让大模型安全调用企业数据API，自动完成合规分析任务
Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs
- 构建智能代理系统，将自然语言转化为对受控API的安全调用
- 在90个真实企业场景中实现高准确率意图理解与合规查询执行
- 适合需要安全、可审计数据分析的企业用户和AI工程师
企业数据分析旨在使组织数据可用于决策，但非技术用户在使用传统商业智能工具或Text-to-SQL系统时仍面临障碍。尽管基于大语言模型（LLMs）的Text-to-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，分析流程依赖受管控的API而非原始数据库。这些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。然而，将数学或聚合逻辑交由LLM处理会带来可靠性与合规风险。为此，我们提出Analytic Agent，一个基于LLM的智能体系统，能够将自然语言意图转化为对企业分析API的安全交互。在领域专家构建的90个真实企业用例上评估，该系统能可靠地理解用户目标，验证权限，执行受控查询，并通过多步推理与策略感知编排生成合规可视化。
原文摘要 · Abstract (English)
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.
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