What multi-agent AI is
Multi-agent AI is an approach where several AI agents collaborate to solve a problem that would be unwieldy for a single agent. Instead of one model trying to plan, research, write, and check all at once, the work is split among agents — each with its own role, tools, or expertise — that coordinate through messages and shared results. The pattern mirrors how human teams divide labour: a coordinator breaks the work down, specialists do their parts, and the pieces are assembled into a whole.
Orchestrator and executor roles
The most common structure is a hierarchy. An orchestrator (sometimes called the lead or planner) reads the goal, decomposes it into sub-tasks, and delegates each to an executor (also called a worker or sub-agent). Executors focus narrowly — one might search the web, another might write code, another might draft prose — and return their results to the orchestrator, which stitches them together and decides whether more work is needed. This keeps each agent’s context small and focused, which often produces better results than overloading one agent with everything.
Communication patterns
Agents have to talk to each other, and how they do so shapes the system. In a hierarchical pattern, messages flow up and down between an orchestrator and its workers. In a sequential pipeline, the output of one agent becomes the input of the next, like an assembly line. In a collaborative or peer pattern, agents exchange messages more freely, sometimes debating or critiquing each other’s work to reach a better answer. Messages are usually structured — a task description plus the data needed — so each agent knows exactly what it is being asked to do.
Why split work across agents
Several benefits drive multi-agent designs. Parallelism: independent sub-tasks can run at the same time, cutting wall-clock time. Specialisation: an agent tuned for code review will often outperform a generalist on that task. Context management: giving each agent a narrow slice of information avoids the confusion that comes from a single bloated context window. And verification: one agent can be assigned purely to check or critique another’s output, catching errors a lone agent would miss.
The coordination tax
These benefits are not free. Every additional agent means more model calls, so cost and latency multiply quickly — a five-agent system can be several times more expensive than a single agent. Agents can duplicate effort or, worse, propagate an early mistake through the whole pipeline. There is also a trust question: should the orchestrator accept a sub-agent’s result at face value, or verify it? Mature systems answer these with caps on how many agents can spawn, strict task interfaces, and explicit verification stages.
Designing multi-agent systems well
The practical advice is to reach for multiple agents only when the problem genuinely warrants it. Start with one agent; add agents when a task clearly decomposes into independent parts, needs distinct tools, or benefits from an independent reviewer. Keep each agent’s role sharp and its context minimal, define clean message contracts between them, log everything for debugging, and put a ceiling on recursion so the system cannot spiral into an unbounded swarm. Done carefully, multi-agent AI turns problems that overwhelm a single model into something tractable.