agent-teams

Coordinating parallel work across components requires spawning a multi-agent team to share findings and synchronize tasks. This approach assigns clear ownership across layers like frontend and backend to prevent miscommunication and bottlenecks.

Multi-agent collaboration for competing hypotheses involves running parallel analyses with independent agents to compare results. Agents challenge assumptions and converge on a coordinated plan, ensuring comprehensive evaluation of different analytical paths.

Cross-layer coordination for frontend, backend, and tests is managed by spawning an agent team with clearly assigned ownership. Agents investigate specific layers, then share findings to synchronize review and converge on an integrated plan.

Yes, a defined team size is required to start multi-agent coordination. You must also load teammate context, implement plan-based work, and establish safe shutdown and cleanup protocols to manage the parallel workstreams effectively.

You should use multi-agent teams for task design when your project involves competing hypotheses, cross-layer coordination, or multi-module features. These scenarios benefit from synchronized review and parallel workstreams to prevent bottlenecks.

Safe shutdown and cleanup protocols for parallel agent workstreams require predefined termination procedures. The metadata specifies these protocols as a necessary condition for managing a multi-agent team, ensuring synchronized review concludes without bottlenecks.