Multi-agent systems: how a team of AI agents works
The short version: a multi-agent system is several AI agents working on one task together, each with its own role, instructions and tools. Usually one agent — the orchestrator — splits the task and hands the parts to specialists: a researcher, a writer, a designer, a reviewer. That way complex work is done by several short, reliable sets of instructions instead of one long, muddled one.
A multi-agent system in plain words
Picture a newsroom. The editor-in-chief does not write every story: they give a reporter an assignment, pass the finished piece to a copy editor, order an illustration and put the issue together. Everyone does what they do best, and the editor answers for the whole.
A multi-agent system works the same way. Each agent in it is an ordinary AI agent with its own instructions and tools. The difference is that tasks are set not only by a person but by another agent, and the result goes back not to a chat but to whoever assigned it.
Why several agents, when you could have one
| One agent | A team of agents | |
|---|---|---|
| Instructions | One long set that covers everything | Several short ones, one each |
| Tools | All at once — the model gets confused | Each has only its own |
| Quality on complex tasks | Drops as the task grows | Holds up thanks to specialisation |
| Self-checking | The agent checks itself | A separate critic agent |
| Cost and time | Fewer model calls | More calls: you pay for coordination |
| Debugging | Simpler | You have to work out which agent went wrong |
A simple rule
Multi-agent system architectures
In practice almost every system is built from three layouts. Watch how the work moves in each.
How agents hand work to each other
Three basic multi-agent system layouts. Watch where the message goes.
You → Lead: Launch a post about the new plan
1/8When it fits
The task splits into independent parts: gather facts, write the copy, make the image.
Where it breaks
The lead agent is the bottleneck: if it frames a subtask badly, every sub-agent does the wrong thing.
| Architecture | How it works | When to choose it |
|---|---|---|
| Orchestrator (hierarchical) | A lead agent splits the task, hands out subtasks and gathers the answer | The task splits into different kinds of work: facts, copy, an image |
| Pipeline (sequential) | Each agent’s output is the next one’s input | Stages in strict order: transcript → minutes → slides |
| Writer and critic (loop) | One creates, the other reviews and sends it back for another round | Quality matters: website copy, legal replies, code |
| Decentralised (swarm) | Equal agents exchange messages with no lead | Research and simulations; rare in business tasks |
Problems with multi-agent systems
A team of agents inherits a single agent's weaknesses and adds its own. They are worth knowing about before you build one.
- Cascading errors. An inaccurate fact from the researcher becomes confident copy in the writer's hands and a banner in the designer's. The fix is a check between stages and a "links only" rule.
- Lost context. A sub-agent sees only what it is given. If the orchestrator framed the subtask too briefly, the specialist does the wrong thing. Good orchestrator instructions spell out what to pass to each member.
- Endless loops. A writer and a critic can go round in circles. You need "done" criteria and a limit on rounds.
- Cost. Every agent means model calls. A team of four can easily spend several times what one agent would.
- Blurred responsibility. When the result is bad, it is hard to tell whose instructions are at fault. Test each agent on its own before you assemble the team.
How to build a multi-agent system without code
The order is the same on any platform: specialists first, then the orchestrator. In Twelver it looks like this:
- Create the specialist agents and test each on its own — each should handle its part well. How to do that is in the step-by-step guide.
- Create the lead agent. In its instructions, describe how to split the task and how to assemble the result.
- Add the specialists in the "Agent team" section.
- For each member, fill in its "Usage Instructions": when the lead agent should turn to it and what to pass along.
- Test the team on a real task and look at who the orchestrator gave what.
Agent teams are on the Premium plan
Multi-agent systems on LLMs: frameworks for developers
If the system has to live inside your own product, it is built in code. The most common options:
- LangGraph — agents as nodes in a graph with explicit transitions; handy for pipelines and review loops.
- CrewAI — a "crew" of agents with roles and tasks, closest to the newsroom idea.
- AutoGen — agents that negotiate by exchanging messages; popular in research.
- OpenAI Agents SDK — handing control between agents (handoffs) and agents as tools for one another.
In any of these frameworks, agents reach external services through a standard protocol — that is the chapter on AI agents and MCP. Ready scenarios where a team earns its keep are in AI agents for business and the agent examples.
Frequently asked questions
What is a multi-agent system in simple terms?
What multi-agent system architectures are there?
What problems do multi-agent systems have?
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