Hard Issue, Weak Model, or Bad Run? Rethinking Failure Prediction for Coding Agents
Trajectory-based failure predictors for software engineering (SE) agents read the partial trajectory of a run to flag whether it is going right or wrong. If it is going wrong, the run can be stopped, retried, or switched to another model. We ask whether these predictors learn anything about the run itself. On public SWE-bench Verified trajectories, where each model attempts each issue once, two run-independent factors dominate predictor performance. The first is issue difficulty: a reference score that never looks at the run, only at how hard the issue is for other models, reaches 0.92 AUROC, whereas trajectory predictors reach about 0.62. The second is model ability: within an issue, knowing which model ran predicts failure better than our predictors do, and the predictors largely reproduce the overall ability ranking of the models. Beyond difficulty and ability, what predictors gain from the trajectory is statistically equivalent to zero. This confusion carries over to the actions taken after a prediction. Model switching is often motivated by the gap between the best single model's resolution rate and an any-model oracle. This gap is no larger than random variation alone produces. Per-issue switching gains nothing on held-out runs. In a repeated-run pilot, the same model often solves an issue in some runs and fails it in others. Retrying thus has real room to repair failures, but it rescues only failures caused by a bad run, not those on an issue beyond the model's ability. Current predictors cannot tell the causes apart, as they mostly predict the issue and the model, not the run. We call for failure prediction that explains why a run fails, evaluated on repeated-run benchmarks and in closed-loop, equal-budget settings.