Building an AI agent or LLM integration doesn't stop at the demo: once live, it consumes bandwidth, calls dependencies, and moves data across the network like any other service, and its engineers are often the last to know something's wrong. Application performance tools see the code, not what's happening on the wire when a new agent overloads a dependency, leaks unexpected data, or degrades a shared backbone.

The same network-level visibility that flags shadow AI for security also gives builders their own early warning: real dependency maps, real latency numbers, and real traffic patterns for the service they shipped, before a security review or an outage forces the conversation.