An Introduction to Loop Engineering in Machine Learning

A new guide explores the history and mechanics of "loop engineering," tracing its conceptual origins back five years rather than treating it as a sudden recent trend. It highlights how the practice has evolved from manual prompting to designing automated, iterative systems for AI agents. Loop engineering shifts the focus from humans writing individual prompts to designing systems where AI agents autonomously prompt, test, and iterate on themselves. Understanding its historical context helps developers build more robust agentic workflows instead of just following temporary industry hype. Rather than a single breakthrough, loop engineering relies on recursive goals where an AI acts, observes the outcome, and refines its approach until a target is met. This architecture underpins modern AI coding agents like Claude Code, which write, test, and debug software autonomously.