In today's data-driven era, fully automated end-to-end data analytics, particularly insight discovery, is critical for discovering actionable insights that assist organizations in making effective decisions. With the rapid advancement of large language models (LLMs), LLM-driven agents have emerged as a promising paradigm for automating data analysis and insight discovery. However, existing data insight agents remain limited in several key aspects, often failing to deliver satisfactory results due to: (1) insufficient utilization of domain knowledge, (2) shallow analytical depth, and (3) error-prone code generation during insight generation. To address these issues, we propose DataSage, a novel multi-agent framework that incorporates three innovative features including external knowledge retrieval to enrich the analytical context, a multi-role debating mechanism to simulate diverse analytical perspectives and deepen analytical depth, and multi-path reasoning to improve the accuracy of the generated code and insights. Extensive experiments on InsightBench demonstrate that DataSage consistently outperforms existing data insight agents across all difficulty levels, offering an effective solution for automated data insight discovery.

翻译：在当今数据驱动时代，全自动端到端数据分析（尤其是洞察发现）对于挖掘可指导组织有效决策的行动洞察至关重要。随着大语言模型（LLMs）的快速发展，基于LLM的智能体已成为自动化数据分析和洞察发现的重要范式。然而，现有数据洞察智能体仍存在若干关键局限，常因以下原因难以取得理想效果：（1）领域知识利用不足；（2）分析深度较浅；（3）洞察生成过程中易产生错误代码。为应对这些问题，本文提出DataSage——一种创新的多智能体框架，其具备三项核心特性：通过外部知识检索增强分析语境，采用多角色辩论机制模拟多元分析视角以深化分析深度，并引入多路径推理提升生成代码与洞察的准确性。在InsightBench基准上的大量实验表明，DataSage在所有难度级别上均持续优于现有数据洞察智能体，为自动化数据洞察发现提供了有效解决方案。