Lesson 18: Capstone Project — Build a complete AI Agent Team

Build AI Agents: From Zero to Production

Part 6: Production & Actual Deployment

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Introduction

This is the summary — you will build a complete multi-agent system from A to Z, applying everything you have learned.

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1. Project: AI Research & Content Team

Architecture

User Request
    │
    ▼
┌─────────────┐
│  Supervisor │ (LangGraph)
│   Agent     │
└──────┬──────┘
       │
  ┌────┼────┐
  ▼    ▼    ▼
┌───┐┌───┐┌───┐
│ R ││ W ││ E │
│ e ││ r ││ d │
│ s ││ i ││ i │
│ e ││ t ││ t │
│ a ││ e ││ o │
│ r ││ r ││ r │
│ c │└───┘└───┘
│ h │
│ e │
│ r │──── MCP: Web Search
│   │──── MCP: Database
│   │──── RAG: Knowledge Base
└───┘

Components checklist

• Supervisor Agent (LangGraph orchestration)
• Research Agent (web search + RAG + memory)
• Writer Agent (content generation)
• Editor Agent (review + quality check)
• MCP Servers (web search, database)
• Memory system (short-term + long-term)
• Guardrails (input validation, output filtering)
• Observability (LangSmith tracing)
• FastAPI wrapper + WebSocket
• Docker deployment
• Evaluation suite (golden test cases)

2. Step-by-step Implementation

Phase 1: Core Agents (2 hours)

• Implement 3 agents with clear roles
• Define tool schemas
• Build basic orchestration

Phase 2: Infrastructure (1 hour)

• RAG knowledge base
• Memory system
• MCP server connections

Phase 3: Safety & Quality (30 minutes)

• Guardrails
• Observability
• Basic evaluation

Phase 4: Deployment (30 minutes)

• FastAPI wrapper
• Docker
• Deploy

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3. Best Practices Summary

Architecture

• Single Responsibility: each agent does one good thing
• Supervisor pattern for complex orchestrations
• Loose coupling: agents communicate via messages, not shared state

Safety

• Never trust user input
• Read-only tools by default
• Human-in-the-loop for critical actions
• Cost budgets

Performance

• Cache responses when possible
• Parallel tool execution
• Token budget management
• Model selection: cheap model for simple tasks

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🎉 Congratulations!

You have completed the Build AI Agents: From Zero to Production series! From here, you can:

1. Build agents for work: automate research, content, coding tasks
2. Contribute open-source: build MCP servers, agent tools
3. Build products: SaaS products powered by AI agents
4. Continue learning: explore OpenAI Swarm, AutoGen, DSPy

Final exercise

1. Complete the capstone project with all components
2. Deploy to the cloud and share the link
3. Write a blog post about experience building AI agents
4. Pick a real-world problem and build an agent to solve it