Governance around AI agents is becoming a louder topic.
That’s not a reactionary trend. It’s a predictable one.
When agents move from generating text to taking action inside real systems, governance stops being theoretical.
Most organizations approach governance as a policy problem.
Write the document. Define the rules. Approve the tool.
That’s not enough.
Agent governance is a systems design problem.
Where are actions logged?
Who approves escalations?
What happens when an agent makes the wrong decision?
How is confidence measured?
How is authority bounded?
Governance cannot live outside the workflow.
It has to be embedded inside it.
The teams that treat governance as architecture, not paperwork, will move faster in the long run.
Because trust, once broken, is expensive to rebuild.