Towards Autonomous Business Intelligence via Data-to-Insight Discovery Agent

TL;DR

This paper introduces AIDA, an autonomous system that leverages reinforcement learning and a domain-specific language to enable deep, multi-dimensional business data analysis, outperforming existing agents.

Contribution

The paper presents the first end-to-end framework for autonomous business data exploration, integrating a DSL and Pareto-guided reasoning to enhance analysis depth and accuracy.

Findings

AIDA outperforms workflow-based agents in experiments.

AIDA achieves superior environmental perception.

AIDA provides more in-depth analysis from diverse perspectives.

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.