Multi-Agent Architecture with RAG and Dynamic Context Windows for Text-to-SQL Optimization

2026-04-12 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A multi-agent Text-to-SQL architecture has been developed to optimize the translation of natural language questions into SQL queries, specifically for large corporate databases. This architecture integrates Retrieval Augmented Generation (RAG) with dynamic context windows and metadata dictionaries. The system's core innovation lies in its ability to select only the relevant tables and columns at query time, rather than sending the entire database schema with each prompt. In a case study involving Firebird enterprise databases, this approach achieved an average reduction of 84.4% in processed tokens, leading to more efficient query generation without compromising output quality. This advancement aims to democratize access to complex corporate data.

Key takeaway

For AI Engineers building natural language interfaces for large databases, this multi-agent Text-to-SQL architecture offers a significant reduction in token consumption. You should consider integrating dynamic context windows with RAG and metadata dictionaries to improve query efficiency and reduce computational costs, especially when dealing with hundreds of tables. This approach can make LLM-powered database access more scalable and cost-effective.

Key insights

A multi-agent Text-to-SQL system uses RAG and dynamic context to reduce token consumption.

Principles

• Dynamic context windows improve LLM efficiency.
• RAG can enhance schema selection for Text-to-SQL.

Method

The architecture combines RAG and metadata dictionaries within a multi-agent framework to dynamically select relevant database tables and columns, optimizing token usage for Text-to-SQL tasks.

In practice

• Implement RAG for database schema filtering.
• Utilize metadata dictionaries for context selection.

Topics

• Multi-Agent Architecture
• Text-to-SQL
• Retrieval-Augmented Generation
• Dynamic Context Windows
• Database Schema Optimization

Best for: AI Engineer, Machine Learning Engineer, Research Scientist, AI Scientist, NLP Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.