Databricks research shows multi-step agents consistently outperform single-turn RAG when answers span databases and documents

Data teams building AI agents keep running into the same failure mode. Questions that require joining structured data with unstructured content, sales figures alongside customer reviews or citation ...
IEEE

NLSQL: Generating and Executing SQL Queries via Natural Language Using Large Language Models

Abstract: The goal of data management has long been to make data more approachable for non-technical users. Natural language interfaces (NLIs), which let users interact with data by asking questions ...
InfoQ

Building Hierarchical Agentic RAG Systems: Multi-Modal Reasoning with Autonomous Error Recovery

Traditional RAG systems struggle bridging structured SQL databases and unstructured document collections (a challenge we call the modality gap), leading to incomplete reasoning and hallucinations.
GitHub

anagh9/llm-sql-tool

A sophisticated AI-powered system that converts natural language questions into SQL queries and executes them against a MySQL database. Built with OpenAI's GPT-4, FastMCP protocol, and intelligent ...