Ara Khan argues that benchmark scores and subjective impressions both have limitations. He recommends treating reported scores as approximations and seeking current evaluations that reflect the work a team cares about.
Ara Khan describes building evaluations from consented coding workflowsEvaluation is the systematic assessment of a system's behaviour or results against explicit criteria. and running multistep tasks in isolated environmentsAn AI agent sandbox is an isolated execution environment that limits which files, processes, networks, credentials, and external systems an agent can access.. He explains how parallel runs and failure traces can reveal weaknesses in a model, agent harness or infrastructureAn AI agent harness is the software framework that packages a model with tools, instructions, context management, execution controls, and user interaction..
Ara Khan distinguishes useful engineering improvements from overfitting to a test and argues that benchmark results should be considered alongside actual product experience.
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