Most conversations about agentic AI focus on autonomy, speed, and task execution. In regulated, probabilistic domains — finance, wealth management, capital markets — that framing is dangerous. The real challenge isn't prediction; it's making judgment explicit, contestable, and auditable while still leveraging what AI can do.

This book lays out a risk architecture for agentic AI in regulated environments, built around three pillars:

– Evidence — what the system believed at the time – Governance — what actions were permitted – Accountability — why a decision happened and who owned it

Drawn from 25+ years building systems in regulated, risk-sensitive environments, it gives compliance, risk, and AI leaders a working framework for deploying agents that hold up under regulatory scrutiny.