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Lesson 13: Multi-Agent Systems - CrewAI & Autogen

Topics Covered
  • When Single Agents Fail: The case for multi-agent architectures.
  • CrewAI: Role-Based Teams: Defining agents by role, goal, and backstory.
  • Autogen: Conversational Agents: Agents that talk to each other.
  • Comparing Patterns: Delegation vs conversation, when to use each.
  • Task Orchestration: Sequential, parallel, and hierarchical execution.
  • Inter-Agent Communication: Handoffs, context sharing, and conflict resolution.

A single agent with many tools eventually hits a wall: prompts become unwieldy, context windows overflow, and the agent loses focus. Multi-agent systems solve this by decomposition—specialized agents that collaborate on complex tasks. In this lesson, you'll explore two dominant paradigms: CrewAI's role-based teams and Microsoft Autogen's conversational agents.

Synopsis​

1. The Case for Multi-Agent Systems​

  • Single agent limitations: context overflow, conflicting instructions
  • The decomposition principle: specialists beat generalists
  • Real-world examples: research teams, editorial workflows, code review
  • When multi-agent is overkill (and when it's essential)
  • The coordination overhead trade-off

2. CrewAI: Role-Based Agent Teams​

  • The mental model: a startup with defined roles
  • Installing CrewAI
  • Core concepts: Agent, Task, Crew, Process
  • Defining agents with role, goal, and backstory
  • Why backstory matters for agent behavior

3. CrewAI Agents in Depth​

  • Agent configuration options
  • Assigning tools to specific agents
  • Memory and context sharing between agents
  • Verbose mode for debugging agent decisions
  • Agent delegation: allowing agents to assign work to others

4. CrewAI Tasks and Processes​

  • Defining tasks with descriptions and expected outputs
  • Task dependencies and ordering
  • Sequential process: one agent at a time
  • Hierarchical process: manager agent delegates
  • Custom process flows

5. Building a Content Creation Crew​

  • The team: Researcher, Writer, Editor
  • Defining each agent's role and tools
  • Task chain: research → write → edit
  • Running the crew and collecting outputs
  • Iterating on agent prompts for quality

6. Microsoft Autogen: Conversational Agents​

  • The mental model: a group chat with specialists
  • Installing Autogen
  • Core concepts: ConversableAgent, GroupChat, GroupChatManager
  • Agents that reply to each other
  • The conversation-driven workflow

7. Autogen Agent Types​

  • AssistantAgent: LLM-powered responders
  • UserProxyAgent: human-in-the-loop or auto-reply
  • Custom agents with specialized behavior
  • Code execution agents (running generated code)
  • Tool-using agents in Autogen

8. Autogen Group Dynamics​

  • GroupChat: managing multi-agent conversations
  • Speaker selection strategies (round-robin, auto, custom)
  • Termination conditions
  • Managing conversation length and context
  • Handling disagreements between agents

9. Building a Code Review System with Autogen​

  • The team: Developer, Reviewer, Tester
  • Conversation flow: code → review → tests → iteration
  • Code execution in sandboxed environments
  • Handling review feedback loops
  • When to terminate the conversation

10. CrewAI vs Autogen: Choosing the Right Tool​

  • CrewAI strengths: structured workflows, clear task boundaries
  • Autogen strengths: dynamic collaboration, emergent behavior
  • Hybrid approaches: combining both patterns
  • Performance and cost considerations
  • Community and ecosystem comparison

11. Common Multi-Agent Pitfalls​

  • Agents talking past each other
  • Infinite conversation loops
  • Context window exhaustion
  • Role confusion and overlap
  • Debugging multi-agent systems

Additional Resources​