The most common failure we see in enterprise AI work is not a bad model. It is a team with no way to tell whether a change made things better. Without a graded test suite, every model swap is a leap of faith and every regression is discovered by a user.
The harness comes first. Assemble a representative set of inputs with agreed-correct outputs, define what a graded pass means for your workflow, and wire it into the pipeline so no model or prompt change ships without a score. This is ordinary regression testing, and it is treated as optional far more often than it should be.
Regulated workflows add a second requirement: every decision needs to be reconstructable. That means pinning model versions, recording the exact inputs, and storing the output alongside the version that produced it. Months later, when someone asks why a claim was classified the way it was, the answer has to be retrievable rather than inferred.
None of this is exotic. It is discipline applied to a probabilistic component, and it is the difference between a system that passes audit and a demonstration that impressed a steering committee.