What is a multi-agent system? When one AI agent is not enough

A multi-agent system is a setup where two or more AI agents each handle part of a task. A coordinator or fixed workflow passes work between them and combines the results. It helps when work splits into parallel or specialised parts. For most business tasks, one well-built agent is cheaper and easier to run.

What is a multi-agent system?

It is a system built from several agents that work on one goal. Wikipedia, as of October 2026, defines it as a computational system composed of multiple interacting intelligent agents. Textbooks on it date back to at least 1999.

Today, an agent is a language model with instructions and a set of tools. It reads a request, decides which tool to call, looks at the result and repeats until the job is done. Our post on agents versus assistants covers the basics.

A multi-agent system puts more than one of these to work. Each agent gets its own instructions and tools, plus a slice of the job. One agent might search documents, a second might draft text, and a third might check the draft.

How does a multi-agent system work?

Most designs use one of a few patterns, and the main difference is who is in control. The OpenAI Agents SDK documentation, as of October 2026, describes two of them in plain terms.

In the first, a manager agent keeps control of the conversation and calls specialist agents as if they were tools. The specialists do a piece of work and return the result to the manager, which writes the final answer. OpenAI calls this “agents as tools”. It is also known as the manager pattern.

In the second, a triage agent routes the conversation to a specialist. That specialist then becomes the active agent for the rest of the turn. This is called a handoff.

The same page says the flow can be decided by the model or by your code.

Microsoft’s Azure Architecture Center, as of October 2026, lists more patterns. A sequential pattern chains agents in a predefined, linear order. A concurrent pattern runs several agents at the same time on the same task.

What is the difference between a single agent and a multi-agent system?

A single agent does the whole task in one loop. A multi-agent system splits the task across agents and adds a layer that coordinates them. The table summarises what changes in practice.

When do you need more than one agent?

In most cases, you need more than one agent only when a single agent has been tried and fails for a specific reason. Anthropic’s guide on claude.com, published 23 January 2026, names three situations where multiple agents consistently outperform a single one. They are context pollution that degrades performance, tasks that can run in parallel, and specialisation that improves tool selection or focus.

The same article says Anthropic has seen teams spend months on elaborate multi-agent designs. They then found that better prompting on a single agent gave equivalent results. Its advice is to start with the simplest approach that works.

Microsoft’s Cloud Adoption Framework, as of October 2026, takes a similar line. It suggests multiple agents from the start when the use case crosses security and compliance boundaries, involves several teams or is expected to grow. For other use cases that are not low complexity, it recommends testing a single agent first.

Use this short test before you plan more than one agent:

1. Have you tried one agent with clear instructions and tested it on real examples?
2. Did it fail because it holds too much context, or because it has too many tools to choose from?
3. Can parts of the task run at the same time without needing each other’s results?
4. Do different teams own different parts of the work, with different rules for each?
5. Can you afford the extra token use and the extra effort to review the results?

If the answer to the first question is no, that is usually the place to start. If you cannot say yes to at least one of questions two to four, a single agent is usually the better choice.

What are the trade-offs of using multiple agents?

The main costs are more tokens and more coordination, and someone has to review what each agent did. In Anthropic’s testing (guide, 23 January 2026), multi-agent setups typically used three to ten times more tokens than single agents for equivalent tasks.

Anthropic’s engineering team gave related figures on 13 June 2025. In its data, agents used about four times more tokens than chat, and multi-agent systems about fifteen times more. Both figures compare against a chat, not a single agent. The same post reports a result from its internal research eval. A Claude Opus 4 lead agent with Claude Sonnet 4 subagents scored 90.2% higher than a single Claude Opus 4 agent. Treat it as one vendor’s test on one kind of task, not a general benchmark.

Coordination is the other risk. Cognition, the company behind the Devin coding agent, argued in June 2025 that subagents can act on conflicting assumptions that nobody set out upfront. Its principle is that agents should share context.

LangChain’s Harrison Chase wrote in June 2025 that read actions are inherently more parallelizable than write actions. Several agents can gather information at once. When several agents write in parallel, they must share context and then merge their outputs into one result.

What does a multi-agent system look like in a business?

These are illustrations only. No results are claimed.

• A marketing team could have a manager agent send one agent to read competitor pages and another to read the team’s documents. The manager then merges the findings into a brief. The two reading tasks are independent.
• A support team could use a triage agent that passes each request to a billing agent or a product agent. Each specialist has only the tools for its own topic.
• An operations team could chain one agent that pulls figures with one that writes the summary. Because the order is fixed, code can run it.

In each case, a team should first try one agent with good instructions. Add the second only if the test shows a gap.

Where does TobyBot fit?

TobyBot is an agentic bot by Xinference, and you can use it to try one task end to end before you plan anything bigger. You set a goal. TobyBot plans the steps, shown as one card for each, then runs them with your tools. Every tool call and result is visible while it runs, and you can pause it.

Every tool call and result is visible while a task runs, so you can see where it does well and where it falls short. A task can be saved as a bot that keeps its role, model, tools and knowledge.

To see this on your own task, read our post on what happens after you type a request or book a demo.

Try TobyBot. See all use cases, or book a demo on your own workflow.