What is a multi-agent system in AI?

A multi-agent system is an AI setup where several LLM-powered agents work together, each handling part of a task, instead of relying on a single agent to do everything. The agents can specialize, collaborate, and check each other's work — a bit like a team of coworkers rather than one generalist.

How does a multi-agent system work?

The motivation is simple: complex jobs often go better when you split them up. Each agent gets its own role, instructions, and tools, and one agent's output becomes another's input, so the group can tackle something no single prompt could handle cleanly. A typical division of labor looks like this:

• A planner breaks the overall goal into smaller subtasks.
• Specialists each take a piece — one researches, another writes code, another drafts text.
• A reviewer checks the output and flags mistakes before anything is finalized.

What are the common multi-agent patterns?

The most familiar setup uses an orchestrator agent that breaks a goal into subtasks, delegates them to specialist agents, and assembles the results. Another approach has agents debate or critique one another, catching errors a single model would miss on its own. Both share the same insight: giving each agent a narrower job, and letting a second agent inspect the first, tends to produce more reliable results than asking one model to do everything in a single pass.

What are the trade-offs of multi-agent systems?

The benefits are focus and reliability — specialized agents with narrower jobs tend to perform better, and having one agent review another adds a safety net. The costs are money and complexity: more agents means more calls, more coordination, and more ways for things to go wrong. In practice, teams reach for a multi-agent design only when a single agent visibly struggles, since the coordination overhead isn't worth it for simple tasks.

Multi-agent systems are an active frontier as people push AI from answering questions toward completing large, multi-step projects on their own.