Orchestrator Agent — AI Glossary

Reference details

Related terms

• Multi-Agent System
• Agent Handoff
• AI Agent

How it works

1. It holds the goal and the plan, so the decomposition and the record of what remains live in one place rather than in each worker.
2. Sub-tasks are dispatched to workers with only the context each needs, which keeps a specialist's prompt small and its behaviour predictable.
3. Worker returns are evaluated against the sub-task's criterion before being accepted, so a confident wrong answer can be rejected and retried.
4. Accepted results are merged into shared state, and the plan is revised if a return changes what the remaining work should be.
5. Completion is its decision: it compares state against the original goal and stops, which is the judgement a router never has to make.

Example: Why the model split is an arithmetic decision

A task needs one planning call and six worker calls. Running everything on a frontier model at a notional 10 cost units per call is 70 units. Planning on the frontier model and the six workers on a specialist at 1 unit each is 10 + 6 = 16 units, under a quarter, provided the workers' sub-tasks are narrow enough that the cheaper model is reliable on them. That proviso is the whole design problem: the saving is real only when the decomposition made each sub-task small.

Commonly confused with

Also just called the orchestrator, and not to be confused with a router: a router chooses a destination and stops, while this decomposes a goal, delegates to worker agents, and decides when the goal is met. The difference that matters operationally is that it holds state — it has to recognise a returned sub-result as incomplete and decide what follows, so it needs its own step budget and stopping condition, and a multi-agent system without one is a cost problem waiting to happen. Outside this field "orchestrator" usually means a workflow or container scheduler, which is a different thing entirely.

When to use it

Reach for it when:

• A goal needs several specialised capabilities no single prompt covers well.
• Cost or latency justify putting planning on a strong model and execution on cheaper ones.
• Worker output needs checking before it is accepted into shared state.

Reach for something else when:

• A single destination has to be chosen and then nothing more — that is a router.
• The task is one step, where orchestration adds a planning round-trip for nothing.
• Sub-tasks cannot be verified, so the orchestrator merges unchecked claims.

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