DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle
This work addresses the need for rigorous benchmarking of autonomous data agents in enterprise settings, though it is incremental as it builds on existing evaluation methods by introducing a more comprehensive testbed.
The authors tackled the problem of evaluating autonomous data agents in enterprise data intelligence workflows by introducing DAComp, a benchmark of 210 tasks that mirror real-world data engineering and analysis processes. The results showed that state-of-the-art agents performed poorly, with success rates under 20% on data engineering tasks and below 40% on data analysis tasks, exposing critical bottlenecks in pipeline orchestration and open-ended reasoning.
Real-world enterprise data intelligence workflows encompass data engineering that turns raw sources into analytical-ready tables and data analysis that convert those tables into decision-oriented insights. We introduce DAComp, a benchmark of 210 tasks that mirrors these complex workflows. Data engineering (DE) tasks require repository-level engineering on industrial schemas, including designing and building multi-stage SQL pipelines from scratch and evolving existing systems under evolving requirements. Data analysis (DA) tasks pose open-ended business problems that demand strategic planning, exploratory analysis through iterative coding, interpretation of intermediate results, and the synthesis of actionable recommendations. Engineering tasks are scored through execution-based, multi-metric evaluation. Open-ended tasks are assessed by a reliable, experimentally validated LLM-judge, which is guided by hierarchical, meticulously crafted rubrics. Our experiments reveal that even state-of-the-art agents falter on DAComp. Performance on DE tasks is particularly low, with success rates under 20%, exposing a critical bottleneck in holistic pipeline orchestration, not merely code generation. Scores on DA tasks also average below 40%, highlighting profound deficiencies in open-ended reasoning and demonstrating that engineering and analysis are distinct capabilities. By clearly diagnosing these limitations, DAComp provides a rigorous and realistic testbed to drive the development of truly capable autonomous data agents for enterprise settings. Our data and code are available at https://da-comp.github.io