AI Orchestration
Definition
AI orchestration coordinates models, agents, tools, and deterministic process steps in a shared workflow.
Orchestration defines sequence, handoffs, parallelism, states, error handling, and approvals. It is an architectural pattern, not a single standard or provider.
Simply explained
How to define a workflow that decides which specialist or tool acts when.
Why it matters in practice
Relevant for multi-stage AI automations involving multiple systems and clear control points.
Example from business
A complaint workflow links classification, ERP query, text drafting, and human approval in a defined sequence.
Limits and pitfalls
- Avoid using too many agents for simple tasks; do not model states and error cases; let LLMs decide over deterministic steps.
Origin & history
Workflow and multi-agent orchestration builds on classic workflow concepts and is implemented in current agent frameworks as an explicit coordination pattern.
Use in mid-sized companies
- Possible application in SMEs: Add multi-stage processes with agents only where flexible language or decision logic is truly needed.
Frequently asked questions
Is AI orchestration the same as an agent?
No. Does every workflow need multiple agents? No.
Sources
- Workflows · Microsoft Agent Framework, Workflows
Sources checked on 10.10.2026.
Editorially reviewed on: