import{c as t,Q as a,j as i,m as s}from"./chunks/framework.BPKcPtvA.js";const u=JSON.parse('{"title":"Module 8: Capstone — Production Multi-Agent System","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m8-capstone.md","filePath":"modules/m8-capstone.md","lastUpdated":1780488246000}'),n={name:"modules/m8-capstone.md"};function r(l,e,o,d,h,p){return a(),i("div",null,[...e[0]||(e[0]=[s(`Module 8: Capstone — Production Multi-Agent System Overview Build, deploy, and evaluate a complete production-grade multi-agent system. Choose from three project options, or propose your own.
Project Options Build a multi-agent system that monitors how LLMs mention a brand across multiple AI services.
Agents :
Scanner Agent — queries ChatGPT, Gemini, Grok, Perplexity for brand mentionsAnalyzer Agent — classifies mentions as positive/negative/neutralReporter Agent — generates daily summary reportsRequirements :
Each agent has domain-locked permissions Each agent maintains a mental model Scanner uses cascade routing (Flash for scrape, Sonnet for analysis) Daily automated runs via heartbeat Cost tracking per scan A plan → build → review → verify chain with CI/CD integration.
Agents :
Planner — reads spec, creates implementation planBuilder — writes code per planReviewer — reviews code for bugs, style, securityVerifier — read-only agent that checks all claimsRequirements :
YAML-defined agent chain pipeline Golden dataset for regression testing Verifier agent with confidence ladder GitHub PR integration Cost analysis per review cycle Option 3: Strategic Decision Board (Advanced) An 8-agent CEO board with adversarial debate, fact verification, and execution planning.
Agents :
CEO (orchestrator) 6 board members with distinct perspectives Verifier (fact-checker) Tracker (quarterly review) Requirements :
Each board member has distinct persona + mental model Fact verification (2+ sources per claim) Contrarian position tracked at 3x weight Structured memo output Audio summary (TTS) Cost tracking per deliberation Capstone Phases Phase 1: System Design (60-90 min) Deliverable: One-page architecture document including:
System diagram (agents + data flow + tool surface) Agent roster (names, personas, domain permissions) Data model (what state is shared, how) Security architecture (what L-level, what hooks) Cost estimate per session Template :
markdown # System Design: [Project Name] ## Architecture [ASCII diagram or mermaid] ## Agent Roster | Agent | Model | Tools | Domain | Memory | |-------|-------|-------|--------|--------| ## Data Flow [How agents communicate, what state they share] ## Security [L-level, hooks, verifier, damage-control rules] ## Cost Estimate [Per-session estimate with model pricing] Phase 2: Agent Implementation (2-4 hours) Build each agent:
System prompt with persona + behavioral rules Tool definitions with reasoning parameter Mental model file (agent-owned expertise) Skills for composable behavior Phase 3: Integration (1-2 hours) Wire agents together:
Chain (if pipeline pattern) Team (if dispatcher pattern) P2P (if flat pattern) Shared context (conversation log) Phase 4: Security Hardening (1-2 hours) Apply security layer:
Minimum L3 (blacklist hook) Target L4 (whitelist hook) Optional L5 (no bash) Damage-control rules Verifier agent (read-only checks) Phase 5: Testing & Evaluation (1-2 hours) Create golden dataset (10+ test cases) Run pass@k evaluation (k=3, k=5) Calculate cost per task Identify optimization opportunities Phase 6: Deployment (1-2 hours) CI/CD pipeline with golden dataset gate Shadow deployment vs baseline Observability: every tool call + cost logged Monitoring dashboard Phase 7: Review & Iterate (1 hour) Red-team your own system (try to break it) Document lessons learned Create retrospective Deliverables Checklist [ ] Architecture document (one page, with diagram) [ ] All agent source code with system prompts [ ] Mental model files for each agent [ ] Security configuration (damage-control rules) [ ] Verifier agent (if applicable) [ ] Golden dataset (10+ test cases) [ ] Test results (pass@k at k=1, k=3, k=5) [ ] Cost analysis ($/task, optimization opportunities) [ ] Security audit (which L-level, what gaps remain) [ ] Retrospective (max 1 page) Pass Criteria Criterion Minimum Target 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 Area Weight Poor (0) Good (1) Excellent (2) Architecture 20% No diagram, unclear design Diagram present, mostly clear Clear diagram, justified choices Implementation 25% Agents don't work Agents work on happy path Agents handle errors gracefully Security 20% L1 only L3+ with damage-control L4+ with verifier Testing 15% No eval pass@k computed pass@k + cost analysis + grind detection Documentation 10% Minimal Architecture + setup Architecture + setup + retrospective Cost Optimization 10% Single model Cascade routing Cascade + verified savings
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