What is a multi-agent system?

A multi-agent system consists of several AI agents, each with a specialized role, that together solve a task too large or too varied for a single agent. The agents exchange intermediate results, check each other, or work on subtasks in parallel; often a supervising agent coordinates the distribution. The principle is that of a team of specialists rather than a single generalist.

Why does this matter?

Specialized agents with narrowly scoped tool and data access are easier to test, secure, and improve than one agent that can do everything. At the same time, handovers, costs, and failure points grow with the number of agents – which is why multi-agent systems need orchestration and an analysis of how they actually interact.

What does this look like in practice?

For a process analysis in procurement, one agent evaluates cycle times, a second investigates compliance breaches, a third calculates working-capital effects. A coordinating agent merges the findings, recognises that two of them share a cause — invoices arriving without a prior purchase order — and combines them into one action.

Not to be confused with

Not the same as agent orchestration: the multi-agent system is the architecture, orchestration the control within it. Nor the same as a single agent with many tools, where one model makes every decision; in a multi-agent system responsibilities are spread across several.