AI glossary
Multi-agent system
A multi-agent system splits a task between several AI agents, each with its own role, tools and context, coordinated by code or by another agent.
A common shape is an orchestrator that breaks a job into parts and hands each to a worker agent, then checks and combines what comes back.
Splitting helps when parts are truly independent and each needs a clean context, such as researching several sources at once. It costs more calls, more tokens and more places to fail.
More agents is not more intelligence. Start with one agent, and split only when you can show the work suffers from one context doing everything.
Learn it hands-on
These free lessons teach it with commands to run in a lab and checked tasks. Read them whole with no account.