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

This study introduces AgentNLQ, a new multi-agent method for Natural Language to SQL (NL2SQL) conversion, achieving 78.1% semantic accuracy on the BIRD benchmark. It leverages LLMs in an optimized orchestrator for planning, reflection, and self-correction to generate accurate SQL queries from enriched schemas and business rules.