A Different Perspective on Agentic AI Governance

Governing AI Agents with Proven Quality Frameworks

Regulated industries already know how to measure, monitor, and improve process quality. Lean Six Sigma, DMAIC, and mistake-proofing techniques have governed critical operations in banking and financial services for decades. These are not new ideas. They are proven disciplines with executive buy-in, board-level credibility, and regulatory acceptance.

AI agents introduce a new class of operational risk, but the governance challenge is fundamentally a quality problem: data quality flowing into agent decisions, process quality of agent execution, and the compounding effect when agents work in sequence. Today, all three fall well below the standards financial institutions expect for mission-critical processes.

We believe the right response is not to invent new governance frameworks from scratch. It is to apply the quality disciplines that already work, extend them to data and agentic AI, and give risk and operations leaders a measurement language they already understand.

Data Governance Is Table Stakes for AI

An AI system is only as trustworthy as the data it learns from, reasons over, and acts upon. Regulators across every major jurisdiction explicitly require that institutions demonstrate not just that their AI models are well-governed, but that the data feeding those models is accurate, complete, timely, representative, and properly stewarded.

When data quality failures affected a quarterly risk report, the consequences were serious but contained. When data quality failures affect an AI system making real-time lending decisions or fraud determinations at scale, the consequences multiply by orders of magnitude. You cannot satisfy an AI governance requirement without first satisfying the data governance requirement beneath it.