Module 07: Multi-Agent Systems
A single agent has a single context window, a single thread of execution, and a single perspective. Most real-world problems are bigger than that.
Multi-agent systems solve this by coordinating multiple specialized agents on a shared task. The orchestrator decides what to do. Subagents execute. Critics verify. Researchers gather. Writers synthesize. And the result is qualitatively better than any single agent could produce.
This module covers the full arc: why multi-agent systems exist, how they communicate, how they parallelize work, how they verify each other's outputs, and how to build them with the four most important frameworks in production today.
What You'll Learn
Lessons in This Module
| # | Lesson | Core Idea |
|---|---|---|
| 01 | Why Multi-Agent? | Parallelization + specialization + verification |
| 02 | Orchestrator-Subagent Pattern | The most reliable coordination pattern |
| 03 | Agent Communication Protocols | How agents pass information reliably |
| 04 | Parallel Agent Execution | Running agents concurrently with asyncio |
| 05 | Debate and Critique Patterns | LLMs that critique each other produce better output |
| 06 | OpenAI Swarm | Handoffs, context variables, triage patterns |
| 07 | AutoGen Deep Dive | Conversational multi-agent with Microsoft AutoGen |
| 08 | CrewAI | Role-based agents, tasks, sequential/hierarchical process |
| 09 | LangGraph | Graph-based orchestration with full state control |
The Core Problem This Module Solves
Single agents fail on complex tasks for three reasons:
- Context limits - A task requiring 200,000 tokens of working memory doesn't fit in one context window
- Sequential bottleneck - Tasks that could run in parallel run slowly in sequence
- No verification - An agent can't reliably catch its own errors
Multi-agent systems address all three. But they introduce new problems: coordination overhead, error propagation, communication failures, and emergent behavior that's hard to debug.
This module teaches you to navigate both sides - capturing the power while managing the complexity.
Framework Landscape
By the end of this module, you'll know when to use each of these:
OpenAI Swarm - Lightweight, educational, great for understanding handoff primitives. Not production-ready.
AutoGen - Conversational multi-agent. Best for tasks that genuinely benefit from dialogue between agents. Microsoft-backed, v0.4 is async-first.
CrewAI - Role-based crews with sequential/hierarchical process. Most production-friendly abstraction. Maps to how teams actually work.
LangGraph - Graph-based orchestration with fine-grained state control. Most powerful, most complex. Best for production systems requiring deterministic flows, checkpointing, and human-in-the-loop.
Prerequisites
- Module 04 (Tool Use) - agents calling tools is the foundation
- Module 05 (Planning) - orchestrators are planners
- Module 06 (Memory) - shared state between agents is a memory problem
What You'll Build
By the end of this module, you'll have built:
- A complete orchestrator + subagents pipeline for content generation
- A message-passing system with typed schemas and routing
- A parallel agent executor with DAG-based scheduling
- A 3-agent debate system with convergence detection
- Complete implementations in Swarm, AutoGen, CrewAI, and LangGraph