What is AI Orchestration
Coordinating models, tools, data, and process steps so that the AI system performs a complex task in a controlled manner.
Definition
AI orchestration is needed when a problem cannot be solved by one query to the model. The system must select a model, call the API, receive data, process the file, check the result, transfer the step to another agent and record the result. Orchestration turns individual AI capabilities into a workflow.
Example
The application processing service first classifies the letter, then extracts the data, looks for the client in CRM, offers a response and sends it to the operator for verification.
Why it matters
The term is important for enterprise AI: value often comes not from a single model, but from a bundle of models, tools and rules.
How it works
The orchestrator manages the sequence of actions, conditions, retries, logs, access rights, error handling and context passing.
Where it is used
- process automation
- multi-step AI agents
- integration of models and services
Limitations
Complex chains are more difficult to debug. An error at one step can ruin the entire result, so tests, logs and quality control are needed.