The orchestrator-worker pattern is a multi-agent design where one orchestrator agent breaks a task into subtasks, routes each to a specialized worker agent, and assembles their results, keeping coordination centralized in one place while the actual work runs in parallel across narrow, focused agents.
How does the orchestrator-worker pattern actually run?
The orchestrator does not do the underlying work itself: it plans, decides which subtask goes to which worker, and combines what comes back. Each worker stays narrow on purpose, built for one kind of subtask with its own tools, and does not need visibility into the other workers, only the piece of work handed to it.
Why is this the default pattern in most multi-agent systems?
It maps cleanly onto how deep agents and other production multi-agent systems already work: a planning layer at the top, isolated subagents doing the actual execution, and one place responsible for assembling the pieces. Keeping coordination centralized, rather than letting every worker talk to every other worker, keeps the system predictable as workers grow in number.
When does this pattern break down?
When the orchestrator itself becomes overloaded: heavy reasoning on top of routing and assembly, or too many workers, and its own calls start to dominate cost and latency, making it a single point of failure. Structured work with clear handoffs earns the overhead; a short task with one clear next step usually just needs a single AI agent.
Frequently asked questions
How does the orchestrator decide how to split up a task?
It reasons over the task at runtime and breaks it into subtasks dynamically, rather than following a fixed decomposition written in advance. That is what lets the same orchestrator handle tasks of varying shape instead of only the exact structure someone anticipated.
Do worker agents need to know about each other?
No, and that is the point of the pattern. Each worker only needs its own subtask and enough context to complete it; the orchestrator is the only agent that needs a view of the whole task and how the pieces fit together.
What is the main failure mode of this pattern?
The orchestrator becoming a bottleneck. If it also does heavy reasoning work itself, or if the number of workers grows large, the planning and result assembly calls start to dominate the total cost and latency, and a single orchestrator becomes a single point of failure for the whole run.