The Agent Said It Worked. I Asked the Kernel.

In a recent experiment, a developer explored the gap between AI-generated code claims and actual system behavior. By building a native backup client with eight deliberately seeded behaviors, the author demonstrates that relying solely on an AI's assertion of success is insufficient. Using observability tools like eBPF, strace, and CPU sampling, the project verifies software claims against independent evidence from the Linux kernel and network activity. The experiment highlights that while AI can produce functional code, it may also introduce hidden inefficiencies or unexpected behaviors that standard tests might overlook. The author argues that as AI-generated code becomes more prevalent, engineers must adopt more rigorous, independent verification methods—such as inspecting system calls and packet captures—to ensure that software not only reports success but actually performs the intended tasks correctly and efficiently without unnecessary side effects.