Enterprise analytics aims to make organizational data accessible for decision-making, yet non-technical users still face barriers when using traditional business intelligence tools or Text-to-SQL systems. While recent Text-to-SQL approaches based on Large Language Models (LLMs) promise natural language access to structured data, they fall short in enterprise settings where analytics pipelines rely on governed APIs rather than raw databases. In practice, these APIs encapsulate complex business logic to ensure consistency, auditability, and security. However, delegating mathematical or aggregation logic to an LLM introduces reliability and compliance risks. To this end, we present Analytic Agent, an LLM-based agentic system that translates natural language intents into secure interactions with enterprise analytics APIs. Evaluated on 90 real enterprise use cases constructed by domain experts, it reliably interprets user goals, validates permissions, executes governed queries, and generates compliant visualizations through multi-step reasoning and policy-aware orchestration.

翻译：企业分析旨在使组织内的数据可用于决策支持，然而非技术用户在使用传统商业智能工具或文本到SQL系统时仍面临障碍。尽管基于大语言模型的最新文本到SQL方法承诺通过自然语言访问结构化数据，但在企业环境中，由于分析流水线依赖受管API而非原始数据库，这些方法存在不足。实践中，此类API封装了复杂的业务逻辑，以确保一致性、可审计性和安全性。但将数学运算或聚合逻辑委托给大语言模型会引入可靠性与合规性风险。为此，我们提出Analytic Agent——一种基于大语言模型的智能体系统，可将自然语言意图转化为与企业分析API的安全交互。该系统在由领域专家构建的90个真实企业用例上评估，通过多步推理与策略感知编排，可靠地解释用户目标、验证权限、执行受管查询并生成合规的可视化结果。