Multi-Agent Orchestration

An orchestrator manages the lifecycle around agent decisions. It creates tasks, assigns owners, routes results and decides when downstream work may start. It can use deterministic rules, an agent supervisor or a combination. Coordination patterns describe the topology; orchestration supplies the running machinery, including timeouts, retries, cancellation and shared-state handling. A disagreement between outputs is also distinct from a write conflict in shared data, and each needs its own policy. The system must know whether a participant has completed useful work, needs input or merely stopped generating text.

The practitioner defines task contracts, dependency conditions and ownership of mutable artifacts. Independent tasks can run concurrently, but dependent work waits for accepted results. Durable status and idempotent dispatch prevent restarts from duplicating external effects. A useful implementation includes a task graph, event history and recovery tests. Failure policy should specify whether a missing worker triggers retry, alternative assignment or an incomplete deliverable. The final assembly step checks consistency and evidence rather than simply concatenating participant responses.

An orchestrator cannot turn a flawed worker result into a correct one merely by tracking completion. Concurrent agents can race on files or overload shared services. Automatic retries may repeat expensive or consequential actions. Quality includes explicit ownership, bounded retries and tested behavior after partial failure. Complex scheduling is justified by actual task dependencies; an elaborate execution graph can otherwise create operational failure modes that a simpler coordinated sequence would avoid.