Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs

2026-05-20

AI Summary

This work addresses the challenge faced by non-technical users in enterprise environments who struggle to securely and compliantly access governed analytical data through natural language. Traditional text-to-SQL approaches fail to accommodate managed APIs that encapsulate complex business logic. To bridge this gap, the authors propose Analytic Agent—a large language model (LLM)-based agent system that translates natural language intents into secure invocations of governed analytical APIs through multi-step reasoning and policy-aware orchestration. The system integrates permission validation, query execution, and compliance-aware visualization, achieving the first deep integration of LLM agents with enterprise-grade governed APIs while ensuring auditability, consistency, and security. Evaluation on 90 real-world enterprise use cases demonstrates that the system accurately interprets user intent, executes compliant queries, and generates visual results, significantly enhancing self-service analytics capabilities for non-technical users.

Abstract

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.

Problem

• Enterprise Analytics
• Governed APIs
• Text-to-SQL
• LLM Reliability
• Compliance

Innovation

• Agentic LLM
• Governed APIs
• Enterprise Analytics
• Policy-aware Orchestration
• Natural Language to API