Multi-Agent System — AI Glossary

Reference details

Related terms

• AI Agent
• Agent Handoff
• Agentic Loop
• Orchestrator Agent

Example: Where the errors go

A planner, a researcher and a critic each at 90% reliability do not give you 90% end to end. Errors compound along the chain: 0.9 × 0.9 × 0.9 ≈ 0.73. Add a step and it falls again. Multi-agent designs buy specialisation and pay for it in compounding failure and multiplied token cost.

1 agent  @ 0.90            = 0.90
3 agents @ 0.90 in series = 0.90^3 = 0.729
5 agents @ 0.90 in series = 0.90^5 = 0.590

Commonly confused with

Multiple prompts is not multiple agents. Calling a model three times with different instructions is a pipeline. A multi-agent system has agents that decide, delegate and react to each other's output — which is also what makes it hard to debug.

When to use it

Reach for it when:

• Sub-tasks genuinely need different tools, context or permissions
• One agent's context window cannot hold the whole problem
• A separate critic or verifier measurably improves output quality

Reach for something else when:

• A single agent with good tools would do — this is the common over-build
• You cannot trace which agent caused a bad outcome
• Cost matters: every agent adds a full model call, and they multiply

Referenced by

This term is used by the following ModelRefs references:

• supervisor worker
• debate
• chain of agents

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