Multi-Agent Systems: When One AI Agent Isn’t Enough

As organizations move from a first AI agent to broader agentic AI, a question comes up quickly: should one agent do everything, or should several specialised agents work together? Multi-agent systems can be powerful, but they also add complexity. Here is how to think about them.

What a multi-agent system is

A multi-agent system splits a goal into roles. One agent might research, another draft, a third check compliance, and an orchestrator decides who does what and when. Each agent has a narrow job, its own tools and its own instructions.

Why split the work?

• Focus: narrow agents are easier to prompt, test and improve.
• Least privilege: each agent only gets the tools its role needs.
• Quality checks: a reviewer agent can catch errors before a person sees the output.
• Reuse: a well-built research or extraction agent can serve several workflows.

Common orchestration patterns

Pipeline

Agents run in a fixed order — extract, enrich, draft, review. Predictable and easy to monitor; the best starting point.

Supervisor

An orchestrator agent assigns tasks to specialists based on the request and assembles the result. Flexible, but needs strong limits and logging.

Reviewer loop

A producer agent creates output and a reviewer agent checks it against rules, sending it back with feedback when needed, up to a fixed number of rounds.

When not to use multiple agents

If a single agent with a clear job and a handful of tools can do the work reliably, adding more agents adds cost, latency and new failure points. Start with one well-scoped agent inside a structured workflow and split only when evaluation shows a clear benefit.

Designing for production

Whatever the pattern, keep the workflow deterministic around the agents: fixed triggers, explicit hand-offs, limits on loops, human approval for sensitive actions and full traces of every step. Our agentic AI team designs these systems, and our builder training helps internal teams maintain them.