Short Summary of AI Loops AI loops are cycles in which an artificial intelligence system performs a task, checks the result, corrects failures, and repeats the process until it reaches a goal or a defined limit. There are three main types: - Turn loop: the AI reviews its own work before responding. - Goal loop: it keeps working until verifiable criteria are met. - Time loop: it repeats a task at scheduled times or intervals. In Claude Code, /goal keeps the AI working until a condition is proven, while /loop repeats a task at a time interval. Stop Hooks can control whether work continues after each turn. Auto mode makes tool use easier, but it does not create a complete loop by itself. A reliable loop needs a clear objective, success criteria, evidence, time or attempt limits, and a stop condition. Loops can consume many tokens when they involve large contexts, many retries, multiple agents, or repeated checks. The main idea is: Do not simply ask the AI to keep going. Define the goal, how it will be verified, when corrections should happen, and when the process must stop. A well-designed loop increases autonomy. A poorly designed loop increases errors, rework, and cost.