swarm-coordinator

Parallel development tasks are coordinated through a shared task list and Directed Acyclic Graph (DAG) that manage task claiming and dependencies. An orchestrator session monitors global state and runs final integration tests while worker sessions execute distinct project modules.

Distributed task management prevents file conflicts by enforcing a worker protocol for claiming and completing tasks. This protocol acts as a claiming mechanism to prevent race conditions and provides a framework for resolving file conflicts across concurrent sessions.

Yes, you can generate a Directed Acyclic Graph (DAG) of tasks from an implementation plan to define dependencies. This allows distinct modules like API, database layer, and authentication to be assigned to separate agents and built concurrently.

Scaling large-scale implementations is best handled by true parallelism where an orchestrator session monitors global state. This accelerates development time by allowing multiple AI agents to work concurrently on distinct tasks within the same shared project.

Parallel AI agents should not be used for development workflows when a project cannot be divided into distinct, independent tasks. Coordinated parallelism requires separable modules to prevent file conflicts and ensure the orchestrator can run meaningful final integration tests.