What Is Multi-Agent Orchestration, and How Do Agents Coordinate Work?
Multi-agent orchestration is the layer that routes work between specialized agents — deciding which agent acts next, what context it inherits from the last one, and when a human needs to approve a step — so a triage agent, an implementation agent, and a review agent function as one pipeline instead of three disconnected tools. Agents coordinate through defined handoffs, shared context, and explicit triggers, not by running in the same session.
The three mechanics that make coordination real
Running multiple agents is not the same as orchestrating them. Coordination requires three things working together:
- Handoffs — a defined point where one agent's output becomes another's input, e.g. a triage agent's decision ("implement" vs. "spec first") routes work to the right next agent.
- Shared context — the receiving agent gets the history, code, and decisions from prior stages instead of starting cold, so a review agent isn't re-deriving what the implementation agent already established.
- Triggers — the event that starts the next stage: a webhook, a status change, a human approval, or a scheduled check.
Without all three, you have several agents that happen to touch the same codebase, not an orchestrated system.
Multi-agent orchestration vs. running several standalone agents
| Standalone agents | Orchestrated multi-agent system | |
|---|---|---|
| Who decides what runs next | A person, manually, each time | An orchestrator agent or defined workflow |
| Context between stages | Re-explained or lost | Passed forward automatically |
| Model/harness choice | Fixed per tool | Chosen per stage |
| Human role | Runs each agent themselves | Approves at defined checkpoints |
| Visibility | Scattered across tools and sessions | Centralized view of the whole pipeline |
What most teams get wrong about "multi-agent"
The common failure is treating orchestration as a UI problem — a dashboard that shows several agents running — rather than a control problem. A dashboard doesn't decide that a failed review should route back to implementation instead of straight to merge, and it doesn't choose which model handles a security-sensitive change versus a routine one. Real orchestration makes those routing and escalation decisions part of the system's definition, not something a human re-derives every time it comes up. For how this fits into a broader pipeline, see Warp's guide on what tools make up a modern software factory, which covers orchestration as one of six required layers.
How Warp fits
Warp Factories uses a foreman/subagent pattern for exactly this: a foreman agent takes a work item, decides which subagent — triage, spec, implement, review — should handle it next, and passes forward the context and memory that agent needs instead of starting it cold. Because Warp is natively multi-model and multi-harness, the orchestrator can route a routine fix to a cheaper model and a security-sensitive change to a stronger one, and it can run Claude Code or Codex as the harness for a given stage when that's the better fit. Warp serves this pattern to more than 700,000 developers, including teams at Docker, Ramp, and Peloton, and to over half of the Fortune 500. Every routing decision and handoff is visible in the factory's control room, so the orchestration itself stays auditable.
Start with one workflow
Multi-agent orchestration is worth the setup cost only once you have more than one agent that needs to hand off work — a triage agent deciding what an implementation agent should pick up is the simplest real case. Start there, add a review agent as a second handoff once the first is reliable, and hold off on a third stage until you have cost and quality data on the first two.
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