mlsanityData to 7 Oct 2026

AgentNLQ: A General-Purpose Agent for Natural Language to SQL

arXiv 2605.19010 ↗First posted 18 May 2026cs.AIcited by 2

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 10 of its 19 references that are in the map. First posted on arXiv 18 May 2026.

Builds on

10 of 19 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025ReFoRCE: A Text-to-SQL Agent with Self-Refinement, Consensus Enforcement, and Column Exploration2 Feb
  2. 2023MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL18 Dec

Other references · 8

  1. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  2. 2024Magentic-One: A Generalist Multi-Agent System for Solving Complex Taskscited by 221
  3. 2024CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQLcited by 132
  4. 2025Command A: An Enterprise-Ready Large Language Modelcited by 57
  5. 2025XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQLcited by 40
  6. 2024Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench Benchmarkcited by 33
  7. 2024ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaborationcited by 22
  8. 2024From Natural Language to SQL: Review of LLM-based Text-to-SQL Systemscited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agentscited by 0
  2. 2026ReToolSQL: Agentic Reinforcement Learning for Robust Text-to-SQLcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026An Agentic System for Schema Aware NL2SQL Generation
  2. 2026Arming Data Agents with Tribal Knowledge
  3. 2026A Semantic-Layer-Mediated Agent for Natural Language to SQL over Heterogeneous Enterprise Databases
  4. 2025Text2Schema: Filling the Gap in Designing Database Table Structures based on Natural Language
  5. 2024NAT-NL2GQL: A Novel Multi-Agent Framework for Translating Natural Language to Graph Query Language
  6. 2025Agentic NL2SQL to Reduce Computational Costs
  7. 2025BAPPA: Benchmarking Agents, Plans, and Pipelines for Automated Text-to-SQL Generation
  8. 2025Multilingual Text-to-SQL: Benchmarking the Limits of Language Models with Collaborative Language Agents