Multi-Agent Systems
Several AI agents working together on one problem, each with a role — powerful in demos, awkward in production.
When not to use it
- When a workflow would do. Fixed steps with a model call at each one is the right answer far more often than a team of agents, and it can be debugged.
- When the sub-tasks depend on each other. Agents coordinate badly; sequential dependencies remove the only real benefit, which is parallelism.
- When cost or latency matter. Every agent is a full inference loop. Three agents is roughly three times the bill, plus the messages between them.
Reach for something else instead
- A single agent with tools — usually the same capability with one context and one place to look when it breaks.
- A workflow — explicit steps, deterministic control flow, model calls where judgement is genuinely needed.
- One model, multiple passes — draft then critique in sequence, which captures most of the critic benefit without a second agent's overhead.
Sources & further reading
- Wu et al. (2023), AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation — the framework, and a fair look at its patterns.
- Park et al. (2023), Generative Agents — the emergent-behaviour result that started much of the enthusiasm.
- Cemri et al. (2025), Why Do Multi-Agent LLM Systems Fail? — a taxonomy of failures observed in practice. Read this one first.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Adding agents to fix quality problems. If one agent is unreliable, three unreliable agents produce unreliable output more expensively.
- Assuming agents catch each other's errors. They share training data and share blind spots, so they often agree confidently and wrongly.
- Shipping without hard step and budget limits. Two agents can loop politely and indefinitely, and the bill arrives either way.
At a glance
FieldAI Agents
Core ideaseveral agents, divided roles
Works forparallelism
Fails atcoordination
DifficultyAdvanced
Flashcards for this concept
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Often compared with
Multi-agent vs. workflow — agents deciding the steps vs. you deciding the steps and the model filling them in.