id="lesson-7-6-always-on-agents" tabindex="-1">Lesson 7.6: Always-On Agents <a class="header-anchor" href="#lesson-7-6-always-on-agents" aria-label="Permalink to &quot;Lesson 7.6: Always-On Agents&quot;">​</a></h2><h3 id="voice-to-command-bridge" tabindex="-1">Voice-to-Command Bridge <a class="header-anchor" href="#voice-to-command-bridge" aria-label="Permalink to &quot;Voice-to-Command Bridge&quot;">​</a></h3><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>User speaks → STT (faster-whisper) → LLM transcribes to command → Execute → TTS response</span></span></code></pre></div><h3 id="job-server-pattern" tabindex="-1">Job Server Pattern <a class="header-anchor" href="#job-server-pattern" aria-label="Permalink to &quot;Job Server Pattern&quot;">​</a></h3><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>Submit → Queue → Worker picks up → Claude Code executes → Result stored → Check later</span></span></code></pre></div><h3 id="heartbeat-execution-from-paperclip" tabindex="-1">Heartbeat Execution (from Paperclip) <a class="header-anchor" href="#heartbeat-execution-from-paperclip" aria-label="Permalink to &quot;Heartbeat Execution (from Paperclip)&quot;">​</a></h3><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>Timer fires → Check for queued work → Wake agent → Agent executes → Agent sleeps</span></span></code></pre></div><p>No continuous running. Agents wake, work, and sleep on a schedule.</p><hr><h2 id="lab-7-7-build-an-autoresearch-loop" tabindex="-1">Lab 7.7: Build an Autoresearch Loop <a class="header-anchor" href="#lab-7-7-build-an-autoresearch-loop" aria-label="Permalink to &quot;Lab 7.7: Build an Autoresearch Loop&quot;">​</a></h2><p><strong>Objective</strong>: Agent runs experiment, measures result, logs it, decides keep/discard.</p><p><strong>Starter</strong>: <code>course/labs/L7-autoresearch/starter.py</code></p><p><strong>Checkpoints</strong>:</p><ol><li>Run code, measure baseline metric</li><li>Modify code (agent makes change)</li><li>Re-measure, compare, log</li><li>Discard if regression, keep if improvement</li><li>Include integrity guard (code hashing)</li></ol><h2 id="lab-7-8-meta-agent" tabindex="-1">Lab 7.8: Meta-Agent <a class="header-anchor" href="#lab-7-8-meta-agent" aria-label="Permalink to &quot;Lab 7.8: Meta-Agent&quot;">​</a></h2><p><strong>Objective</strong>: Agent generates a new agent persona from documentation.</p><p><strong>Starter</strong>: <code>course/labs/L7-meta-agent/starter.py</code></p>`,52)])])}const g=a(n,[["render",l]]);export{c as __pageData,g as default};
+<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># Extract exit code from sentinel</span></span></code></pre></div><hr><h2 id="lesson-7-6-always-on-agents" tabindex="-1">Lesson 7.6: Always-On Agents <a class="header-anchor" href="#lesson-7-6-always-on-agents" aria-label="Permalink to &quot;Lesson 7.6: Always-On Agents&quot;">​</a></h2><h3 id="voice-to-command-bridge" tabindex="-1">Voice-to-Command Bridge <a class="header-anchor" href="#voice-to-command-bridge" aria-label="Permalink to &quot;Voice-to-Command Bridge&quot;">​</a></h3><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>User speaks → STT (faster-whisper) → LLM transcribes to command → Execute → TTS response</span></span></code></pre></div><h3 id="job-server-pattern" tabindex="-1">Job Server Pattern <a class="header-anchor" href="#job-server-pattern" aria-label="Permalink to &quot;Job Server Pattern&quot;">​</a></h3><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>Submit → Queue → Worker picks up → Claude Code executes → Result stored → Check later</span></span></code></pre></div><h3 id="heartbeat-execution-from-paperclip" tabindex="-1">Heartbeat Execution (from Paperclip) <a class="header-anchor" href="#heartbeat-execution-from-paperclip" aria-label="Permalink to &quot;Heartbeat Execution (from Paperclip)&quot;">​</a></h3><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>Timer fires → Check for queued work → Wake agent → Agent executes → Agent sleeps</span></span></code></pre></div><p>No continuous running. Agents wake, work, and sleep on a schedule.