How to Choose an AI Governance Platform

The text outlines a comprehensive framework for evaluating enterprise-grade AI safety and governance tools, emphasizing the importance of choosing a suitable governance architecture capable of supporting production agents across various frameworks and business units. It highlights the challenges associated with hardcoded safety rules that require full redeployment for updates and the inability of observe-only platforms to adequately manage production agents. The guide proposes an eight-criterion evaluation framework to help organizations make informed procurement decisions, addressing aspects such as deployment flexibility, evaluation model architecture, runtime intervention, centralized policy management, compliance logging, agent-native architecture, framework-agnostic integration, and self-service metric customization. These criteria are crucial for ensuring robust AI governance, as they encompass visibility into decision paths, measurement of behavior, and runtime controls necessary to preemptively address failures before they impact customers. The text also discusses the evolving regulatory landscape, including the EU AI Act and CPPA rules, and stresses the need for audit consistency and unified policy propagation in environments with multiple frameworks and business units.