"A new kind of traffic is quietly exploding: autonomous AI agents calling APIs and services on their own. Large language model ( LLM) "agents" can plan tasks, chain tool usage, fetch data, and even spin up subtasks - all via outbound requests that traditional infrastructure isn't watching. This agent-driven outbound traffic (let's call it agentic traffic) is the missing layer in today's AI infrastructure."
"Software architects and engineering leaders building AI-native platforms are starting to notice familiar warning signs: sudden cost spikes on AI API bills, bots with overbroad permissions tapping into sensitive data, and a disconcerting lack of visibility or control over what these AI agents are doing. It's a scenario reminiscent of the early days of microservices - before we had gateways and meshes to restore order - only now the "microservices" are semi-autonomous AI routines."
A new kind of traffic is emerging: autonomous AI agents making outbound API calls and service requests without traditional infrastructure oversight. These agent-driven outbound requests create visibility gaps, cost spikes, and risks from overbroad permissions accessing sensitive data. An AI Gateway acts as middleware through which all agent requests are channeled, serving as the control point for enforcing policies, routing, cost management, observability, and auditing. A reference design includes components such as a Traffic Interceptor, Policy Engine, Routing & Cost Manager, and Observability & Auditing Layer. Robust gateway design and governance become fundamental infrastructure to enable scalable, safe AI-native systems.
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