When agents act on their own, governance has to live in the data layer

The useful part

Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it? These are your agents, running on your models, touching your data in your infrastructure — and the responsibility for what they do sits with you.

How it works

• Controls at the agent layer are only as reliable as the agent's output is predictable, and autonomy is precisely the property that makes that output hard to predict.
• Governance has to become executable, and enforced where agents actually do their work: at the operational data layer, in the context, and exactly at the moment it is happening.
• Followed literally, an agent could never get in or out of the car at all.
• That responsibility can't be met in hindsight or with a set of abstract policies that live on paper but not in practice.

What to take from it

Agents need rules in the context of the moment, because they don't exercise overriding judgment of their own actions. If you change the context (the car has just crashed, there's a fire, someone is hurt and needs to get out), then the rule you actually want is the opposite. The instinct is to add guardrails around the agent: instructions, policies, and monitoring layered above the model.

Example or evidence

• Governance that depends on reviewing an action before it happens cannot keep pace with a system that acts in milliseconds, across many systems at once.