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You can persist task planning state by writing planning artifacts like task_plan.md, findings.md, and progress.md to disk as a memory layer, ensuring context is preserved and recoverable across long-running agent sessions.

Recovering an interrupted multi-step planning task involves using script-based session recovery utilities like init-session and session-catchup to read persisted progress files and resume execution from the last completed phase.

Filesystem-backed memory for long-running research tasks works by continuously saving findings and phase-based plans to disk, creating a durable state record that prevents context loss during complex, multi-step iterative work.

Yes, managing multi-phase task planning on disk requires structured templates to initialize task_plan.md, findings.md, and progress.md files, which guides the workflow and enforces single-tool-per-turn execution constraints.

An agent loses context during complex iterative project work because session state is held in memory only; persisting planning artifacts to disk as a memory layer prevents this loss across tool interactions.

Yes, you can use provided scripts like init-session and session-catchup to automate planning workflow initialization, manage session state recovery, and handle errors during long-running multi-step tasks.