newsRed Hat AITrust 88 · LabPublished 2d agoLive · yesterday
Beyond benchmarks: The 5 pillars of AI evaluation systems
As AI agents move from demos into production, teams are discovering that benchmark scores alone rarely translate into business value. Routinely, a new frontier model is released, climbs to the top of the benchmark leaderboards, and enters production, but business metrics and code velocity remain flat. We present 5 principles for evaluating agents beyond standard model benchmarks.A new software era requires a new notion of correctnessOur definition of what makes software "correct" must change as
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- PossiblePossibly related (embedding) · 62%zapier/AutomationBench →
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repozapier/AutomationBenchpaperFrontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoningpaperCan We Trust Item Response Theory for AI Evaluation?paperThe Human Creativity BenchmarkpaperAre Performance-Optimization Benchmarks Reliably Measuring Coding Agents?
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paperAre Performance-Optimization Benchmarks Reliably Measuring Coding Agents?paperThe Human Creativity BenchmarkpaperCan We Trust Item Response Theory for AI Evaluation?paperFrontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoningrepozapier/AutomationBench
