Planning with Files

To maintain AI coherence and prevent hallucinations, externalize memory to disk using structured files like task_plan.md, findings.md, and progress.md, ensuring persistent context across iterative refinement steps.

The best way to track AI task progress is by externalizing memory to disk, capturing plans, observations, and progress in structured markdown files to provide a deterministic workflow and auditability.

To set up persistent storage for AI planning, create task_plan.md and findings.md in your workspace, then begin the execution loop following the 3-Strike protocol to enforce a structured, repeatable workflow.

Yes, externalized memory files improve AI workflow reliability by applying deterministic guardrails like the 2-Action Rule and 3-Strike Protocol, which provide structured logging and artifact creation for traceable operation.

You need to externalize memory to disk for multi-step AI tasks requiring persistent context, such as iterative planning, logging observations, and phase-based execution, to prevent hallucinations and maintain long-term coherence.

Using markdown files for AI memory management requires manual initialization and adherence to guardrails; without following the 2-Action Rule and 3-Strike Protocol, the structured workflow may lose traceability and fail to prevent hallucinations.