Steal this AI maturity scale to guide your team toward loop engineering. 👇 (sound on 🔊) Most teams think they're at one "stage" of AI adoption. They're not. They're at all of them at once. We spent the last few quarters talking to hundreds of software teams about how they really use AI and that surprised us. Most people picture AI adoption as a ladder: autocomplete → prompting agents → "loop engineering," where agents run scheduled tasks, wired into your systems, operating on their own. But the teams we talk to are doing all three at once. Autocomplete in one repo. Agents shipping features in another. Already running loops against their own infra. What's consistent is the direction: more autonomy for agents, hand off the recurring and mechanical work, let people move up to the part that still needs a human. Most teams are further down that road than they realize. More autonomy brings more entropy and runaway cost if you scale agents uncontrollably (we've watched companies do exactly that). Plus real change, like reworking your Git flows to handle looping agents. I pulled the best insights from those conversations into the video. Watch it and you'll probably spot where your team sits.

Maybe someone can help me: what is effectively the difference between using claude code's ultracode (which you can define a goal and it uses workflows with many agents etc) and loop engineering? isn't it effectively the same?