Towards Autonomous Business Intelligence via Data-to-Insight Discovery Agent
Abstract
Transforming fragmented enterprise data into actionable insights remains a significant challenge for LLMs, constrained by complex database schemas, limitations in dynamic SQL generation, and the need for deep multi-dimensional analysis.In this paper, we propose AIDA(Autonomous Insight Discovery Agent), the first end-to-end framework designed for autonomous exploration in complex business environments. We establish a highly flexible instant retail environment encompassing 200+ metrics and 100+ dimensions, and integrates a proprietary Domain-Specific Language (DSL) that bridges semantic reasoning with precise SQL execution. Our reinforcement learning system subsequently formulates business analysis as a Pareto Principle-guided cumulative reasoning process. Experimental results demonstrate that AIDA significantly outperforms workflow-based agents, and extensive evaluations further reveal that AIDA achieves superior environmental perception and more in-depth analysis from diverse perspectives. Our work ultimately establishes the transformative potential of autonomous intelligence for industrial-scale business intelligence systems.
Cite
@article{arxiv.2605.07202,
title = {Towards Autonomous Business Intelligence via Data-to-Insight Discovery Agent},
author = {Dongming Wu and Junwen Li and Ming Lu and Gang Wang and Ting Chen},
journal= {arXiv preprint arXiv:2605.07202},
year = {2026}
}