From First to Last Without Changing a Prediction: Observability Is Not Specification in Agent Diagnosis

Agent diagnoses increasingly guide repairs and incident investigations, yet a complete trajectory can leave open what a correct diagnosis should identify. Should it name the final answer to correct or the upstream source to inspect? This choice can reverse a system comparison: on 38 shared cases, the leading method becomes last among four methods when the diagnostic question changes, without changing a single prediction. We make the rules that define acceptable diagnoses a controlled experimental variable: change the rules with the model fixed, then change the executor with the rules fixed. On 720 frozen trajectories, supplying explicit rules raises fully correct diagnoses from 233 to 716. The repairs concern the meaning of an identified event as well as its location and whether a location is justified at all. Targeted role swaps produce the predicted changes among cases originally answered correctly under the rules. On 217 external trajectories, explicit rules improve acceptable answers and reduce unsupported claims of uniqueness; a model and a program agree on all three main scoring outcomes for 213 cases. These tests make diagnostic-object identification operational: they check which rules govern which answers, and whether their execution is reproducible. Before comparing diagnostic systems, specify and test what counts as a correct diagnosis.