orchestration

Coordinate parallel AI agents by decomposing complex requests into independent background worker tasks. This approach enables rapid synthesis and parallel analysis for codebase reviews, feature design, and bug investigations while maintaining human oversight.

The best way to manage multi-step AI tasks is using an orchestration framework that spawns specialized background agents. It enforces TaskCreate and TaskUpdate workflows, applies orchestration rules like no sub-agent spawning, and aggregates outputs into cohesive results.

Parallel task orchestration supports long-running asynchronous workflows via non-blocking background agents. You create tasks, spawn agents, and monitor progress through asynchronous notifications to synthesize final outputs without blocking execution.

You need a swarm of specialized AI worker agents when facing complex, multi-step tasks like documentation projects or bug investigations. It allows independent tasks to run in parallel, accelerating results and enabling rapid synthesis of comprehensive outputs.

Orchestrated AI workflows enforce a strict limitation: background worker agents cannot spawn further sub-agents. This constraint ensures predictable coordination, maintains human oversight, and prevents uncontrolled agent hierarchy during task execution.

You can load domain references and skill guides during task orchestration to align AI agents with best practices and governance. This domain-guided prompting ensures worker agents follow specific organizational standards during execution.