ralph-loop

Autonomous AI coding loops are set up by launching the setup wizard to configure iterative development cycles, managing state and validation until task completion criteria are met. This enables self-correcting code generation that runs autonomously in the background.

An iterative development loop for AI coding is a process where an AI agent repeatedly attempts, validates, and refines code until a task is successfully completed. It orchestrates state management and applies self-correcting logic to ensure automated bug fixing and feature generation meet defined criteria.

Yes, autonomous code generation requires integration with task management systems like Archon to manage progress tracking and state persistence. This integration ensures the iterative loop maintains status updates and validates task completion across background development tasks.

Yes, you can use automated bug fixing to have an AI agent continuously attempt to fix failing tests for a new feature. The self-correcting loop automatically commits successful changes and updates the task status upon completion, running autonomously until validation passes.

Limitations of self-correcting code generation include the dependency on external task management systems like Archon for state persistence and progress tracking. Without proper integration, the iterative loop cannot validate task completion or manage status updates for background development tasks.