orchestration

To coordinate parallel AI agents, you decompose user requests into independent tasks, spawn WORKER agents with a consistent preamble, and synthesize their results. This orchestration process forms a scalable workflow that reduces latency and manual coordination.

Task graph management enforces dependencies and sequencing by using TaskCreate, TaskUpdate, TaskList, and TaskGet operations. This mechanism ensures complex AI workflows execute stages in the correct order and dynamically balance workloads across multiple agents.

The best way to synthesize diverse results from multiple AI agents is to aggregate outputs from spawned WORKER agents into a cohesive final deliverable. This synthesis consolidates parallel task results while providing progress updates throughout the workflow.

You need to decompose requests into parallel tasks when facing complex scenarios requiring staged approvals or dynamic workload balancing. This approach is ideal for problems where independent sub-tasks can execute concurrently to reduce overall latency.

Yes, you can use this orchestration approach for complex problems requiring staged approvals. It manages dependencies and sequencing through task graphs, allowing you to coordinate parallel execution and synthesize outcomes for staged workflows.

Manual coordination fails when managing a swarm of AI agents because it cannot efficiently handle dynamic workload balancing and dependency sequencing. Automating task graph management with TaskCreate and TaskUpdate enforces structure and reduces latency across parallel tasks.