As financial institutions transition from predictive AI models to Autonomous AI Agents—systems that can independently execute trades, manage portfolios, and conduct KYC (Know Your Customer) verifications—the regulatory stakes have never been higher. The fundamental challenge lies in the “autonomy gap”: the space between a high-level human instruction and a multi-step, non-linear execution by an AI.

To maintain trust and compliance, firms must move beyond traditional model risk management. This article proposes a robust governance framework built on the principle of “Traceable Reasoning,” ensuring that every autonomous action is backed by an auditable chain of thought, deterministic guardrails, and clear lines of institutional accountability.

1. The “Black Box” Problem in Agentic Workflows

Traditional financial models are generally static; for a given input, they produce a predictable output. Agentic AI, however, is dynamic. An agent tasked with “optimizing a hedge ratio” might choose to query a real-time news API, analyze … Read More