planning-with-files

You can maintain persistent working memory for an AI agent by managing markdown files on disk, such as tracking task plans and logging chronological progress, to ensure continuity across multi-step operations even after restarts.

The best way to track multi-step task plans is to use disk-based markdown files like task_plan.md and findings.md to store high-level goals, architectural decisions, and research, preventing context loss during large refactoring.

File-based context management works by writing actions to a chronological progress.md file, allowing the agent to read the log upon restart to seamlessly resume the workflow without losing previous state.

Yes, the file-based planning approach handles execution errors by implementing a 3-Strike protocol for robust error management, ensuring reliable execution of multi-step tasks when failures occur.

You need to use disk-based markdown files for agent memory when executing complex, multi-step operations that require continuity and reliability across sessions, ensuring state persists even if the session is interrupted or restarted.