Building a Multi-Agent System

One agent, one job, works fine until the job stops being singular. Ask a single agent to research a topic, write it up, and fact-check its own writing, and you'll watch it lose the thread — the research context crowds out the writing instructions, the writing voice bleeds into what should be a neutral verification pass. The usual fix people reach for is "add more agents." Sometimes that's right. Often it trades one problem (a confused agent) for three (coordination overhead, unclear failure attribution, a bigger bill).

This guide is for people who've already built a single working agent — see Building an AI Agent if you haven't — and are deciding whether to split it into several. It assumes you know what an agent is (AI Agents, Explained), how context windows work (Context Engineering), and how to design a self-correcting loop (Loop Engineering). None of that gets re-explained here. What follows is specific to the moment you add a second agent to the picture: why you'd do it, the shapes that orchestration actually takes, and what breaks that didn't break before.

Three phases. First, the honest case for and against splitting up the work — the coordination problem multi-agent systems solve, and the coordination tax they charge for solving it. Second, three concrete orchestration patterns with nameable shapes: pipeline, fan-out/fan-in, and supervisor-worker, each with a worked scenario. Third, the part that actually determines whether your system survives contact with production: what happens when a sub-agent fails, lies, or loops, and the guardrails — timeouts, retries, output validation, budget caps — that keep one bad sub-agent from taking down the whole run.

By the end you'll know when adding an agent is the right call, which shape fits your problem, and how to keep a multi-agent system from silently burning your budget on a Tuesday afternoon.