Natural Language to SQL: 6 Biggest Challenges for Multi-Agent Systems

Description

Many models are great at writing SQL, but they often fail in subtle ways. In this video, we'll break down the six most common failures of NL-to-SQL and show you how to build a robust, production-ready solution using a multi-agent system with Google's Agent Development Kit (ADK). We'll move beyond a single, unreliable LLM and build a "virtual data analyst" — a team of specialized agents that work together. We'll use a SequentialAgent to manage the workflow, a SchemaExtractor to find the right tables, and a generate-and-critique loop to write and correct the SQL. This isn't about better prompts; it's about better systems engineering. By the end of this tutorial, you'll understand how to use orchestration, deterministic logic, and safeguards to build AI systems you can actually trust. 🕒 TIMESTAMPS: 00:00 - Why NL-to-SQL is a Deceptively Hard Problem 00:12 - Failure 1: Schema Hallucinations 00:30 - Failure 2: Incorrect Order of Operations 00:44 - Failure 3: Complex SQL Logic Errors 01:02 - Failure 4: Dangerous Query Execution 01:17 - Failure 5: Slow Performance & High Cost 01:33 - Failure 6: Messy LLM Output (Preamble & Markdown) 01:49 - Which failure have you run into most? 🔗 RESOURCES & CODE: 💻 GitHub Sample Project: Get all the code shown in this video from the text-to-sql-agent repository. https://github.com/kweinmeister/text-to-sql-agent 📖 Read the Original Blog Post: https://medium.com/@kweinmeister/the-six-failures-of-text-to-sql-and-how-to-fix-them-with-agents-ef5fd2b74b68 🎓 LEARN MORE (Codelabs): Build Multi-Agent Systems with ADK: https://codelabs.developers.google.com/codelabs/production-ready-ai-with-gc/3-developing-agents/build-a-multi-agent-system-with-adk AlloyDB AI Natural Language for SQL: https://codelabs.developers.google.com/alloydb-ai-nl-sql #TextToSQL #AIAgents #NLToSQL #SQL #DataEngineering