</p><h3 id="scheduling-strategies" tabindex="-1">Scheduling Strategies <a class="header-anchor" href="#scheduling-strategies" aria-label="Permalink to &quot;Scheduling Strategies&quot;">​</a></h3><table tabindex="0"><thead><tr><th>Strategy</th><th>Pattern</th><th>Use Case</th></tr></thead><tbody><tr><td>Fixed interval</td><td>Every N minutes/hours/days</td><td>Brand monitoring, price tracking</td></tr><tr><td>Event-triggered</td><td>Webhook → wake agent</td><td>CI/CD pipelines, PR reviews</td></tr><tr><td>Predictive</td><td>Learn optimal check times</td><td>Traffic monitoring, anomaly detection</td></tr><tr><td>Cascade</td><td>Agent A finishes → Agent B starts</td><td>Data pipeline, ETL workflows</td></tr></tbody></table><h3 id="openclaw-the-always-on-employee" tabindex="-1">OpenClaw: The Always-On Employee <a class="header-anchor" href="#openclaw-the-always-on-employee" aria-label="Permalink to &quot;OpenClaw: The Always-On Employee&quot;">​</a></h3><p>OpenClaw is a daemon-style agent that runs as a background service:</p><div class="language-yaml vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang">yaml</span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># openclaw-config.yaml</span></span>
+<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">agent</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">:</span></span>
+<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  name</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;brand-monitor&quot;</span></span>
+<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  heartbeat</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">300</span><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;">  # every 5 minutes</span></span>
+<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  max_sessions</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">1</span></span>
+<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  wake_command</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;claude -p &#39;Check brand mentions since last run&#39;&quot;</span></span>
+<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  sleep_command</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;pkill -f &#39;claude.*brand-monitor&#39;&quot;</span></span>
+<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  log_path</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;/var/log/openclaw/brand-monitor.log&quot;</span></span></code></pre></div><p>It starts, checks for work, executes if needed, then goes back to sleep. No continuous billing, no context window overflow, no runaway loops. This is the pattern for production always-on agents.</p><h3 id="when-not-to-use-always-on" tabindex="-1">When NOT to Use Always-On <a class="header-anchor" href="#when-not-to-use-always-on" aria-label="Permalink to &quot;When NOT to Use Always-On&quot;">​</a></h3><p>Always-on adds complexity. Before building one, verify you actually need it:</p><ul><li><strong>Batch jobs</strong> → Cron is simpler and cheaper</li><li><strong>Real-time</strong> → Event-driven architectures (webhooks, Kafka) outperform polling agents</li><li><strong>One-off</strong> → Just run the agent once</li></ul><p>Always-on makes sense when you need adaptive scheduling, dynamic task generation, or autonomous decision-making about what to work on next.</p><hr><h2 id="lab-7-7-build-an-autoresearch-loop" tabindex="-1">Lab 7.7: Build an Autoresearch Loop <a class="header-anchor" href="#lab-7-7-build-an-autoresearch-loop" aria-label="Permalink to &quot;Lab 7.7: Build an Autoresearch Loop&quot;">​</a></h2><p><strong>Objective</strong>: Agent runs experiment, measures result, logs it, decides keep/discard.</p><p><strong>Starter</strong>: <code>course/labs/L7-autoresearch/starter.py</code></p><p><strong>Checkpoints</strong>:</p><ol><li>Run code, measure baseline metric</li><li>Modify code (agent makes change)</li><li>Re-measure, compare, log</li><li>Discard if regression, keep if improvement</li><li>Include integrity guard (code hashing)</li></ol><h2 id="lab-7-8-meta-agent" tabindex="-1">Lab 7.8: Meta-Agent <a class="header-anchor" href="#lab-7-8-meta-agent" aria-label="Permalink to &quot;Lab 7.8: Meta-Agent&quot;">​</a></h2><p><strong>Objective</strong>: Agent generates a new agent persona from documentation.</p><p><strong>Starter</strong>: <code>course/labs/L7-meta-agent/starter.py</code></p>`,62)])])}const g=a(n,[["render",l]]);export{c as __pageData,g as default};

