Multi-agent systems: how teams of AI agents work together
A multi-agent system uses several AI agents, each with a specific role, tools and instructions, coordinated to complete a larger task. A common pattern is an orchestrator agent that breaks the work down and hands parts to specialist agents, such as a researcher, a writer and a reviewer. Multi-agent systems can handle broader tasks and check each other’s work, but they cost more, are harder to debug and should only be used when one agent is not enough.
What is a multi-agent system?
A single AI agent plans and acts towards a goal. A multi-agent system splits a larger goal across several agents, each with its own role, instructions and tools. They pass work between them, share results and sometimes check each other. It mirrors how human teams divide work between specialists.
What are the common patterns?
When do several agents beat one?
Most organisations should start with one agent and split it only when it clearly struggles. Our use case guide shows tasks that suit a single agent.
How do agents communicate?
Within one platform, agents usually pass structured messages or write to shared memory. Across systems, open standards are emerging. The Model Context Protocol (MCP) gives agents a common way to use tools, and Google’s Agent2Agent (A2A) protocol, now hosted by the Linux Foundation, aims to let agents from different vendors work together.
According to the Linux Foundation, more than 150 organisations supported A2A by April 2026, with integration across Google, Microsoft and AWS platforms. MCP and A2A are complementary: MCP connects an agent to tools and data, while A2A connects agents to each other.
What does a real example look like?
- Orchestrator receives the goal: “Prepare a competitor briefing on five rivals for Monday’s board meeting.”
- Researcher agents gather facts: Each searches the web and internal notes for one competitor.
- Analyst agent compares: Builds a table of pricing, features and recent moves.
- Writer agent drafts: Turns the analysis into a two-page briefing.
- Reviewer agent checks: Verifies figures against sources and flags gaps.
- A person approves: The head of strategy reviews and edits before it goes to the board.
What are the costs and risks?
- More model calls mean higher cost and slower results
- Errors can pass between agents and compound
- Debugging is harder because behaviour emerges from interactions
- Agents can loop or duplicate work without clear stopping rules
- Permissions multiply with each agent and tool
How do you design one well?
- Give each agent one clear role and a short instruction set
- Define exactly what each agent hands to the next
- Set limits on steps, time and cost
- Log every message and tool call
- Keep a person approving the final output and any real actions
Approval design is covered in human-in-the-loop AI.
What roles work well for specialist agents?
- Researcher: Finds and summarises information from search and internal sources.
- Analyst: Compares data, spots patterns and builds tables.
- Writer: Turns findings into drafts in a set format and tone.
- Reviewer: Checks facts, sources, policy and tone before output.
- Executor: Takes approved actions in business systems.
Keeping roles narrow makes each agent easier to test and replace. When results disappoint, you can see which role failed instead of untangling one long, complicated instruction set.
How should you start?
- Build one agent first: Get a single agent working well on part of the task.
- Find where it struggles: Note where instructions grow too long or quality drops.
- Split off one role: Move that part to a specialist agent with its own instructions.
- Add a reviewer if needed: Use a checking agent where errors are costly.
- Measure cost and quality: Compare against the single-agent version before adding more.
Growing a system one role at a time keeps it understandable and shows clearly whether each extra agent is worth its cost.
In short, multi-agent systems are a tool for broad, complex jobs. For most business tasks today, one well-designed agent with good tools and a human reviewer remains the better starting point, and you can grow from there as needs become clearer.
For a closer look at how agents from different vendors cooperate, read MCP vs A2A.
How UIUX Studio helps
We design and build agent systems sized to the job, often starting with one agent and adding specialists where they clearly help. Talk to our Agentic AI & Automation and product engineering teams.
Frequently asked questions
What is a multi-agent system?
Several AI agents with different roles working together on one larger task.
What is an orchestrator agent?
A lead agent that plans the work, delegates parts and combines the results.
Are multi-agent systems better than single agents?
Only for broad tasks; for narrow tasks, one agent is cheaper and simpler.
How do agents talk to each other?
Through structured messages, shared memory and standards such as MCP and A2A.
What is the main risk?
Errors and costs compounding across agents without clear limits.