AI agents are poised to revolutionise data interaction, promising a future where anyone can query complex databases using natural language. However, the gap between a flashy prototype and a reliable, production-ready tool is vast. Early-stage Natural Language-to-SQL agents often falter under real-world pressure, generating hallucinated SQL queries, inaccurate results, and a frustrating user experience.

This session tackles these challenges head-on. We will dive deep into Google's Agent Development Kit (ADK) to build and optimise a robust data agent from the ground up. We'll explore practical techniques for prompt engineering, implementing validation and self-correction loops, and grounding the agent with the right context to ensure accuracy.

Leave this session with a clear, actionable playbook for transforming your unreliable data agents into unstoppable, production-grade assets that your users can trust.