AgentLoupe: Kernel-Resident Semantic Causal Labels for AI Agent Observability

Abstract

AI agents translate user intents into system actions via tool invocations. Accurately attributing these actions to their originating LLM interactions is essential for security auditing, yet existing methods, such as AgentSight, fail for delayed execution and cross-process-lineage behaviors. We present AgentLoupe, an application-transparent eBPF-based observability tool. The core insight of AgentLoupe is to embed causal identity into kernel resources.AgentLoupe employs eBPF to capture TLS plaintext to interpret LLM interactions, and assigns each a kernel-resident semantic label.It maintains labels in eBPF maps, then propagates this label across processes and threads, associates it with kernel resources such as files and sockets. When a persistent resource carries context to an independent process outside the original lineage, this design preserves the delayed causal association.Experiments show that the average observation overhead of AgentLoupe is below 8%, and case studies confirm AgentLoupe traces cross-lineage persistent attacks back to their originating LLM interactions.