What Are Multi-Agent Systems?

The first generation of AI tools was a single model answering a single prompt. The frontier now is multi-agent systems: several AI agents that divide a job, coordinate, and check each other's work. It's a powerful pattern — and a new source of reliability and cost risks. Here's what multi-agent systems are, why companies are building them, and where they go wrong.

What a multi-agent system is

Instead of one model doing everything, a multi-agent system uses multiple specialized agents that each handle part of a task and pass work between them. A common shape:

• A planner breaks a goal into steps.
• Worker agents each execute a step (research, write code, query a database, call an API).
• A reviewer/verifier checks the output before it's accepted.
• An orchestrator routes work and decides when the job is done.

The idea borrows from how human teams work: specialists, a coordinator, and a reviewer.

Why companies build them

• Specialization. A focused agent with the right tools and instructions often outperforms one generalist trying to do everything.
• Complex, multi-step tasks. Jobs that need research, then action, then verification map naturally onto multiple agents.
• Verification. A separate agent checking another's work catches errors a single pass would miss — improving reliability.
• Parallelism. Independent subtasks can run at once, faster than one agent doing them in sequence.

The new failure modes

Multi-agent systems also introduce problems a single model doesn't have:

• Error propagation. A mistake early in the chain gets amplified as later agents build on it.
• Cost explosion. Every agent, every step, every retry is more model calls. A multi-agent task can cost many times a single prompt — easy to underestimate.
• Coordination failures. Agents can loop, disagree, or get stuck handing work back and forth.
• Compounding unreliability. Chain enough probabilistic steps together and the odds that something goes wrong rise quickly.
• Harder debugging. When the output is wrong, finding which agent failed is more complex than inspecting one model.

How teams manage the risks

• Verification steps (a reviewer agent, or human checkpoints) at the points that matter.
• Budgets and step limits so a runaway loop can't rack up huge costs.
• Tiered models — cheap models for simple steps, the expensive one only where it counts.
• Observability — logging each agent's input/output to debug and audit.
• Scoped permissions so each agent can only do what its role requires.

Who should care

• Engineering teams building agentic products — this is the architecture you'll grapple with.
• Anyone budgeting AI — multi-agent costs scale fast and need controls.
• Product leaders weighing reliability: more agents can mean more capability and more ways to fail.

Bottom line

Multi-agent systems split a task across specialized, coordinating agents with a reviewer — unlocking complex work and better verification than a single model. But they also multiply cost and introduce error propagation, coordination failures, and compounding unreliability. Build them with verification, budgets, step limits, and observability, and they're powerful. Build them naively, and they fail in expensive, hard-to-debug ways.