Users across enterprises increasingly rely on AI agents to query their data through natural language. However, building reliable data agents remains difficult because real-world data is often fragmented across multiple heterogeneous database systems, with inconsistent references and information buried in unstructured text. Existing benchmarks only tackle individual pieces of this problem -- e.g., translating natural-language questions into SQL queries, answering questions over small tables provided in context -- but do not evaluate the full pipeline of integrating, transforming, and analyzing data across multiple database systems. To fill this gap, we present the Data Agent Benchmark (DAB), grounded in a formative study of enterprise data agent workloads across six industries. DAB comprises 54 queries across 12 datasets, 9 domains, and 4 database management systems. On DAB, the best frontier model (Gemini-3-Pro) achieves only 38% pass@1 accuracy. We benchmark five frontier LLMs, analyze their failure modes, and distill takeaways for future data agent development. Our benchmark and experiment code are published at github.com/ucbepic/DataAgentBench.
@article{arxiv.2603.20576,
title = {Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents},
author = {Ruiying Ma and Shreya Shankar and Ruiqi Chen and Yiming Lin and Sepanta Zeighami and Rajoshi Ghosh and Abhinav Gupta and Anushrut Gupta and Tanmai Gopal and Aditya G. Parameswaran},
journal= {arXiv preprint arXiv:2603.20576},
year = {2026}
}