Building Multi-Agent AI Systems

1-minute read
ai-agents • multi-agent • llm • architecture

Multi-agent systems divide work among specialized agents-a researcher, coder, critic-coordinated by a supervisor or message bus.

Patterns

Supervisor - One model delegates subtasks and aggregates results.

Peer-to-peer - Agents message each other until consensus or max rounds.

Pipeline - Fixed stages (plan → implement → test).

Implementation Tips

Give each agent a narrow system prompt and tool set. Pass structured state (JSON) between agents, not raw chat logs.

Failure Modes

Infinite loops, duplicated work, conflicting edits. Enforce step limits, idempotent tools, and single-writer rules for shared files.

Conclusion

Multi-agent is not free complexity-use it when tasks decompose naturally. For most apps, a single agent with good tools outperforms a committee.