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Developer Rebuilds AI Agent Certification After Finding Results Were Non-Deterministic

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A developer building HivePlane, an AI agent control plane, ran a field test expecting three pre-built agents to register and certify successfully, but all three failed on day one due to missing dependencies and incompatible environments. The failures revealed a broader portability problem: agents that work within their home repository are not guaranteed to run elsewhere. Further testing exposed a critical flaw where LLM-driven outputs caused certification results to vary across runs, rendering them statistically meaningless. To isolate the control plane's performance from model unpredictability, the developer replaced the original agents with deterministic shims backed by mock tools and fixed data seeds. The redesigned tests ultimately produced consistent, repeatable results — with both test agents passing certification across multiple consecutive runs with signed attestations.

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