pi-planning-with-files

Persistent memory for an AI agent is maintained by writing plans, findings, and progress logs to structured markdown files like task_plan.md, ensuring context is preserved across long sessions without drift.

File-based planning uses markdown files to manage complex agent workflows through structured phases, progress tracking, and detailed error logging. It enforces critical rules and protocols to prevent hidden errors and goal drift.

Recover an interrupted AI agent session by reading the progress.md and findings.md files. The file-based planning system supports session recovery by logging detailed errors and tracking task completion systematically.

Yes, you can use markdown files for context engineering by providing long-term storage for research and plans. They enforce a structured workflow with clear phases and error protocols suitable for complex multi-step tasks.

Prevent goal drift in AI agent workflows by implementing a structured file-based planning system. It enforces clear planning patterns with critical rules, such as the 2-Action Rule, and maintains focus through systematic progress tracking in markdown files.

AI agents lose context during complex research due to volatile memory. Implementing persistent memory with markdown files for task plans and findings mitigates this by systematically recording decisions and progress.