DataSage: Multi-agent Collaboration for Insight Discovery with External Knowledge Retrieval, Multi-role Debating, and Multi-path Reasoning

In today's data-driven era, fully automated endto-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 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.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 In-sightBench demonstrate that DataSage consistently outperforms existing data insight agents across all difficulty levels, improving by 7.5% and 13.9% respectively in the insight-level and summary-level metrics.It offers an effective solution for automated data insight discovery.