Building Governance-Based Agents: A Framework for Cost, Control, and Compliance

As AI agents move beyond simple prototypes to become production infrastructure for enterprises, the need for a Runtime Control Plane capable of enforcing policy across model calls, tool use, and agent-to-agent transitions is growing.

There are three main challenges organizations face when adopting agents. First, difficulty predicting AI costs due to increased token consumption; second, uptime requirements for business continuity; and third, consistent policy enforcement to comply with regulations such as the EU AI Act.

The LLM Gateway is a core element for making these decisions, providing the following capabilities:

• User authentication and selection of approved models
• Minimizing exposed context and enforcing data/spending policies
• Failure management and preservation of decision history (Evidence)

For effective governance, enterprises should approach this from three perspectives—Visibility, Control, and Assurance—and ultimately build an operating model that manages all of these elements in an integrated way.