Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents
This addresses the problem of fragmented data querying for enterprise users, but it is incremental as it builds on existing benchmarks by extending evaluation to a more comprehensive pipeline.
The paper tackles the challenge of building reliable AI data agents for querying fragmented enterprise data across multiple heterogeneous database systems by introducing the Data Agent Benchmark (DAB), which evaluates the full pipeline of integrating, transforming, and analyzing data, and finds that the best frontier model achieves only 38% pass@1 accuracy.
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.