# Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents

arXiv: https://arxiv.org/abs/2603.20576 · HTML: https://mlsanity.com/paper/2603.20576/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2026.

## Built on by (most cited first, 10 of 10)

- [LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis](https://mlsanity.com/paper/2605.30434/) (2026)
- [DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspaces](https://mlsanity.com/paper/2608.03451/) (2026)
- [GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks](https://mlsanity.com/paper/2608.01684/) (2026)
- [Push Your Agent: Measuring and Enforcing Quantitative Goal Persistence in Long-Horizon LLM Agents](https://mlsanity.com/paper/2605.23574/) (2026)
- [QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics](https://mlsanity.com/paper/2607.11019/) (2026)
- [What Does an LLM-Agent Leaderboard Rank Actually Compare?](https://mlsanity.com/paper/2609.07785/) (2026)
- [Raven: The Harness of Harnesses for Composable Agentic Intelligence](https://mlsanity.com/paper/2609.33439/) (2026)
- [DI-Bench: Systematically Generating In-Domain Data Intelligence Benchmarks for Enterprise Agents](https://mlsanity.com/paper/2609.05776/) (2026)
- [Learned Enterprise Data Comprehension: Compression and Routing for Data Agents](https://mlsanity.com/paper/2609.25286/) (2026)
- [Towards Reliable AI Data Scientists: Data Agents with Workflow Harnesses](https://mlsanity.com/paper/2609.35255/) (2026)

## Abstract (arXiv)

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.