Your model is trained. Your pilot is live. Your data looks AI-ready. But for most teams, the toughest part of building successful AI starts after deployment. In this talk, Shane Murray and Ethan Post share lessons from the development of Monte Carlo’s Troubleshooting Agent – an AI assistant that helps users diagnose and fix data issues in production. They’ll unpack what it really takes to build and operate trustworthy AI systems in the real world, including: The Illusion of Done – Why deployment is just the beginning, and what breaks in production; Lessons from the Field – A behind-the-scenes look at the architecture, integration, and user experience of Monte Carlo’s agent; Operationalizing Reliability – How to evaluate AI performance, build the right team, and close the loop between users and model. Whether you're scaling RAG pipelines or running LLMs in production, you’ll leave with a playbook for building data and AI systems you—and your users—can trust.