jit-planning-lead

To plan and break down complex projects for AI agent workflows, you can use a lead agent to divide the project into manageable tasks while enforcing quality control and tracking progress. It handles dependency resolution and lifecycle tracking.

Dependency DAG management in project planning involves cycle detection and hierarchy resolution for task dependencies. This ensures that AI agents execute work in the correct order without circular blockers.

Yes, you can use version control with AI agent task planning because the system generates versioned JSON files. These files are diffable and mergeable like code, making them fully git-friendly for collaborative tracking.

Quality gates in AI agent work planning function as automated and manual checkpoints that record runs and structured findings. They enforce quality control throughout the project lifecycle before dependent tasks proceed.

To prevent write conflicts when multiple AI agents update project tasks, the system uses file locking and advisory work leases. This multi-agent safe design ensures only one agent modifies a file at a time.

Yes, you need the JIT platform to manage issues and enforce gates for work planning. The planning skill requires this platform to handle complex projects, track progress, and validate quality control checkpoints.