发表机构
Tsinghua University; Shanghai Jiao Tong University; The Hong Kong University of Science and Technology (Guangzhou); Renmin University of China（清华大学; 上海交通大学; 香港科技大学（广州）; 中国人民大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传统数据系统在AI时代的局限，提出数据智能体范式，通过六个组件实现自主编排与语义处理，并在真实基准上超越现有方法，为构建全自主数据系统指明方向。
AI 中文摘要
传统数据系统在人工智能时代面临深刻的局限性，它们依赖人工构建的流水线，缺乏对异构数据的语义理解，并通过僵化、反应式的处理方式运行。为应对这些挑战，我们提出了一种名为“数据智能体”（Data Agent）的新范式，旨在以最少的人工干预来管理、处理和分析数据。数据智能体自主执行广泛的数据相关任务，通过从手动设计转向自主编排、从字面操作转向语义解释、从反应式处理转向主动式处理，从而变革传统数据系统。我们的数据智能体系统包含六个组件：语义数据组织、语义算子、智能体化流水线编排与优化、反馈驱动的改进、记忆管理以及主动适应。在此基础之上，我们还开发了两个专用智能体：数据分析智能体和数据科学智能体。在真实基准上的实验表明，我们的数据智能体相较于最先进的方法取得了显著的性能提升。我们指出了开放挑战，以指导未来构建完全自主数据系统的研究。
英文摘要
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.
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