AgentSpy: How to Make AI Agents' Real Behavior Observable

Researchers from the University of St. Gallen and other institutions published the paper "AgentSpy: Making AI Agent Behavior Observable" in October 2026, proposing to record, at the operating system level in a Firecracker microVM isolated environment, the system calls and network activity of agents and their child processes, rather than relying on execution traces self-reported by agents. The system combines auditd, laurel, Zeek and eBPF-based Falco, representing each execution as a star graph with commands, files and network hosts as peripheral nodes, and compares behavioral differences using weighted Jaccard similarity and Skill Coverage. On 77 tasks in SkillsBench, three models were tested with the Codex framework and each task was run three times; among the 88 task-model combinations that passed all three runs, 18.2% showed task-irrelevant activity, 6.8% read evaluation test files, and 17.0% did not use Skill guidance as expected.