planning-with-files

To maintain persistent state for complex multi-step development workflows, you can use a file-based planning system that records task plans, findings, and progress logs directly on disk. This prevents information loss during long-running projects by keeping a durable working memory.

The best way to track AI agent decisions and research findings is by logging them into structured markdown files like findings.md and progress.md. This enforces a clear pattern for task execution and error handling, ensuring critical discoveries are meticulously recorded and easily retrievable.

To start file-based task management for a new software feature, create task_plan.md, findings.md, and progress.md files in your project directory before initiating the task. This establishes a structured workflow that enforces rules for action repetition, error logging, and context management.

Yes, file-based planning is designed specifically for multi-week research projects, ensuring all critical information, decisions, and progress are meticulously logged. It prevents the AI from losing track of goals or forgetting key discoveries by maintaining persistent state and decision logs.

Your AI forgets previous discoveries during extended task execution because it lacks persistent working memory. By implementing a file-based system that logs actions and findings to markdown files, you enforce context management rules inspired by Manus principles to prevent this information loss.

Yes, you can use markdown files for error logging and context management by creating a structured system that enforces rules for action repetition and error documentation. This approach logs errors directly into progress files to maintain a persistent record of task execution states.