Many teams can build an impressive AI agent demo. The harder part is turning it into a production-ready agent that can safely use tools, reason over messy enterprise data, and earn trust from developers, users, and stakeholders.

This session walks through a practical architecture for moving from prototype to production: preparing AI-ready data before model invocation, exposing tools through MCP or API-based integration layers, securing tool access with identity and authorization, adding retrieval and ranking patterns, and instrumenting agent workflows with logging, traces, evaluation, and feedback loops.

Using public-safe enterprise examples from service, dealer, and quality workflows, we will break down the decisions that make agents useful beyond a chat window. Attendees will leave with a blueprint for building agents that are secure, observable, reusable, and ready for real product teams.