executing-plans

Executing an implementation plan with verification requires reading the plan critically first, then following bite-sized tasks strictly while running required verifications at each checkpoint and tracking in_progress or completed states to prevent drift.

The best way to handle code execution blockers is to use a stop-on-blocker behavior that immediately halts the workflow when verification fails or steps are unclear, preventing out-of-scope work until clarification is provided.

Managing development handoff after completing a planned coding task requires an end handoff process to a dedicated finishing workflow, ensuring final verification and safe completion options are coordinated before merging or closing the branch.

Yes, you can run an implementation plan in an isolated workspace. Workspace isolation ensures the agent follows pre-specified requirements strictly, adheres to task checkpoints, and stops immediately if verification fails or instructions are blocked.

AI agents drift from the original plan during code execution when they lack strict step adherence and critical review checkpoints. Forcing in_progress and completed task states alongside immediate stop-on-blocker behavior prevents this drift.