In this talk, Sam, CTO at Gravitee, emphasizes the critical importance of shifting AI agent governance from policy documents and embedded prompts to infrastructure-level controls, specifically through API gateways. He highlights that while many organizations deploy AI agents actively, only a small fraction have proper governance in place, leading to significant risks such as data leaks, regulatory fines, and security breaches. Instead of relying on agents to self-regulate based on internal policies or prompt engineering, Sam advocates for enforcing strict identity and permission controls at the gateway level to prevent unauthorized or harmful actions.
Sam demonstrates this approach through live examples comparing two AI agents: one governed by internal prompt controls and the other managed via Gravitee’s gateway with tightly scoped identities and permissions. In scenarios like prompt injection attacks or unauthorized data access requests, the agent relying solely on internal controls executes harmful commands, whereas the gateway-controlled agent blocks unauthorized actions with clear error responses. This approach ensures that sensitive operations, such as deleting bookings or exposing personal data, are only performed when explicitly permitted, thereby enhancing security and compliance.
Cost management is another key benefit of using a gateway for AI agent governance. Sam shows how uncontrolled agents can generate excessive API calls and token usage, leading to unnecessary expenses. The gateway can enforce rate limiting, token usage caps, and semantic caching to reduce redundant calls and optimize resource consumption. For example, repeated queries can be served from cache rather than invoking costly downstream language models repeatedly, saving both latency and money while improving user experience.
The gateway also enables control over input size and content relevance, preventing agents from processing overly large or irrelevant prompts that could drive up costs or degrade performance. By blocking excessively large inputs or off-topic queries at the gateway, organizations can avoid unnecessary downstream processing and maintain tighter control over AI usage. This is particularly important as AI agents proliferate rapidly, often creating other agents and scaling exponentially, making decentralized governance impractical.
Finally, Sam outlines additional capabilities of Gravitee’s platform, such as flexible routing to different language models, fine-grained tool exposure to limit blast radius and costs, and edge management to monitor and secure AI usage outside the gateway. He stresses that as AI adoption grows and regulatory scrutiny increases, embedding governance at the infrastructure layer is essential for scalable, secure, and auditable AI deployments. The talk concludes with an invitation to learn more about these solutions at their booth, reinforcing the message that effective AI governance requires moving beyond policy documents to robust, infrastructure-driven controls.
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