Constructing Extract-Load-Transform (ELT) pipelines is a labor-intensive data engineering task and a high-impact target for AI automation. On ELT-Bench, the first benchmark for end-to-end ELT pipeline construction, AI agents initially showed low success rates, suggesting they lacked practical utility. We revisit these results and identify two factors causing a substantial underestimation of agent capabilities. First, re-evaluating ELT-Bench with upgraded large language models reveals that the extraction and loading stage is largely solved, while transformation performance improves significantly. Second, we develop an Auditor-Corrector methodology that combines scalable LLM-driven root-cause analysis with rigorous human validation (inter-annotator agreement Fleiss' kappa = 0.85) to audit benchmark quality. Applying this to ELT-Bench uncovers that most failed transformation tasks contain benchmark-attributable errors -- including rigid evaluation scripts, ambiguous specifications, and incorrect ground truth -- that penalize correct agent outputs. Based on these findings, we construct ELT-Bench-Verified, a revised benchmark with refined evaluation logic and corrected ground truth. Re-evaluating on this version yields significant improvement attributable entirely to benchmark correction. Our results show that both rapid model improvement and benchmark quality issues contributed to underestimating agent capabilities. More broadly, our findings echo observations of pervasive annotation errors in text-to-SQL benchmarks, suggesting quality issues are systemic in data engineering evaluation. Systematic quality auditing should be standard practice for complex agentic tasks. We release ELT-Bench-Verified to provide a more reliable foundation for progress in AI-driven data engineering automation.

翻译：构建提取-加载-转换（ELT）流水线是一项劳动密集型的数据工程任务，也是AI自动化的高影响力目标。在首个面向端到端ELT流水线构建的基准测试ELT-Bench上，AI智能体最初成功率较低，暗示其缺乏实际应用价值。我们重新审视这些结果，并发现导致智能体能力被严重低估的两个因素。首先，使用升级后的大语言模型重新评估ELT-Bench发现，提取与加载阶段已基本解决，而转换性能显著提升。其次，我们开发了一种审计-校正方法，将可扩展的LLM驱动的根本原因分析与严格的人工验证（注释者间一致性Fleiss' kappa=0.85）相结合，以审计基准质量。将该方法应用于ELT-Bench后发现，大多数失败的转换任务包含基准本身导致的错误——包括僵化的评估脚本、模糊的规范说明和错误的标准答案——这些错误惩罚了正确的智能体输出。基于这些发现，我们构建了ELT-Bench-Verified，一个具有改进评估逻辑和修正后标准答案的修订基准。在该版本上重新评估取得了完全归因于基准校正的显著改进。我们的结果表明，模型快速进步和基准质量缺陷共同导致了智能体能力被低估。更广泛而言，我们的发现呼应了文本转SQL基准中普遍存在的标注错误现象，表明质量缺陷在数据工程评估中具有系统性。对于复杂的智能体任务，系统性质量审计应成为标准实践。我们发布ELT-Bench-Verified，为AI驱动的数据工程自动化进展提供更可靠的基础。