Governance built for the AI agent and architecture, not just the model.
Most AI governance today is based on policy language, but agentic AI mandates a different approach.
The governance layer needs to be part of the infrastructure, including the permission engine, the context pipeline,
the tool-use boundary, the memory store, the delegation pattern.
This short diagnostic tool scores your deployment across the four critical
areas that regulators, auditors, and your board will eventually ask about.
of banks are investing in comprehensive AI governance frameworks.1
of enterprise AI users bring unauthorized tools to work — the Shadow AI crisis.2
of senior banking decision-makers feel competitive pressure from faster, more agile entrants.1
cite emerging non-financial risk — including AI — as material to medium- and long-term strategy.1
1 Moody's, The Intelligence Edge: Banking's New Decision Advantage, May 2026 (n = 348 senior banking decision-makers). 2 NextFi Advisors, Lessons For Enterprise AI Deployments, April 2026.
Agentic AI governance is not a single discipline. It is the intersection of infrastructure design, regulatory standards, financial-services risk, and a new class of failure modes that classical model risk frameworks do not see.
Answer 24 questions. Get an instant tier score, per-lens breakdown, and your top three gaps. No email required to see your results.
The free diagnostic surfaces where you stand. The paid Agentic AI Governance Review is the regulator-ready engagement that closes the gap — fixed scope, fixed timeline, fixed fee.
Out of scope (separate engagements): implementation, tooling selection, ongoing monitoring, regulatory representation. Kept narrow on purpose.
Run the diagnostic first, or skip ahead and schedule a 20-minute scoping call.
