What is a multi agent system? Explained

What is a multi-agent system in plain words

Think of a small team instead of one person. One agent reads the request, another looks things up, another writes, another checks the result.

IBM describes multi-agent collaboration as the coordinated actions of several independent agents, each with its own local knowledge and its own decisions.

These agents are specialized for specific tasks. Together they can handle complex problems that would overload one agent trying to do everything.

Multi-agent systems are often shortened to MAS. If you already know what a single AI agent is, a MAS is simply more than one of them, working as a group.

How the agents coordinate

Coordination is the hard part. The agents need a clear way to pass work, share what they know and agree on who does what.

A common setup uses one coordinating agent that assigns tasks and gathers results. Other setups let agents talk to each other directly. The coordination layer is covered in AI orchestration.

Each agent connects to the tools it needs, such as a CRM, inbox or document store, and a person can step in at key points. See human in the loop.

A business example

• Intake agent

  Reads and sorts the request.

• Research agent

  Pulls history and the right policy.

• Drafting agent

  Writes the reply.

• Checking agent

  Tests the draft against the policy.

Take a customer request that needs research and a written reply. An intake agent reads the message and sorts it. A research agent pulls the customer's history and the right policy.

A drafting agent writes the reply, and a checking agent compares it against the policy and flags anything off. A person approves before it goes out.

The same pattern fits other requests that need lookup, writing and a check.

When one agent is enough, and the risks

Many tasks need only one agent with the right tools. Adding agents adds hand-offs, cost and more places for errors to hide. Start simple and split roles only when one agent clearly struggles.

By 2027, Gartner sees more than four in ten agentic AI projects being dropped, blaming cost, unclear value and weak controls. Extra agents can worsen all three.

Clear roles, logging and human approval help. For the single-agent view, read the AI agents guide.