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Equation 1 · Part 2 · Agent Evaluation in 2035: Two Axes, Four Scenarios, and What Would Falsify Them

Symbol a

ϵ^(a)\hat\epsilon(a)
aa

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Whether automated LLM-judge evaluation becomes trusted for high-stakes decisions is not a third axis; it is mostly a readout of Axis A applied to one specific evaluator. A useful way to see the dependency is to write the automation decision as a threshold rule. Let ϵ^(a)\hat\epsilon(a) be the estimated error rate of a judge on a decision class a , and let ϵmax⁡(a)\epsilon_{\max}(a) be the maximum error a policy is willing to tolerate for that stakes class. A defensible automation rule is

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