Pop the Hood on an AI Agent: Loop Engineering on Open Models

Back in 2022 I did this the hard way: topic modeling in R on data pulled from the then-free Twitter API, to help a company decide which LMS to invest in based on what users and buyers were actually saying. Now, with agents and loop engineering, I wanted to see if I could build something similar on today’s tools, and break down what loop engineering actually is while doing it.

So I built a competitor-listening agent you can run weekly. Point it at your product and its rivals and it listens across open platforms, then drafts a brief on where you’re winning, losing, and the gaps to attack, on open models with no API keys needed to run it.

But the point of the demo is what’s inside it. When you use an agent in Claude, Copilot, or OpenAI, a loop is running that you never see. I rebuilt that same loop by hand, on open models, so you can watch every part of what an agent is actually doing under the hood. Live, I’ll run it in a Colab and walk the loop step by step: gather from open sources, do the cheap NLP work (sentiment, topic modeling) with free, validated tools, and reserve a single open-model call for the actual reasoning, which is reading the signal and naming the gap. Then it drafts the brief, proposes what to watch next, and I approve to widen the following week’s listening. You’ll see the code, the guardrails, and the repo (a runnable demo plus a fill-in template). It’s a live, Colab-fragile build and I’ll embrace the rough edges.