todo

To prevent AI context drift in long-running project workflows, externalize task state to a persistent todo.md file. This anchors multi-step AI tasks to user intent by storing progress and checkpoint milestones at the project root.

The best way to track progress for multi-step AI tasks is using a deterministic init, check, score, and clear lifecycle. This workflow archives progress milestones in a single file to maintain alignment and prevent context loss.

To re-orient an AI task after pausing a long-running workflow, use a defined recovery workflow that reads the stored state. This approach restores context and re-aligns the AI with the original user intent after scope changes.

You need external state persistence for AI task coordination when executing multi-step projects that require ongoing progress tracking. It prevents goal drift by maintaining mid-task checkpoints outside the model's context window.

Yes, project management state persistence works without external dependencies by using a local todo.md file at the project root. This approach stores task state and milestone checkpoints directly in the repository.

Your AI loses track of goals during extended multi-step workflows due to context loss over time. Externalizing task state to a persistent file prevents this drift by providing mid-task coordination and clear recovery checkpoints.