# Multi-agent system

**Source:** https://promtable.com/glossary/multi-agent

> A multi-agent system is a coordinated set of specialised AI agents that delegate to each other — each agent has a focused role, tool set, and system prompt rather than one mega-agent doing everything.

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A multi-agent system is a coordinated set of specialised AI agents that delegate to each other — each agent has a focused role, tool set, and system prompt rather than one mega-agent doing everything.

Multi-agent systems became a credible production pattern in 2026 because monolithic agents with 30+ tools mis-route badly. Frameworks: OpenAI Swarm, CrewAI, LangGraph hierarchies, Microsoft AutoGen, Claude sub-agents. Typical architectures: hierarchical (a coordinator + specialised workers), pipeline (chained specialists), or graph (state machine across many agents). Production reliability requires explicit handoff protocols, max-handoff caps, shared memory carefully scoped, and per-agent evals. Multi-agent is overkill for simple tasks — a single well-designed agent often beats a poorly-orchestrated multi-agent stack.

## When to use

- Tasks with clear specialisation by sub-domain.
- When one agent's tool surface would exceed ~30.

## Common mistakes

- Over-decomposing — too many agents add coordination cost.
- Skipping per-agent evals — the system fails in places that are hard to attribute.

## Related terms

- [agent](https://promtable.com/glossary/agent)
- [agent-handoff](https://promtable.com/glossary/agent-handoff)
- [agent-loop](https://promtable.com/glossary/agent-loop)
- [mixture-of-agents](https://promtable.com/glossary/mixture-of-agents)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/multi-agent
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