4 Types of AI Agent Loops, and the One Mistake That Breaks Most of Them

Two developers built AI agent loops last month; one ran for six hours with little output, the other never ran unattended. The common mistake was failing to define what the loop could do autonomously, a decision that determines whether a loop works or wastes time.

Member-only story 4 Types of AI Agent Loops, and the One Mistake That Breaks Most of Them A practical framework for designing loops that know when to run, when to stop, and what to hand off, without burning your token budget or babysitting every restart. Two developers built agent loops last month. One’s ran for six hours straight, touched dozens of files, and produced almost nothing usable. The other’s never ran unattended, not even once, despite being called a “loop” from day one. Neither had a broken agent. They shared the same blind spot: neither one had decided, on purpose, what the loop was allowed to do without them standing over it. That single decision, more than the model you pick or the clever prompt you write, is what separates a loop that works from one that quietly wastes your week. In this article, you will understand how production-grade loops are built. If you want more such information about AI, consider subscribing to my newsletter, where you will get noise-free AI information every week Link for the newsletter: Newsletter https://aiengsimplified.beehiiv.com/