AIDA让大模型自动从企业数据中发现商业洞察，无需人工干预。
Towards Autonomous Business Intelligence via Data-to-Insight Discovery Agent
- 构建端到端智能体，融合语义推理与精准SQL生成。
- 在200+指标、100+维度环境中实现深度多维分析。
- 适合需要自动化商业分析的大型企业或数据团队。
将分散的企业数据转化为可操作的洞察仍是大模型面临的重大挑战，受限于复杂的数据库模式、动态SQL生成能力不足以及深层多维分析需求。本文提出AIDA（自主洞察发现智能体），首个专为复杂商业环境设计的端到端自主探索框架。我们构建了一个包含200多个指标和100多个维度的即时零售仿真环境，并集成专有的领域特定语言（DSL），实现语义推理与精确SQL执行的衔接。通过强化学习系统，将业务分析建模为受帕累托原则引导的累积推理过程。实验表明，AIDA显著优于基于工作流的智能体，且在多种评估中展现出更优的环境感知能力与多视角深度分析能力。本工作最终确立了自主智能在工业级商业智能系统中的变革潜力。
原文摘要 · Abstract (English)
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.
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