I Thought I Was Reading a Repo

There are habits you don’t realize you’re carrying until they show up somewhere new. Mine came from cybersecurity. For years, my job wasn’t to understand software from the inside. It was to understand behavior.

Threat actors don’t leave explanations. They leave fragments. You build a model from partial evidence, test it, and discard it when it no longer fits. After a while, that way of thinking becomes instinct.

When I started trying to understand AI agents, Anthropic’s work on mechanistic interpretability gave me a way in because it felt familiar. The model begins as a black box and the work looks a lot like reverse engineering: trace its internal machinery and work backward toward an explanation.

Then I started reading through Prime Agent, an open-source agent from Prime Intellect. I approached the repo the same way: trace the flow, map the pieces, and follow execution. Pretty quickly I realized its runtime had changed the problem. Execution, state, and orchestration were now laid out in front of me. The model itself was still mostly opaque but the system around it wasn’t.

That reminded me of what EDR changed in cybersecurity. EDR didn’t tell you what an attacker was thinking. It gave you enough telemetry to reconstruct what happened: what ran, what changed, where it moved, and in what order.

Prime Agent gave me a similar feeling. I could trace how decisions moved through the system, what persisted, what called what, and where responsibility lived. There was finally enough evidence to reason from.

That was when I understood what had drawn me in. It wasn’t AI in the abstract, it was observability.

So this piece isn’t really an explainer. It’s a marker on the trail. I thought I was reading a repo. What I was actually recognizing was a familiar pattern: once behavior becomes observable, even an opaque system gives you something to reason from.