Governed validation agents in model risk management

As regulatory standards tie revalidation to model materiality and inventories grow with machine learning, vendor, and generative AI models, manual validation gets harder to sustain. Validators spend the first pass assembling code, data versions, and documentation when they should be challenging models. AI agents can take on that assembly, but only if every finding stays traceable to its evidence and to a validator who owns the decision.

In this white paper, you’ll find:

• A governance framework for validation agents that propose and never approve
• Policy as code that turns your standards into enforceable workflows
• Three agent roles that draft tier proposals, flag documentation gaps, and run challenger tests
• Five rules that keep each finding defensible when an examiner asks how it was reached

Download it to compare four approaches to validation and see what a reproducible audit record requires.