Developer Builds Independent Kernel-Level Tests to Verify AI Agent Code Claims

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A developer, inspired by researcher Hemapriya Kanagala's question about AI benchmark reliability, built a small native backup client with eight distinct behaviors to independently verify whether an AI agent's code actually does what it claims. Rather than trusting the agent's own logs or test results, the experiment observes program behavior from outside — using packet captures, kernel-level activity, and file comparisons. The setup uses a C client, a Python receiver over loopback TCP, and SHA-256 digest checks to confirm that saved files genuinely match their source. The author notes that when an agent produces both code and its tests, shared misunderstandings between the two can make passing tests misleading. The experiment argues that execution traces, network activity, and resulting file state together provide independent evidence to judge a program's behavior against its actual requirements.

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