What is a multi-agent system in AI?

A single agent that has to do everything tends to get confused: too many instructions, too many tools, too much context. Splitting the work between specialised agents helps, as it does between people. Each has short, clear instructions and sees only what it needs.

The downside is complexity. More agents mean more calls to the model, more costs, more points where an error can spread. If the first agent misreads a request, the others carefully work on the wrong assumption. That is why you need verification steps and logs that let you see where something broke.

An example in an SMB

A furniture company receives very varied quote requests. A first agent reads the request and extracts measurements and materials. A second looks for compatible products in the catalogue. A third writes the draft quote. A fourth compares the draft with the price list and flags inconsistencies. Roberto receives only the final result, with the checker's notes.

The most common mistakes

• Building five agents when a single, well-instructed agent would be enough.
• Not providing an agent or a person who checks the result before it goes out.
• Not recording what the agents passed to each other, which makes it impossible to understand errors.

Why it matters to you

Many vendors sell multi-agent systems as the advanced solution. Always ask why more agents are needed and how the work is checked between one step and the next.

All the words of AI explained simply are in the glossary.