A New System of Trust: The Control Plane for Agentic Systems

AI agents are operating inside critical business workflows, making decisions, taking actions, and interacting with enterprise systems with increasing autonomy. But the infrastructure enterprises rely on was never designed for systems that reason and act on their own.
As a result, many organizations are deploying agents without clear oversight for how they behave, what decisions they make, or how policy and governance should be applied. Risk surfaces only after something goes wrong, and compliance teams lack the visibility required to confidently scale adoption.
The next phase of enterprise AI requires a new system of trust: a control plane for agentic systems.
This session makes the case that the control plane is the real constraint in enterprise AI. We'll show what closes that gap: inline guardrails that enforce policy on coding agents’ request and response paths and fleet-wide intelligence that ties agent behavior to cost by developer, team, and token. We will also cover how the control plane serves as a foundation for auditable governance and how Fiddler Centor Models maintain low evaluation costs and can help control AI budgets. You'll leave with a concrete framework for deciding how much autonomy your agents have earned and how to extend it without losing oversight.