Building effective AI agent teams mirrors human team dynamics. Key roles include doers (execute individual steps), planners (break complex tasks into smaller steps), tool operators (interact with APIs and services), learners (retrieve and filter external information, often via RAG), critics/feedback agents (check for hallucinations, run QA tests), supervisors (monitor progress at task or project level), and presenters (synthesize and communicate results to users). Popular patterns like ReAct combine several of these roles. To make each role perform well, four levers are available: prompting with clear instructions, selecting the right model for the role, fine-tuning with labeled examples, and carefully managing context. Agent teams should start small and grow as task complexity increases.
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