Josie Cochran

Something strange happened last week while working on a side project we've been building alongside Murror.

I described what I wanted a feature to do not how, just what. The AI built it. I tested it. It worked. Not just "worked" it handled edge cases I hadn't even thought of.

One thing I've noticed is that people often look for an "AI governance tool" as if governance is a single category.

In practice, it usually looks more like a stack. Different controls solve different problems, and most teams end up combining several of them as AI agents move from experiments into production.

1. Model Monitoring

This is the layer that helps teams understand how AI systems behave over time. Performance drift, unusual activity, reliability issues, and changing usage patterns tend to show up here first.

https://github.com/hetima/sigma-...
It should already be available in the app