Guide · Agent orchestration
What AI Agent Orchestration Actually Does
Follow a practical example of a coordinator assigning work, checking results, and deciding when a multi-agent task is finished.
A request such as "prepare a customer onboarding plan" hides several jobs. Someone needs to read the contract, identify missing information, draft the schedule, and check whether the promises are achievable. Agent orchestration is the part of the system that decides how those jobs move between workers and what happens when something goes wrong.
Follow one request through the system
Imagine a small implementation agency preparing a plan for a new customer. This is a hypothetical workflow. A coordinator receives the approved contract and asks a research worker to extract deliverables. A second worker checks the team's availability. A writer then uses both results to draft the plan.
The order matters. The writer can prepare the document structure early, but cannot assign reliable dates before the availability check returns. The coordinator tracks those dependencies and keeps the result attached to the original customer request. Otherwise, a perfectly reasonable answer can end up in the wrong plan.
Decide what each worker can return
Give the contract worker a compact result: deliverable, supporting contract section, uncertainty, and any missing detail. The availability worker returns possible dates and the source schedule. Neither needs to produce a polished customer letter. That is the writer's job.
Ask the coordinator to reject incomplete results before combining them. A date without a source should remain unresolved. When the system carries that uncertainty forward, the final reviewer can see what still needs a decision rather than discovering it after the customer has received the plan.
Separate reasoning from the machinery
The model can propose assignments and interpret findings. Your application or agent platform still needs to manage files, permissions, tool execution, and saved state. Choosing GPT-6 Astra or Claude Fable 5.1 does not, by itself, define those operating rules.
For example, the application can refuse a customer-email action until a person has approved the exact draft. It can also record which contract version was read. These controls should remain meaningful even when the model changes its plan or a worker returns an unexpected answer.
Give completion a visible meaning
A useful finish condition for this example is an internally reviewed plan with every commitment tied to an approved source. Sending it to the customer can be a separate action. Keep the final artifact and a short list of unresolved questions together.
Try the workflow on one representative contract before expanding it. Watch where people still need to intervene. If most of the time goes into explaining ambiguous sales promises, improve that input first. Adding another agent won't make the contract clearer.
The useful output of orchestration is a completed piece of work you can inspect. A screen full of active agents may be interesting to watch, but the plan still needs the right customer, accurate commitments, and a responsible reviewer.