planning-with-files

You can maintain context for long-running tasks by creating external planning files like task_plan.md, findings.md, and progress.md to persistently track goals, decisions, and session logs across interruptions.

The best way to plan complex workflows with many tool calls is to use an external memory system that generates task_plan.md for phase tracking and progress.md for session logging, ensuring disciplined execution and traceable decisions.

You can recover AI agent task progress after an interruption by maintaining an external memory file like progress.md, which logs session history and allows seamless resumption with preserved goals and context.

You need external memory files for AI agent planning when executing long-running, multi-step tasks that require many tool calls, ensuring you preserve context, track phases, and maintain traceable decisions reliably.

Yes, this task planning approach works without external dependencies by using built-in scripts to initialize and manage local markdown files for phase tracking, research documentation, and progress logging.

A limitation of using markdown files for persistent task planning is that managing task_plan.md, findings.md, and progress.md requires manual or scripted synchronization to ensure session logs and phase tracking remain consistently updated across complex workflows.