AI glossary
Multi-Agent Systems: What They Are and When to Use One
A working definition of multi-agent systems, how they divide work, what they cost and when a single agent is the better choice.
By Chad Westleigh · Updated
What is a multi-agent system?
A multi-agent system splits one job across several specialized AI agents. Instead of a single agent holding every instruction and tool, each agent gets a narrow role: one researches, one drafts, one checks compliance, one updates the CRM. A coordinator, either a lead agent or an orchestration layer, hands out the work and assembles the result.
The idea is older than large language models; computer scientists have studied multi-agent systems for decades in areas like robotics and simulation. What changed is that language models made it practical to build agents that read documents, write and use business software, so the pattern now appears in sales, marketing and operations.
How multi-agent systems divide the work: a public example
Anthropic's write-up of the multi-agent research system behind Claude's Research feature (June 2025) is one of the clearest public examples. A lead agent plans the research and starts subagents that search in parallel, each in its own context window, then combines what they find. On Anthropic's internal research evaluation, the multi-agent setup outperformed a single Claude Opus 4 agent by 90.2%.
The same write-up is candid about cost: multi-agent systems used about 15 times more tokens than ordinary chats. Anthropic also notes that most coding tasks have fewer truly parallel parts than research, which makes them a weaker fit. The lesson for business buyers: multi-agent designs pay off when work splits into independent strands and the result is worth the extra compute.
Single agent vs multi-agent system
| Single agent | Multi-agent system | |
|---|---|---|
| Structure | One model with all instructions and tools | Several agents with narrow roles and a coordinator |
| Suits | Focused tasks with a clear sequence | Broad tasks that split into parallel or specialized parts |
| Running cost | Lower | Higher; Anthropic measured about 15x the tokens of a chat |
| Debugging | One log to read | Errors can pass between agents, so every step needs tracing |
| Main risk | Overloaded instructions degrade quality | Coordination failures and runaway cost |
| How agents communicate | Not applicable | Shared state in the orchestrator, or protocols such as A2A |
When a multi-agent system is worth it
Build one when most of these are true. Otherwise, start with a single agent.
- The task splits into parts that can run independently
- Different steps need different tools, permissions or expertise
- One agent's instructions have grown too long to follow reliably
- The outcome is valuable enough to cover higher model usage
- You can log and trace each agent's work
- A person reviews the combined output before it reaches customers or money
Where Operator Intelligence uses multi-agent systems
Multi-agent systems are the core of the Operator Intelligence Agentic Digital Strategy offering: coordinated agents handle parts of ecommerce operations for DTC brands, such as site management, analytics and lifecycle marketing, with senior human strategy in the loop.
In promotional products, 2iC (2nd In Command) is a 13-agent AI system built for Wizard Creations, the distributor founded by our co-founder Luke Freeman. Reported outcomes include 30–50% operations time saved, a 15%+ margin improvement and six months to full ROI; see our promotional products page. 2iC is now our multi-agent back office for promotional products distributors, and walkthroughs are available on request.
We add agents only when one agent can't do the job well. Every system gets a lead with a clear remit, narrow workers, logs for each step and human-in-the-loop approval before anything reaches a customer. For the underlying idea, see agentic AI.
Multi-agent systems FAQ
How many agents should a multi-agent system have?
As few as the job needs. Each extra agent adds coordination, cost and another place for errors. Start with one agent, split out a specialist when a clear role emerges, and measure whether quality improves.
What is the difference between MCP and A2A?
The Model Context Protocol connects an agent to tools and data. Agent2Agent (A2A), a protocol Google created and the Linux Foundation has hosted since June 2025, is designed for agents to communicate with other agents. A multi-agent system can use both.
Are multi-agent systems more accurate than a single agent?
On broad tasks they can be: Anthropic reported a 90.2% improvement over a single agent on its internal research evaluation. On tightly connected tasks, extra agents can add confusion, so test both designs on real cases.
Can a small company run a multi-agent system?
Yes, if the work justifies it. Model accounts, workflow tools and logs can sit in the company's own accounts. The usual constraint is ownership: someone on the team needs to read the logs and own the outcomes.
Find out whether you need one agent or several
Describe the process and we'll recommend the simplest design that works: one agent, a workflow or a coordinated team of agents. Or start with the AI readiness assessment.
Book a scoping callSources
- Anthropic, "How we built our multi-agent research system" (13 June 2025) (2025): Multi-agent system (Claude Opus 4 lead with Sonnet 4 subagents) outperformed single-agent Claude Opus 4 by 90.2% on Anthropic's internal research eval.
- Anthropic, "How we built our multi-agent research system" (13 June 2025) (2025): Multi-agent systems use about 15x more tokens than chats; most coding tasks involve fewer truly parallelizable tasks than research.
- Linux Foundation press release (2025): Linux Foundation launched the Agent2Agent (A2A) project, an open protocol created by Google for agent-to-agent communication, on 23 June 2025.
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