# AgenticDataBench: A Comprehensive Benchmark for Data Agents arXiv: https://arxiv.org/abs/2607.01647 ยท HTML: https://mlsanity.com/paper/2607.01647/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. - Builds on 23 of its 54 references that are in the map. - First posted on arXiv 2 Jul 2026. ## Placed in - LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/) ## Builds on (most cited first, 23 of 23) - [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018) - [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023) - [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024) - [The Quantization Model of Neural Scaling](https://mlsanity.com/paper/2303.13506/) (2023) - [DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?](https://mlsanity.com/paper/2409.07703/) (2024) - [Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models](https://mlsanity.com/paper/2307.14430/) (2023) - [A Theory for Emergence of Complex Skills in Language Models](https://mlsanity.com/paper/2307.15936/) (2023) - [Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models](https://mlsanity.com/paper/2310.17567/) (2023) - [DataSciBench: An LLM Agent Benchmark for Data Science](https://mlsanity.com/paper/2502.13897/) (2025) - [KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes](https://mlsanity.com/paper/2506.06541/) (2025) - [Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First](https://mlsanity.com/paper/2509.00997/) (2025) - [Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning](https://mlsanity.com/paper/2408.14774/) (2024) - [Reward-SQL: Boosting Text-to-SQL via Stepwise Execution-Aware Reasoning and Process-Supervised Rewards](https://mlsanity.com/paper/2505.04671/) (2025) - [AgenticData: An Agentic Data Analytics System for Heterogeneous Data](https://mlsanity.com/paper/2508.05002/) (2025) - [R-Bot: An LLM-based Query Rewrite System](https://mlsanity.com/paper/2412.01661/) (2024) - [Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems](https://mlsanity.com/paper/2507.01599/) (2025) - [Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs](https://mlsanity.com/paper/2601.17058/) (2026) - [Task Cascades for Efficient Unstructured Data Processing](https://mlsanity.com/paper/2601.05536/) (2026) - [AgenticScholar: Agentic Data Management with Pipeline Orchestration for Scholarly Corpora](https://mlsanity.com/paper/2603.13774/) (2026) - [PrepBench: How Far Are We from Natural-Language-Driven Data Preparation?](https://mlsanity.com/paper/2605.08687/) (2026) - [Automating Database-Native Function Code Synthesis with LLMs](https://mlsanity.com/paper/2604.06231/) (2026) - [Data Agents Under Attack: Vulnerabilities in LLM-Driven Analytical Systems](https://mlsanity.com/paper/2606.08661/) (2026) - [ST-Raptor: An Agentic System for Semi-Structured Table QA](https://mlsanity.com/paper/2602.07034/) (2026) ## Built on by (most cited first, 7 of 7) - [DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspaces](https://mlsanity.com/paper/2608.03451/) (2026) - [Agentic Transaction: Towards ACID-Compliant Agent Systems](https://mlsanity.com/paper/2608.13900/) (2026) - [QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics](https://mlsanity.com/paper/2607.11019/) (2026) - [Agents in the Large: Perception-Centered Architecture for Persistent Agents](https://mlsanity.com/paper/2608.30478/) (2026) - [Data Agents: Agentic Data Systems](https://mlsanity.com/paper/2609.24137/) (2026) - [ArrivalBench: Agent-Generated Data Pipelines Are Correct Once and Wrong Under Time](https://mlsanity.com/paper/2610.02363/) (2026) - [The Silent Failures in Agentic Data Science](https://mlsanity.com/paper/2610.14115/) (2026) ## Abstract (arXiv) Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society. Automating this process is essential to reducing labor-intensive efforts for data scientists and enabling scalable data-driven applications. Recently, large language model (LLM)-based data agents have emerged as a promising solution to automate data science workflows. However, the field lacks comprehensive benchmarks to rigorously evaluate these agents across diverse scenarios with fine-grained granularity. To address this gap, we propose AgenticDataBench, a comprehensive benchmark featuring realistic tasks spanning diverse domains with fine-grained ground-truth labels. This enables evaluations to capture the diversity and complexity of data science workflows and the detailed performance of agents. First, to cover diverse domains, we collect real datasets and tasks from 15 vertical domains, including 5 real-world B2B use cases from a leading fintech company. Second, to remove redundancy in real-world tasks and generate high-quality tasks for domains lacking real data, we introduce data science skills, recurring data-centric operational patterns, and quantify benchmark coverage by the number of skills included. Representative skills are extracted from large-scale task solutions on Stack Overflow using skill-aligned hierarchical clustering. Third, for real-world business tasks, we select task-solution pairs that maximize diversity in skill composition, ensuring broad coverage of practical scenarios. Fourth, to generate realistic tasks for devise domains without real tasks, we propose a systematic LLM-based task generation approach to create workflows and tasks based on these skills. Finally, we evaluate state-of-the-art data agents using our annotated benchmark and open-sourced testbed, providing detailed skill-level insights.