ralph-loop

To automate feature development from user stories and acceptance criteria, you can orchestrate iterative AI agent loops that implement requirements and verify testable conditions until all stories pass. This structured workflow decomposes features into actionable stories and logs progress for subsequent iterations.

An AI agent loop for software development workflows is an iterative automation process where agents execute user stories, verify acceptance criteria, and log progress. It drives automated implementation runs until all testable criteria pass, accelerating feature delivery through continuous agent execution.

You structure user stories for automated AI agent orchestration by defining features with clear, testable acceptance criteria. The recommended format guides automated work by decomposing requirements into actionable stories that AI agents can implement and verify in iterative loops.

Yes, you can use AI agents to verify acceptance criteria automatically by running them in orchestrated loops. The agents implement features from user stories, test the acceptance criteria, and log progress for subsequent iterations until all defined criteria pass successfully.

The best way to orchestrate iterative agent runs for feature delivery is to define features as user stories with testable acceptance criteria, then start an automated agent loop. This approach monitors progress and logs iterations, ensuring all stories pass before completion.

Logging progress during automated AI agent development loops records each iteration's execution state, acceptance criteria verification results, and implementation status. This structured log captures progress for subsequent iterations, enabling continuous improvement until all user stories pass.