Perhaps you've heard the term Loop Engineering: instead of solving a problem by hand, you build a system, set a measurable goal, and let an agent keep iterating until it gets there. It sounds great until something goes wrong. So I sat down with Annie Wang to talk through the four most common ways Loop Engineering breaks down, and how to fix each one. What's in the video What Loop Engineering actually means: building an agentic system that retries toward a definable goal Failure #1 - runaway loops: you need a hard stop rule because tokens cost real $$$. Failure #2 - unverified autonomy: why letting an agent grade its own work is like asking a kindergartner to grade its own homework, and why you want agent A checking agent B's work instead Failure #3 - vague or uncheckable goals: why "make this better" breaks an LLM, and how to write criteria that are actually non-negotiable Failure #4- complexity overflow: when a single loop chokes on a big task, and why that's the moment to move from Loop Engineering to Graph Engineering Have you hit any of these failure modes yourself? Tell me which one (or more) got you.