Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs
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.
Keywords
Cite
@article{arxiv.2605.21027,
title = {Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs},
author = {Gundeep Singh and Parsa Kavehzadeh and Jing Xia and Xue-Yong Fu and Julien Bouvier Tremblay and Md Tahmid Rahman Laskar and Vincent Lum and Shashi Bhushan TN},
journal= {arXiv preprint arXiv:2605.21027},
year = {2026}
}
Comments
The first four authors contributed equally to this work