Direct answer
What is multi-agent orchestration?
Multi-agent orchestration coordinates which specialist agents run, in what order or in parallel, what context and tools each receives, how state and outputs move between them, when control is handed off, how failures recover, and where a human intervenes. It can be model-directed, code-directed, or a hybrid of both.
Reviewed by Raltic Research · Updated August 15, 2026
The short answer first, followed by the operating details and next decision.
Specialization helps only when task state and ownership survive every handoff.
- 01
Route
Choose a specialist by code, model decision, or explicit graph.
- 02
Context
Transfer the task, sources, constraints, state, and expected output.
- 03
Coordinate
Run sequentially, concurrently, through handoff, or under a manager.
- 04
Resolve
Merge results, recover failures, escalate uncertainty, and review.
What are common multi-agent patterns?
Common patterns include manager-and-specialists, handoffs, sequential pipelines, parallel fan-out, review loops, and group collaboration.
OpenAI Agents SDK distinguishes agents-as-tools from handoffs. Microsoft Agent Framework documents sequential, concurrent, handoff, group, and manager-led patterns. The best pattern depends on whether the model or code should decide the next step.
- 01Manager calls specialists
- 02Triage handoff
- 03Sequential pipeline
- 04Parallel investigation
- 05Generator and reviewer loop
What must an orchestration layer preserve?
It must preserve task state, context boundaries, outputs, ownership, and recovery information.
Passing an entire transcript can be expensive and unsafe; passing too little causes repeated work and lost constraints. Define the handoff packet and who owns the final result before adding more agents.
When should a team use multiple agents?
Use multiple agents for clear specialization, independent critique, parallel evidence gathering, or incompatible runtime needs.
Do not create several personas for a task one agent can complete reliably. Every additional agent adds routing, context, latency, cost, failure, evaluation, and ownership work.
- 01Specialist capability
- 02Independent check
- 03Parallel speed
- 04Runtime or permission boundary
- 05Measured improvement over one agent
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