The knowledge behind a trustworthy answer

A business can have the right data and still lack an answer it can trust. Which customer record is the real one? What counts as revenue? Which period does the business actually mean? These are not simply questions of SQL syntax. They depend on definitions, exceptions, relationships, and experience that may never have been written down for a machine.

Building Enterprise AI Agents: Text-to-SQL and Structured Data for Answers Your Business Can Trust follows that knowledge from the people who hold it into a form an AI system can use. Viren Shah begins with the shapes of structured data and the metadata that describes them, then examines how models read that information, interpret a business question, and produce a query.

The book continues beyond generating SQL. It explains why queries need checks before execution, why access and cost limits matter, how results can be tested before anyone acts on them, and why human review and a record of the decision remain part of the process. Later chapters move from models to agents, showing how focused tasks and operating controls fit into a larger system.

Written for data professionals, business operators, and executives, the book gives these groups a shared way to understand and evaluate the work. Technical teams bring knowledge of systems and data. Business operators bring the meaning behind everyday decisions. Executives need to understand the foundations they are funding and the limits that still apply.

This is a focused guide to structured data and trustworthy business answers, not a general survey of AI or a manual for one vendor's product. Its central concern is the one expressed on the back cover: bringing the knowledge an organization already holds into a form an agent can use, and distinguishing a convincing demonstration from an answer that can be checked.