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+import{c as a,Q as i,j as e,m as t}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Module 7: Advanced Topics","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m7-advanced.md","filePath":"modules/m7-advanced.md","lastUpdated":1780488246000}'),n={name:"modules/m7-advanced.md"};function l(h,s,p,r,o,d){return i(),e("div",null,[...s[0]||(s[0]=[t("",62)])])}const g=a(n,[["render",l]]);export{c as __pageData,g as default};

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site/.vitepress/theme/LandingLayout.vue

@@ -9,6 +9,7 @@
         </div>
         <div class="nav-links">
           <a href="/modules/curriculum">Course</a>
+          <a href="/free-preview">Preview</a>
           <a href="/labs/index">Labs</a>
           <a href="/blog">Blog</a>
           <a href="/buy" class="nav-cta">Enroll — $97</a>
@@ -27,7 +28,8 @@
           <p class="hero-sub">65 lessons, 13 labs, 20 skill kits. Build production-grade multi-agent systems that are secure, cost-optimized, and always-on.</p>
           <div class="hero-actions">
             <a href="/buy" class="btn-p btn-primary">Enroll Now — $97</a>
-            <a href="/modules/m1-foundations" class="btn-p btn-ghost">Start Learning Free</a>
+            <a href="/free-preview" class="btn-p btn-ghost">Free Preview</a>
+            <a href="/modules/m1-foundations" class="btn-p btn-ghost">View Curriculum</a>
           </div>
         </div>
         <div class="term-widget">
@@ -109,6 +111,37 @@
         </div>
       </section>
 
+      <!-- Instructor -->
+      <section class="sec" id="instructor">
+        <div class="sec-label">Your Instructor</div>
+        <h2 class="sec-title">Built by an Engineer, For Engineers</h2>
+        <div class="instructor-grid">
+          <div class="instructor-card">
+            <div class="instructor-avatar">
+              <span class="material-symbols-outlined">terminal</span>
+            </div>
+            <div class="instructor-info">
+              <h3>Artale</h3>
+              <p class="instructor-role">Founder, FDSA Agency</p>
+              <p class="instructor-bio">I've spent the last two years building production multi-agent systems — from <strong>CEO Boards</strong> for strategic decision-making to <strong>Brand Monitor</strong> for always-on reputation tracking, to <strong>UI Agents</strong> that generate production Vue components. This course distills everything I learned shipping real agent systems, not toy demos.</p>
+              <p class="instructor-bio">Every pattern in this course — the 6-level security ladder, P-threads, cascade routing, the verifier pattern — came from building systems that had to work reliably in production. If it's in the course, it's been battle-tested.</p>
+            </div>
+          </div>
+        </div>
+      </section>
+
+      <!-- Email Capture -->
+      <section class="sec" id="newsletter">
+        <div class="sec-label">Stay Updated</div>
+        <h2 class="sec-title">Get Free Agent Engineering Resources</h2>
+        <p class="newsletter-sub">Weekly essays on multi-agent systems, security patterns, and production deployments. No spam, unsubscribe anytime.</p>
+        <form class="newsletter-form" action="https://gumroad.com" method="get" target="_blank">
+          <input type="email" class="newsletter-input" placeholder="you@example.com" required disabled value="Join waitlist — coming soon">
+          <button type="submit" class="btn-p btn-primary" disabled>Subscribe</button>
+        </form>
+        <p class="newsletter-note">Newsletter launching soon. Follow <a href="https://github.com/Artale" target="_blank">@Artale</a> for updates.</p>
+      </section>
+
       <!-- Why FDSA -->
       <section class="sec" id="why-fdsa">
         <div class="sec-label">Compared To</div>
@@ -382,6 +415,29 @@ onMounted(() => {
 .compare-cons .material-symbols-outlined{color:#ef4444}
 .compare-cons{color:var(--vp-c-text-2)}
 
