ralph-loop

Autonomous loop development for prd.json is automated by reading user stories, implementing tasks, running tests, updating progress.txt, and restarting context after each cycle. This enforces a single-task-per-cycle workflow to iteratively complete all defined requirements.

Tracking progress and preventing context pollution is handled by automatically recording results in progress.txt and restarting context after each development cycle. This approach ensures the AI agent maintains a clean state and avoids status contamination across iterative loops.

Context restart for autonomous AI agents works by completing one task per cycle, saving the outcome, and resetting the operational context. This mechanism prevents state pollution and allows the agent to resume subsequent user stories defined in prd.json with a clean environment.

Yes, AGENTS.md and prd.json are required to run autonomous development cycles. The workflow reads these files alongside progress.txt to identify pending user stories, execute tasks, enforce the single-task-per-cycle rule, and mark stories as passed when tests succeed.

An AI agent stops after one user story because the workflow enforces a single-task-per-cycle rule. After executing the task, it records results, updates progress.txt, and relies on the system to restart context for the next pending story in prd.json.

Yes, this autonomous loop is designed for multi-stage software iterations and requirement implementation. It continuously selects the first unfinished user story from prd.json, implements it, runs tests, and updates progress until all stories are marked as passed.