This hands-on tutorial will guide participants through building an end-to-end AI agent that translates natural language questions into SQL queries, validates and executes them on live databases, and returns accurate responses. Participants will build a system that intelligently routes between a specialized SQL agent and a ReAct chat agent, implementing RAG for query similarity matching, comprehensive safety validation, and human-in-the-loop confirmation. By the end of this session, attendees will have created a powerful and extensible system they can adapt to their own data sources.

Using LiteLLM in a Real-World RAG System: What Worked and What Didn’t

LiteLLM provides a unified interface to work with multiple LLM providers—but how well does it hold up in practice? In this talk, I’ll share how we used LiteLLM in a production system to simplify model access and handle token budgets. I’ll outline the benefits, the hidden trade-offs, and the situations where the abstraction helped—or got in the way. This is a practical, developer-focused session on integrating LiteLLM into real workflows, including lessons learned and limitations. If you’re considering LiteLLM, this talk offers a grounded look at using it beyond simple prototypes.