A deep dive into how agentic AI systems improve upon traditional text-to-SQL approaches. Covers the spectrum from direct chat and RAG to fully agentic LLM systems, explaining how LLMs solve long-standing schema-linking problems without schema-specific training. Includes a historical table tracing text-to-SQL research from 1982 grammar-based systems through deep neural networks to modern LLM agents. Also discusses evaluation using Spider and Spider 2 benchmarks, practical limitations (latency, complex schemas, transactional locking), and how Red Hat OpenShift AI with EDB's PG Airman MCP server implements conversational analytics.
Table of contents
Agentic AI versus chat and RAGThe modern agentic AI approach to text-to-SQLPowering conversational analytics with LLM-based AI agentsEvaluationNext steps92 Impressions