Developers can now bootstrap the Ralph AI agent workflow using `npx ralph-scaffold`. Ralph is an iterative coding loop where an AI agent repeatedly implements stories, runs tests, and learns from mistakes. The setup uses a bash script, a prompt file, a task list in `prd.json`, and a `progress.txt` log to track learnings across iterations.

The workflow addresses context drift by restarting the agent with fresh context each loop, supplying notes from previous attempts. However, this method can dramatically increase token usage, especially if the agent repeatedly fails. The author recommends using Claude's premium plans over API-based tokens to manage costs, noting that token consumption can be "nuts" for this approach.

Ralph is best suited for small task stories with clear acceptance criteria and reliable feedback loops like typecheckers or linters. The process is driven by a bash loop that pipes prompts into an agent, which picks the next story, implements it, commits if passing, and logs learnings. Memory persists through Git commits, `progress.txt`, and `prd.json`, creating a cumulative record of patterns and gotchas.