# Multi-agent systems explained: when several AI agents beat one

A multi-agent system is a setup where several AI agents each handle one part of a job and pass work between them. How it works, when it beats a single agent, what can go wrong, and how to keep it under control.

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## Steps

1. **Start with one agent.** Add a second only when one clearly cannot do the job well. See install a specialist bot.
2. **Give each agent the least access for its role.** The researcher does not need your card; the writer does not need your inbox. See connect only what you need.
3. **Keep the final action with a person.** Agents can pass work around freely, but sends and payments wait for you. See review before place.
4. **Cap total spending.** Several agents can run up usage fast. See how much an AI agent costs.
5. **Log every hand-off.** So you can see which agent did what. See AI agent audit logs.

## FAQ

### What is a multi-agent system?

Several AI agents working on one job, each with its own role, often with one agent coordinating the rest.

### Is a multi-agent system better than one agent?

Only for jobs with clearly separate parts. For a single clear task, one agent is simpler, cheaper, and easier to check.

### What is an orchestrator agent?

The agent that splits a job into parts, hands them to other agents, and combines the results.

### Are multi-agent systems more expensive?

Usually, because several agents use the AI model at once. Set a spending cap that covers all of them together.

### What are the risks of multi-agent systems?

Mistakes can pass from one agent to the next, permissions can spread through hand-offs, and costs can climb. Limit access per role and keep a person on final actions.