# Loop Engineering: Build AI That Knows When to Continue, Retry, or Stop Canonical: https://brew.new/templates/substack/loop-engineering-build-ai-that-knows-when-to-continue-retry-or-stop Brand: substack.com Category: newsletter ![Preview of Loop Engineering: Build AI That Knows When to Continue, Retry, or Stop](https://cdn.brew.new/email-preview-d90a82a675b72ebc-tracking_r57yxv59qhyj6g7x79nh8y22rh8e75kn-1789089649806.png) ## Email content Forwarded this email? Subscribe here for more Loop Engineering: Build AI That Knows When to Continue, Retry, or Stop Turn one agent into a reliable workflow with state, tools, checks, approvals and a project you can publish on GitHub. ChatGPT Sep 10 ∙ Preview READ IN APP You have probably seen this happen. You give an AI tool a real job: read the brief, check the figures, draft the report and tell you what is missing. The first answer looks good enough to keep. Then you notice one outdated number. You ask for a correction. The number changes, but a requirement disappears. You ask again. The answer becomes longer, more confident and somehow less useful. After a few rounds, you are no longer delegating the work. You are supervising a very fast intern who forgets what happened five minutes ago. The obvious conclusion is that you need a better model or a cleverer prompt. Sometimes you do. More often, the missing piece is the loop around the model. This is the next part of our practical AI series. Prompt Engineering taught us how to give the model a clear job. Context Engineering showed us how to give it the right surroundings. Memory Engineering, RAG, and Knowledge Graph Engineering added memory, retrieval and relationships. Harness Engineering made the environment safer and more observable. Loop Engineering answers the next question: How does the system know what to do next, whether the last step worked, and when it should stop? A loop is not an AI being told to try again. It is a controlled cycle that loads state, takes one action, observes the result, checks it against the goal and then chooses whether to continue, repair, ask for approval or finish. That distinction matters because repetition without feedback only repeats mistakes. A useful loop needs an independent check, a memory of what already happened and a limit that prevents it from consuming time, money or permissions forever. In this article, I’ll take you from a manual loop you can run in a chat window to a single-agent workflow, then show how to add RAG, memory, approvals and a multi-agent graph without losing control. Below, I break down the loop contract, three practical projects, the tests that tell you whether the system works, and the GitHub structure that turns your result into evidence of skill. You will build the first version with ordinary files and Python before deciding whether a framework earns its place... User's avatar Continue reading this post for free in the Substack app Claim my free post Or upgrade your subscription. Upgrade to paid Like Comment Restack © 2026 ChatGPT 548 Market Street PMB 72296, San Francisco, CA 94104 Unsubscribe Get the appStart writing [Open and remix this design](https://brew.new/templates/substack/loop-engineering-build-ai-that-knows-when-to-continue-retry-or-stop) [Browse email designs](https://brew.new/browse/templates)