Governance · · 7 min read
AI Agent Governance vs AI Model Governance
Model governance is not enough for autonomous agents. Learn why agent-native governance requires behavioral testing and runtime evidence.
Models Predict. Agents Act.
AI model governance focuses on model risks, documentation, evaluation, and lifecycle controls. AI agent governance must additionally account for tools, permissions, workflows, memory, API calls, and autonomous decisions.
Agent-Native Governance Requires Behavior Evidence.
Questionnaires and policy documents can show intent. They cannot prove whether an agent follows approved boundaries when it uses tools or faces adversarial input.
The Missing Layer Is Behavior Assurance.
AI Agent Certify combines policy-to-test automation, certification scorecards, runtime monitoring, and revocable trust credentials to close the gap between governance intent and production behavior.
See the AI Agent Certify platform.