End-of-trajectory rewards do not identify the decision associated with a tool-agent failure. We treat step-level credit assignment as failure localization and test whether it supports selective repair. Silver labels come only from completed training trajectories. Prefix-only classifiers use task-grouped model selection and remain frozen during evaluation. On the retail domain of 𝜏²-bench, the selected classifier reaches 46.4% out-of-fold F1 and 61.1% F1 on 60 held-out trajectories comprising 333 scored decisions. The frozen gate selects 101 decisions, but the verifier changes actions in only seven trajectories. None changes binary reward relative to its task-matched baseline. Airline and telecom produce no changed trajectory. Mean reward differences are small, and every task-clustered confidence interval includes zero across the three domains. An exact replay audit reproduces 60 telecom baseline trajectories but triggers no intervention. The evidence identifies a localization–repair gap: detecting suspicious decisions does not yield repair without calibrated treatment coverage, grounded alternatives, and controlled attribution.