Model governance and AI agent governance overlap, but they are not the same. Model governance focuses on the model: development, validation, performance, bias, documentation, approval, monitoring, and retirement. Agent governance focuses on the full system that uses a model to retrieve information, reason through steps, call tools, and take action.
This distinction matters because many risks in agents do not come from the model alone.
What model governance covers
Traditional model governance is strong for systems where the model itself is the primary decision engine. It asks whether the model was developed appropriately, validated, monitored, documented, and approved. It often includes performance metrics, data lineage, bias testing, explainability, change control, and model owner accountability.
Those practices remain useful. Agents still rely on models, and model quality can affect safety and compliance.
What agent governance adds
Agent governance asks additional questions. What tools can the agent call? What data can it retrieve? Can it send messages, update records, run code, or trigger transactions? What instructions control it? Can untrusted content influence its behavior? Where does human approval happen? What happens when the agent is uncertain?
A model can be acceptable while the agent around it is risky. For example, a general-purpose language model may become high risk when connected to customer records and account actions.
Runtime behavior matters
Model governance often focuses on development and validation stages. Agent governance has to stay close to runtime behavior because tool use and context can vary by request. Logs, blocked actions, approval decisions, prompt injection attempts, and incidents become governance evidence.
The governance record should show what the agent was allowed to do and what it actually did.
Use both views
Organizations should not abandon model governance. They should extend it. The model record should connect to the agent or system record. The agent record should include model information, but also autonomy, data, tools, controls, monitoring, and evidence.
A simple test helps: if the risk would still exist after swapping the model provider, it is probably an agent governance issue, not only a model governance issue.