Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs
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arXiv
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| Format: | Preprint |
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2026
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| author | Singh, Gundeep Kavehzadeh, Parsa Xia, Jing Fu, Xue-Yong Tremblay, Julien Bouvier Laskar, Md Tahmid Rahman Lum, Vincent TN, Shashi Bhushan |
| author_facet | Singh, Gundeep Kavehzadeh, Parsa Xia, Jing Fu, Xue-Yong Tremblay, Julien Bouvier Laskar, Md Tahmid Rahman Lum, Vincent TN, Shashi Bhushan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21027 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs Singh, Gundeep Kavehzadeh, Parsa Xia, Jing Fu, Xue-Yong Tremblay, Julien Bouvier Laskar, Md Tahmid Rahman Lum, Vincent TN, Shashi Bhushan Computation and Language Artificial Intelligence 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. |
| title | Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2605.21027 |