Hidden environmental variables that skew AI agent evaluation results

AI agent evaluations often suffer from reproducibility issues due to hidden environmental variables that silently influence model behavior. While administrators may control the model and prompt, factors like the underlying operating system, shell environment, and absolute file paths can steer an agent's logic. For instance, agents often perform better in bash-based Unix environments than in PowerShell because their training data is more heavily weighted toward Linux-style interactions. Source

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