orchestrating-swarms

You coordinate multiple AI agents for parallel code reviews by defining a team, assigning specialized roles like security or performance, and using message passing protocols to report findings back to a lead agent.

Multi-agent swarm orchestration is coordinating multiple AI agents using divide-and-conquer patterns. You need it for complex workflows, pipeline dependencies, and self-organizing task queues that benefit from parallel processing.

Build pipeline workflows with dependencies by applying task orchestration to define, assign, and track tasks across agents, enabling concurrent sub-task execution while managing team dynamics and message passing.

Yes, you run parallel tasks using Claude Code's TeammateTool and Task system by creating agent teams, spawning concurrent agents for different sub-tasks, and managing communication through message passing protocols.

Before setting up self-organizing task queues, you need understanding of agent primitives, team dynamics, task management, and message passing protocols to effectively divide complex workflows across a coordinated agent swarm.

You should not use a multi-agent swarm for simple, linear tasks that lack complexity, as the overhead of managing team dynamics, agent primitives, and message passing protocols outweighs the divide-and-conquer benefits.