Coordinating multiple AI models, tools and steps into a single reliable workflow.
1 min readupdated 2026-06-22
/ quick answer
AI Orchestration is the layer that decides which model or tool runs at each step, how data flows between them, how failures are handled, and how the whole pipeline is observed in production. Coordinating multiple AI models, tools and steps into a single reliable workflow.
Coordinating multiple AI models, tools and steps into a single reliable workflow. AI Orchestration is the layer that decides which model or tool runs at each step, how data flows between them, how failures are handled, and how the whole pipeline is observed in production. In practice: An order-refund workflow that uses a classifier model to triage, a RAG step to fetch policy, an LLM to draft the reply, and a human approval gate before sending. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
AI Orchestration is the layer that decides which model or tool runs at each step, how data flows between them, how failures are handled, and how the whole pipeline is observed in production.
Example
An order-refund workflow that uses a classifier model to triage, a RAG step to fetch policy, an LLM to draft the reply, and a human approval gate before sending.
AI Orchestration is the layer that decides which model or tool runs at each step, how data flows between them, how failures are handled, and how the whole pipeline is observed in production.
What is an example of AI Orchestration?
An order-refund workflow that uses a classifier model to triage, a RAG step to fetch policy, an LLM to draft the reply, and a human approval gate before sending.
Why does AI Orchestration matter for AI and automation?
Coordinating multiple AI models, tools and steps into a single reliable workflow. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.