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Definition

AI Orchestration

AI Orchestration is the coordination layer that connects AI models, agents, business rules, APIs, and data sources into a single reliable workflow, deciding what runs when, in what order, and what happens if a step fails.

A single model call is not a system. The moment a task needs more than one step, calling a model, checking the result, deciding what to do next, calling a second tool, someone has to own the sequencing, the error handling, and the state that carries between steps. That coordination layer is what orchestration actually is, and it is usually where a working prototype turns into something a production team will trust.

What it actually coordinates

In practice this means routing a task to the right model or tool, holding conversation and task state across multiple steps, deciding where a human checkpoint sits, retrying or escalating a failed step instead of silently swallowing it, and logging what happened well enough that someone can reconstruct it later. Frameworks like LangGraph exist specifically to give this coordination a defined structure rather than leaving it as ad-hoc glue code.

A real example, not a hypothetical one

Kelriva's own agent system runs on exactly this pattern: one orchestrator fires on a schedule and fans out to several specialised agents in sequence, each one logging its own status, cost, and output to a shared store rather than running blind. That structure is what makes it possible to see what actually happened on a given day, not just trust that it probably worked.

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Last updated 9 September 2026