AI Agents Don't Read Your Policy Docs. They Hit Your APIs — Gravitee
The talk argues that AI agent governance and security cannot be reliably enforced through application-level policies (like prompts, markdown files, or internal agent logic). Instead, governance must be shifted to the infrastructure layer, specifically a dedicated Gateway. By implementing controls at the gateway, organizations can prevent destructive actions, block data exfiltration (PII leaks), manage costs via rate limiting and semantic caching, and enforce context limits before requests ever reach the underlying Large Language Model (LLM).
Key takeaways
-
Governance must move to the Gateway
2:02
Relying on internal agent policies is insufficient for security. The gateway provides a centralized, scalable, and hard-controlled layer that enforces rules regardless of the agent's internal instructions.
-
Blocking destructive actions and data exfiltration
4:57
The gateway can intercept and block unauthorized tool calls (e.g., 'delete all bookings') and prevent the leakage of sensitive PII, even if the agent is prompted to do so.
-
Cost and resource control
10:56
The gateway allows for critical cost controls, including rate limiting (preventing runaway requests) and semantic caching (serving repeated prompts from a cache instead of calling the LLM repeatedly).
-
Handling oversized context
The gateway can block prompts that exceed defined token limits or context size, preventing unnecessary downstream LLM calls and controlling costs.