+/* Instructor */
+.instructor-grid{max-width:720px;margin:0 auto}
+.instructor-card{display:flex;gap:32px;align-items:flex-start;background:var(--vp-c-bg-soft);backdrop-filter:blur(12px);border:1px solid rgba(128,128,128,0.08);border-radius:16px;padding:36px}
+.instructor-avatar{width:72px;height:72px;border-radius:50%;background:rgba(217,92,65,0.12);display:flex;align-items:center;justify-content:center;flex-shrink:0}
+.instructor-avatar .material-symbols-outlined{font-size:32px;color:var(--vp-c-brand-1)}
+.instructor-info{flex:1}
+.instructor-info h3{font-size:20px;font-weight:700;color:var(--vp-c-text-1);margin-bottom:2px}
+.instructor-role{font-size:13px;color:var(--vp-c-text-2);margin-bottom:16px}
+.instructor-bio{font-size:14px;line-height:1.7;color:var(--vp-c-text-2);margin-bottom:12px}
+.instructor-bio:last-child{margin-bottom:0}
+.instructor-bio strong{color:var(--vp-c-text-1)}
+
+/* Newsletter */
+.newsletter-sub{text-align:center;color:var(--vp-c-text-2);font-size:15px;margin-bottom:24px;max-width:480px;margin-left:auto;margin-right:auto}
+.newsletter-form{display:flex;gap:12px;max-width:440px;margin:0 auto;justify-content:center}
+.newsletter-input{flex:1;padding:12px 16px;border-radius:8px;border:1px solid rgba(128,128,128,0.2);background:var(--vp-c-bg-soft);color:var(--vp-c-text-1);font-family:inherit;font-size:14px;outline:none;transition:border .2s}
+.newsletter-input:focus{border-color:var(--vp-c-brand-1)}
+.newsletter-input:disabled{opacity:0.5;cursor:not-allowed}
+.newsletter-form .btn-p{padding:12px 24px;font-size:14px;border:none;cursor:pointer}
+.newsletter-form .btn-p:disabled{opacity:0.5;cursor:not-allowed}
+.newsletter-note{text-align:center;font-size:12px;color:var(--vp-c-text-3);margin-top:16px}
+.newsletter-note a{color:var(--vp-c-brand-1)}
+
 /* Pricing */
 .pricing-grid{display:grid;grid-template-columns:repeat(3,1fr);gap:16px;max-width:960px;margin:0 auto}
 .price-card{background:var(--vp-c-bg-soft);backdrop-filter:blur(12px);border:1px solid rgba(128,128,128,0.08);border-radius:12px;padding:32px 24px;text-align:center;transition:all .3s;display:flex;flex-direction:column}

