The article argues that AI “loops”—self-prompting agents using a goal, context, evaluation, and an agent—outperform single-shot prompts for long-running tasks. It outlines key components, real examples like PR babysitters and bug fixers, and explains why better models, built-in loop commands, and maturing toolchains make loops practical now.

This article traces the evolution of AI loops—small programs that run, check, and re-prompt coding agents—from early ReAct and AutoGPT examples to today’s durable, multi-agent orchestration with scheduling and self-verification. It shows why loop management, not model calls, is now the biggest cost in AI coding and outlines best practices: cap iterations, build reusable skills, and include feedback checkpoints.