Large language model (LLM) data agents answer structured-data questions by generating SQL, invoking tools, executing queries, and summarizing results. In operational settings, reliability depends on both reported answer and computation used to produce it, as errors may remain undetected when the resulting answer matches an expected value. We refer to the mismatch between natural-language intent and operator-level computation required to satisfy that intent as the Structure Gap, and define Trace Integrity as a reliability criterion for assessing whether the computation supporting an answer is explicit, executable, schema-valid, operator-faithful, replayable, answer-consistent, and auditable. We operationalize Trace Integrity through execution contracts that record the schema elements, operator plan, assumptions, executable query, verification status, and final-answer linkage associated with a request. We also define the Correct Answer / Invalid Trace (CAIT) Rate, which measures the proportion of correct answers whose recorded computations fail trace-integrity checks. These provide a way to evaluate whether answer-level success is supported by a valid and inspectable computation. We study this on 100 BIRD Mini-Dev text-to-SQL examples using three prompting conditions: Direct SQL, Operation Summary + SQL, and Contract-First SQL. The three conditions achieve answer accuracies 20.0%, 22.0%, and 24.0%, Trace Integrity Pass Rates 39.0%, 43.0%, and 40.0%, and CAIT Rates 55.0%, 59.1%, and 45.8%, respectively. The results show that answer accuracy, trace validity, and the frequency of correct answers supported by invalid traces capture different aspects of system behavior. Trace-level evaluation therefore complements conventional answer-based evaluation by providing evidence about the computational support, inspectability, and reproducibility of LLM data-agent outputs in real-world systems.

翻译：暂无翻译