site/free-preview.md

@@ -0,0 +1,59 @@
+# Free Preview: Lesson 1.1 — What Makes an Agent?
+
+This is a sample lesson from **Module 1: Foundations** of the FDSA Agentic Engineering Course. Full course includes 65 lessons, 13 labs, and 20 skill kits.
+
+---
+
+## Lesson 1.1: What Makes an Agent?
+
+**Definition**: An AI agent = LLM + Tools + Loop. Without any one of these three, it's not an agent.
+
+```
+Agent = LLM (reasoning engine)
+      + Tools (capability surface)
+      + Loop (autonomous decision cycle)
+```
+
+- A single LLM call with no tools = chatbot
+- An LLM with tools but no loop = augmented inference
+- Tools + loop + LLM = agent (it can decide what to do next)
+
+### The Three Components
+
+**LLM** — The reasoning engine. Given context + available tools, it decides which tool to call and with what parameters. The LLM is NOT the agent — it's the brain of the agent. Different models have different reasoning capabilities, but the core function is the same: given a situation and available actions, decide what to do.
+
+**Tools** — The capability surface. Functions the agent can call: read files, run commands, search the web, query databases, call APIs. Each tool has a name, description, and input schema. The tool surface defines what the agent CAN do — everything outside this surface is something the agent cannot do, no matter how smart the LLM is.
+
+**Loop** — The autonomous decision cycle. Think (LLM decides) → Act (tool executes) → Observe (result comes back) → Repeat. The loop is what makes it autonomous. Without a loop, you have a single decision. With a loop, you have an agent that can work toward a goal across multiple steps.
+
+### Why This Matters
+
+This definition is not academic. Every production agent failure I've seen traces back to one of these three:
+
+| Failure | Root Cause |
+|---------|-----------|
+| Agent does something unexpected | Loop didn't terminate correctly |
+| Agent can't do the task | Tools are insufficient for the task |
+| Agent makes bad decisions | LLM doesn't have enough context |
+| Agent costs too much | Loop runs too many iterations |
+
+If you understand these three components and how they interact, you can debug any agent system. If you don't, you're guessing.
+
+---
+
+## What You'll Learn in the Full Course
+
+| Module | Topic | Lessons | Labs |
+|--------|-------|---------|------|
+| M1 | Foundations | 8 lessons | 1 lab |
+| M2 | Agent Architecture | 7 lessons | 2 labs |
+| M3 | Safety & Security | 7 lessons | 2 labs |
+| M4 | Multi-Agent Orchestration | 11 lessons | 2 labs |
+| M5 | Production Systems | 10 lessons | 2 labs |
+| M6 | Model Economics | 7 lessons | 2 labs |
+| M7 | Advanced Patterns | 8 lessons | 2 labs |
+| M8 | Capstone Project | — | Build & deploy |
+
+[Enroll Now — $97](/buy) · [View Full Curriculum](/modules/curriculum)
+
+*Note: This preview shows approximately 30% of a single lesson. Full lessons include code examples, diagrams, quiz questions, and lab exercises.*

site/modules/m7-advanced.md

@@ -165,6 +165,41 @@ Timer fires → Check for queued work → Wake agent → Agent executes → Agen
 
 No continuous running. Agents wake, work, and sleep on a schedule.
 
+### Scheduling Strategies
+
+| Strategy | Pattern | Use Case |
+|----------|---------|----------|
+| Fixed interval | Every N minutes/hours/days | Brand monitoring, price tracking |
+| Event-triggered | Webhook → wake agent | CI/CD pipelines, PR reviews |
+| Predictive | Learn optimal check times | Traffic monitoring, anomaly detection |
+| Cascade | Agent A finishes → Agent B starts | Data pipeline, ETL workflows |
+
+### OpenClaw: The Always-On Employee
+
+OpenClaw is a daemon-style agent that runs as a background service:
+
+```yaml
+# openclaw-config.yaml
+agent:
+  name: "brand-monitor"
+  heartbeat: 300  # every 5 minutes
+  max_sessions: 1
+  wake_command: "claude -p 'Check brand mentions since last run'"
+  sleep_command: "pkill -f 'claude.*brand-monitor'"
+  log_path: "/var/log/openclaw/brand-monitor.log"
+```
+
+It starts, checks for work, executes if needed, then goes back to sleep. No continuous billing, no context window overflow, no runaway loops. This is the pattern for production always-on agents.
+
+### When NOT to Use Always-On
+
+Always-on adds complexity. Before building one, verify you actually need it:
+- **Batch jobs** → Cron is simpler and cheaper
+- **Real-time** → Event-driven architectures (webhooks, Kafka) outperform polling agents
+- **One-off** → Just run the agent once
+
+Always-on makes sense when you need adaptive scheduling, dynamic task generation, or autonomous decision-making about what to work on next.
+
 ---
 
 ## Lab 7.7: Build an Autoresearch Loop