AgentNLQ: A General-Purpose Agent for Natural Language to SQL
This study presents a multi-agent method for NL2SQL that achieves 78.1% semantic accuracy on the BIRD benchmark. It leverages semantically enriched schema representation and business rules to generate accurate SQL queries, with key contributions including an optimized orchestrator, advanced schema enrichment, and demonstrated cross-domain generalization.
[2605.19010] AgentNLQ: A General-Purpose Agent for Natural Language to SQL
[Submitted on 18 May 2026]
Title:AgentNLQ: A General-Purpose Agent for Natural Language to SQL
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Abstract:Natural language to SQL (NL2SQL) conversion is an important problem for researchers and enterprises due to the ubiquitous importance of relational databases in broad-ranging practical problems. Despite the rapid advancements in the capabilities of LLMs, NL2SQL has not reached parity in accuracy with human expert SQL writers, hence needing additional improvements in NL2SQL algorithms. This study presents a new multi-agent method for NL2SQL that achieves 78.1% semantic accuracy on the BIg Bench for LaRge-scale Database (BIRD) benchmark. Our method leverages a semantically enriched representation of user-provided schema, adds user-provided business rules, and produces accurate SQL queries. The main contributions of this study are (a) We designed an optimized new orchestrator in a multi-agent solution that uses LLMs to plan, orchestrate, reflect, and self-correct to generate accurate SQL queries, (b) We developed an advanced schema enrichment method that creates context-aware metadata to improve accuracy, and (c) We demonstrated the accuracy and generalizability of the method across different domains and datasets by evaluating it on the BIRD-SQL benchmark.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.19010 [cs.AI]
(or arXiv:2605.19010v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.19010
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chandra Dhir [view email] [v1] Mon, 18 May 2026 18:33:15 UTC (551 KB)
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