Agentic Governance & Model Risk Controls
A control pattern for AI that has to move fast without losing review, assurance, or the trust of the people who answer for the decision.
- Focus
- Governance evidence, approval design, model risk, and escalation paths
- Data posture
- Synthetic examples and generalized operating patterns
- System posture
- Controls before autonomy; auditability before scale
- Primary audience
- Finance, operations, risk, compliance, and executive teams
What it frames
- Approval gates, so no AI recommendation moves money before a human signs off.
- A full evidence trail: the prompt, the sources, the assumptions, and who decided what.
- A clear line between AI that advises and the systems that actually execute.
- Synthetic test cases for judging agent behavior, with no confidential data at risk.