Multi-Agent Systems: When One AI Assistant Isn't Enough

A single AI assistant handling an entire task end to end — researching, writing, checking its own work — tends to blur those jobs together, and that blur is often where quality slips. Multi-agent systems tackle this by splitting a task across several model calls, each with a narrower job, rather than asking one call to do everything at once.

The typical pattern: an "orchestrator" agent breaks a request into subtasks and hands each one to a specialized "worker" agent — one focused on research, one on writing, one on reviewing the others' output — then assembles the results. Each individual agent gets a simpler, more specific instruction to follow, which tends to produce more reliable output than one broad prompt asking a single model to hold every part of the task in mind at once.

A common concrete example is a coding agent split into three roles: one plans a change, a second writes it, and a third's only job is reviewing the diff before it ships — mirroring the way a human team splits planning, implementation, and code review across different people rather than asking one person to do all three without a second set of eyes.

The tradeoff is worth naming plainly: more agent calls means more latency and more cost, since each hop through the pipeline is its own request, and a poorly designed multi-agent pipeline can just as easily introduce new failure points — handoffs and synthesis between agents — as it improves accuracy on any given step.

Multi-agent systems aren't a strict upgrade over a single well-prompted call; they're a tradeoff worth reaching for when a task genuinely benefits from separation — distinct problem types, or a dedicated review step — rather than a default reached for because it sounds more sophisticated. A single, well-scoped prompt still beats an over-engineered pipeline for plenty of everyday tasks.