Data Agents: Agentic Data Systems

2026-09-21

AI Summary

本文提出数据代理新范式，通过自主执行数据任务解决传统数据系统在AI时代面临的限制，包括语义理解、自主编排和主动处理等方法。

Abstract

Traditional data systems face profound limitations in the AI era, relying on human-crafted pipelines, lacking semantic understanding of heterogeneous data, and operating through rigid, reactive processing. To address these challenges, we propose a new paradigm called the Data Agent, designed to manage, process, and analyze data with minimal human intervention. Data agents autonomously execute a wide range of data-related tasks, transforming traditional data systems by shifting from manual design to autonomous orchestration, from literal manipulation to semantic interpretation, and from reactive to proactive processing. Our Data Agent system includes six components: semantic data organization, semantic operators, agentic pipeline orchestration and optimization, feedback-driven refinement, memory management, and proactive adaptation. Building on this foundation, we also develop two specialized agents: the data analytics agent and the data science agent. Experiments on real benchmarks demonstrate significant performance gains of our data agent over state-of-the-art methods. We identify open challenges to guide future research in building fully autonomous data systems.

Problem

• data systems
• semantic understanding
• heterogeneous data
• autonomous orchestration
• proactive processing

Innovation

• Data Agent
• semantic interpretation
• autonomous orchestration
• proactive processing
• feedback-driven refinement