Multi-Agent Systems
Teams of specialist agents that collaborate
🔑 Key Concepts
- When multi-agent helps — Single agent juggling too many responsibilities drops quality. Specialists outperform generalists.
- Supervisor pattern — One agent delegates to specialists: researcher, writer, critic. Supervisor monitors and handles failures.
- Frameworks — CrewAI (role-based teams), AutoGen (Microsoft, conversation-based), LangGraph (stateful orchestration).
- Cost management — Use cheap models for routine agents (GPT-4o-mini), expensive models only for quality-critical roles.
💡 Practice: Try implementing each concept yourself before moving on. Reading about RAG and building RAG are very different things.