ralph-supervisor

To prevent context drift in autonomous coding loops, you can apply a pattern that starts each iteration with a fresh context. This approach resets the AI's working memory to eliminate stale assumptions and stabilize long-running development sessions.

The best way to persist progress between automated coding sessions is using Git-backed persistence. This approach commits completed work after every iteration, ensuring traceability and allowing you to track outcomes over time without losing state.

Validation gates work by requiring verification of the generated code before progression to the next iteration. This structured flow ensures that each automated coding cycle meets quality standards before the AI supervisor allows further development.

Yes, you can supervise an autonomous coding loop using a dual agent setup where one AI acts as the supervisor and another as the executor. This pattern applies to iterative development tasks, coordinating context refreshes and automated reviews.

To get started with automating an iterative coding loop, initialize a session, add your development story, run the iteration loop, and finally review the results. This workflow applies validation gates and persists progress via Git.

Your AI coding session loses track of learnings over time due to context drift in long-running sessions. You can solve this by applying a lightweight memory system that carries outcomes forward to inform and improve future iterations.