iterative-planner

To stop an AI agent from losing focus during complex multi-step tasks, use a state-machine-driven iterative planning process. This enforces a structured Explore, Plan, Execute, Reflect, and Pivot cycle, maintaining progress and recoverability.

Filesystem persistent memory for AI task management records every decision, failed attempt, and discovery as files. This prevents context rot and information loss, ensuring complex projects remain auditable and recoverable across multiple execution states.

The best way to plan refactoring a large codebase with AI is enforcing task decomposition through a structured state machine. Iterative planning guides the agent sequentially through exploration, planning, execution, reflection, and pivoting.

Yes, you need Node.js installed as a prerequisite to run the iterative planner. The environment provides the runtime necessary to execute the scripts that enforce the state machine and manage the persistent filesystem memory.

AI repeats errors during complex development tasks due to context window limitations causing information loss. Implementing a structured state machine with persistent filesystem memory logs previous attempts, preventing unproductive loops.

You can recover an off-track AI task by utilizing a state machine that logs every decision and failed attempt. This persistent memory allows the execution process to resume from a known state instead of restarting.