Offline evals increasingly guide changes to LLM-powered products, but showing that an eval agrees with human judgment does not tell a team which changes it can ship without an A/B test. That decision depends on whether eval treatment effects predict online treatment effects across a class of interventions. We call this requirement trial-level surrogacy and show that it is logically independent of unit-level (Prentice) surrogacy. In historical launch logs, sampling noise in both effect estimates attenuates the observed relationship. Under a bivariate normal working model, subtracting the reported within-intervention variances recovers the between-intervention covariance, and the corrected moments give a closed-form ship/test/kill rule that bounds the probability of shipping a change with a non-positive online effect. LLM judges add a separate distortion. When the judge is equally accurate on both arms, eval effects are only rescaled, but arm-dependent accuracy can reverse their sign. Once deployed, the eval-to-outcome relationship can no longer be observed in the ship and kill regions, so drift there becomes undetectable without randomized audit experiments. On 183 interventions from the Upworthy Research Archive, the pooled eval-to-outcome relationship is weak despite precise measurement on both sides, and the illustrative gate sends every intervention to an A/B test.

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