Definition
AI orchestration is the coordination of models, tools, data sources and people so a multi-step process runs reliably, with routing, retries, logging and handoffs.
Orchestration decides which model handles which task, what happens when a step fails, and how work moves between automated steps and human checkpoints.
Why it matters
A prototype agent can work on a laptop with one model and one tool. A production process involves several models, several systems, people at checkpoints and many ways to fail: a timeout, an empty search result, a supplier API that is down.
Orchestration is the layer that keeps that reliable. It routes each task to the right model, retries or falls back when something fails, records what happened at every step and makes sure work never silently disappears between a system and a person.
An example
A loan application arrives. Orchestration sends the documents to an extraction model, runs rule checks in code, asks a second model to summarise risk factors, and queues the file for an underwriter. If extraction fails on a blurred page, the file is routed to a person for manual entry instead of stopping the pipeline.
Illustrative example, not a client project.
Common questions about AI orchestration
Do small projects need AI orchestration?
A single, simple task may not. Once a process has several steps, systems or people involved, some orchestration is needed to make it reliable and auditable.
Is orchestration a product or a pattern?
Both exist. It can be built with workflow engines and agent frameworks, or with plain application code, as long as routing, retries, logging and handoffs are handled.
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