Définition
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.
Exemple
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.
Pourquoi c'est important
The term is important for enterprise AI: value often comes not from a single model, but from a bundle of models, tools and rules.
Fonctionnement
The orchestrator manages the sequence of actions, conditions, retries, logs, access rights, error handling and context passing.
Où c'est utilisé
- process automation
- multi-step AI agents
- integration of models and services
Limites
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.
FAQ
Why is “AI Orchestration” useful to know?
The term is important for enterprise AI: value often comes not from a single model, but from a bundle of models, tools and rules.