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Do YOU use LOOPs
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.
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Do YOU use LOOPs
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