发表机构
The Hong Kong Polytechnic University; Jilin University（香港理工大学; 吉林大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文从工作流约束视角系统研究数据智能体，提出五阶段分类体系、15种技术路线，并识别四个开放可靠性问题，强调严格约束与共享资源对可靠自动化数据科学的关键作用。
AI 中文摘要
大型语言模型智能体正越来越多地部署于数据密集型工作中，然而可靠的数据分析需要的不仅仅是通用推理和临时工具增强。配备工作流约束的数据智能体，为自动化端到端数据科学生命周期提供了一种有前景的范式。本文从约束中心视角审视数据智能体。首先，我们引入数据智能体及相关数据环境的分类体系，围绕五个功能阶段组织文献：感知、规划、执行、验证和修复。其次，我们分析每个阶段内的关键技术路线，识别出从数据结构探测到数据状态重建的15种不同方法。第三，我们指出四个开放可靠性问题：非活跃语义校准、缺失澄清、缺失经验迁移，以及缺失验证-修复仓库。这些问题解释了为何即使各个组件功能正常，静默故障仍可能持续存在，凸显了对严格工作流约束和共享可靠性资源的需求。最后，我们总结数据智能体的横向任务族，考察其纵向应用场景及评估基准，并在本HTTPS URL维护配套仓库。
英文摘要
Large language model agents are increasingly deployed for data-intensive work, yet reliable data analysis requires more than general-purpose reasoning and ad hoc tool augmentation. Data Agents, equipped with workflow harnesses, offer a promising paradigm for automating the end-to-end data science lifecycle. This paper examines Data Agents from a harness-centric perspective. First, we introduce a taxonomy of Data Agents and associated data environments, organizing the literature around five functional stages: perception, planning, execution, verification, and repair. Second, we analyze the key technical routes within each stage, identifying 15 distinct approaches ranging from data structure probing to data state reconstruction. Third, we identify four open reliability problems: inactive semantic calibration, missing clarification, missing experience transfer, and the missing verification-repair repository. These problems explain why silent failures can persist even when individual components function correctly, highlighting the need for rigorous workflow harnesses and shared reliability resources. Finally, we summarize the horizontal task families of Data Agents, examine their vertical application settings, and benchmarks for evaluation, while maintaining a companion repository at https://github.com/DEEP-PolyU/Awesome-Data-Agents.
