Imagine asking your database complex questions in plain English and getting instant, accurate results. That's the promise of text-to-SQL, a field of AI research focused on translating natural language into SQL queries. But building effective text-to-SQL systems is tough. Databases can be massive, schemas complex, and natural language inherently ambiguous. A new research paper introduces CHESS, a clever multi-agent framework designed to tackle these challenges. CHESS uses four specialized AI agents working together: an Information Retriever to gather relevant data, a Schema Selector to prune large schemas, a Candidate Generator to create and refine queries, and a Unit Tester to validate the results. This collaborative approach makes CHESS highly efficient. It can handle industrial-scale databases, preserve data privacy by using open-source models, and scale with available compute resources. One of CHESS's key innovations is its ability to handle massive schemas, something that trips up even the most powerful AI models. By intelligently selecting only the necessary parts of the schema, CHESS boosts accuracy and significantly reduces processing time. The results are impressive. In tests, CHESS achieved near state-of-the-art accuracy on the challenging BIRD benchmark, even outperforming some proprietary systems while using significantly fewer resources. CHESS represents a significant step forward in making databases more accessible. By bridging the gap between human language and database queries, it opens up exciting possibilities for data analysis, reporting, and more. While challenges remain in handling even larger real-world databases and further refining the unit testing process, CHESS paves the way for a future where anyone can unlock the power of data with the simplicity of natural language.