I've been seeing a term floating around AI circles this year: "loop engineering." First reaction — another buzzword. So I went and checked where it actually came from before repeating it. Turns out it's real. Coined by a well-known engineer earlier this year, building on ideas a couple of others were already circling. But here's what surprised me: the underlying idea isn't new at all. Researchers were studying "reason, then act, then check, then decide again" loops back in 2023, and the pattern traces back decades further, into old control-theory and feedback-loop thinking. So what's actually new? Not the technique. The name gave people a shared way to talk about something that used to get scattered across three separate conversations — prompt design, agent reliability, AI observability. Once there's one name for it, you notice it's really one discipline: how do you design the trigger that starts an agent, the planner that decides its next move, the verifier that checks the output, and — the part everyone skips — the stop-rule that tells it when to quit? That last one is the piece I've started paying real attention to. I found a case where a company burned through an entire year's AI-tooling budget in about four months, spread across thousands of engineers, before anyone capped individual spend. Nobody meant for that to happen. It's just what a loop does when it keeps running and no one designed an explicit stopping condition. Here's what I've taken from digging into this: when I build anything agent-like now, I ask the stop-rule question before I ask the reasoning question. Not "how smart can I make this," but "how does it know when to stop, and what happens if it's wrong for a while before anyone notices?" It's a boring question. It's also the one most people skip, and it's usually where the real cost — in money or in mistakes — hides. Still figuring out where the line is between a good-enough stop-rule and over-engineered guardrails. If you've built agent loops and have a rule of thumb for that, I'd like to hear it.

If I asked you to place your business on a five-stage scale, from doing nothing with AI to running on a full AI-driven operating system, where would you land? Here's the scale, in plain terms. Stage 0 — Manual everything. Spreadsheets, email chains, phone calls. The tools exist, but a person is the bridge between all of them, and that person is usually the owner. Stage 1 — Partial AI, no integration. ChatGPT for emails, a Zapier trigger here and there, Copilot in Word. Each tool helps a little, but nothing talks to anything else, so you're still re-entering the same data by hand. A lead comes in, gets enriched, updates the CRM, and a follow-up drafts itself — AI inside the real workflow, not bolted on top. A person still reviews before it goes out, but the grunt work is gone. Call this Stage 2. Stage 3 — Agents, not just automations. Multiple agents each own one recurring job — follow-ups, reports, invoice chasing — with human approval only at the decisions that actually matter. This is also where a system starts accumulating months of context about how you actually work — the part a competitor starting from zero doesn't have. You check in, handle the exceptions, and the rest runs — a self-managing operating layer. Most businesses I talk to aren't here yet, and that's fine — the point isn't to rush the ladder, it's to know which rung you're actually on. This is Stage 4. Most SMBs sit at Stage 0 or 1. That's not a criticism — it's just where the gap between "using AI" and "running on it" tends to live. Where would you place yourself?