planning-with-files

Persistent state management for multi-step tasks is achieved by creating and updating task_plan.md, findings.md, and progress.md files on disk. This external memory pattern prevents information loss during long-running operations and project development.

Tracking research findings and progress logs in markdown files is handled by writing discoveries to findings.md and session logs to progress.md. This structured file I/O approach ensures critical decisions and encountered errors are meticulously recorded.

Markdown files can be used for deterministic task management by utilizing file I/O, shell commands, and globbing to manage task_plan.md. This approach provides reliable state persistence without requiring a database or external dependencies.

This persistent planning approach works for multi-day software development projects by serving as an external memory for AI agents. It logs research, decisions, and errors, allowing for seamless resumption and clear oversight of complex phases.

AI agents lose context during long-running task operations due to a lack of state persistence. Creating dedicated markdown files for task plans and findings solves this by providing external memory that survives session interruptions.

File-based state persistence for task management is needed when tackling complex, multi-step operations that require meticulous tracking of phases and discoveries. It prevents information loss by maintaining continuous external memory.