A few months back, I wrote about Loop Engineering: The Layer After Prompt, Context, and Harness Engineering, arguing that once your prompts are tuned, your context is assembled, and your harness is wired up, the thing that actually determines whether an agent works is the loop it runs in: how it decides to keep going, stop, retry, or hand off. A few readers asked a fair follow-up after that piece: loops over what, exactly? What happens when one job stops being one loop's worth of work?That question turned out to have an answer that was already forming across the AI engineering world by the time I went looking for it. Mid-July 2026 saw a fast, noisy round of debate on X about exactly this, kicked off by a question from OpenClaw creator Peter Steinberger about whether the conversation had already moved past loops into graphs. Within a couple of days it had a name: graph engineering. I want to walk through what it actually means, where it overlaps with loop engineering, and where I think the skeptics in that debate had a fair point.