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.
