A research step (gather insights from across the codebase and internet for how to accomplish the next step), planning step (how should I sequence implementation given that research), an implementation step, and a verification step (code review of the implementation) is super effective workflow for me.

yup, as the blog says

> The full setup works with any project that has a benchmark and test suite.

so having a clear and measurable verification step is key. Meaning you can't simply give an AI agent a vague goal e.g. "improve the quality of the codebase" because it's too general.