Knowing When to Stop: The Art of Making a Loop Converge
Summary
The article analyzes loop engineering in AI, exploring how to decide when a loop should stop and what constitutes 'done'. It outlines the four completion criteria (target state, observable current state, precise changes, stopping rule), discusses the role of verifiers and infrastructure, and delves into the economics and practical stack needed to manage iterative AI work.