master-planner

Project planning for multiple AI agents involves creating a comprehensive plan detailing phases, dependencies, and verification gates. It generates self-contained sub-agent prompts and progress trackers, ensuring structured execution and coordination across parallel workstreams.

Coordinating multiple agents requires defining sub-agent responsibilities and creating clear execution artifacts like phase-specific prompts and progress trackers. This structured approach ensures independent agents can execute tasks concurrently while maintaining alignment with the overall project plan.

Generating sub-agent prompts involves creating self-contained execution instructions for each project phase. These prompts define specific responsibilities and boundaries, enabling independent agent execution without requiring constant context from the overall workflow.

Yes, workflow automation can scaffold a dedicated progress tracking file to monitor execution status, findings, and blockers across parallel workstreams. This provides a centralized view of project health and highlights dependencies affecting concurrent tasks.

Yes, project planning with verification gates supports rollback strategies for complex tasks. By defining clear dependencies and phase boundaries, the plan ensures that failed verification checks can trigger a structured rollback to a previous stable state.

Automated task breakdown should be avoided for simple, single-step tasks or projects with highly unpredictable, exploratory requirements. It is designed for multi-phase initiatives where structured execution, dependency mapping, and parallel agent coordination provide tangible value.