Everyone has seen the AI demo that works perfectly: give an agent a prompt, connect a few tools, ask a question and watch it produce an impressive result.

Then real users arrive.

The context is incomplete. A tool returns unexpected data. The model makes a confident assumption. Costs start accumulating. A workflow that looked reliable in a prototype becomes unpredictable when people actually depend on it.

This session explores what happens between an impressive AI prototype and a system that can survive real use. We will break down practical failure points across agents, RAG, tool calling, evaluation, guardrails, observability and cost, and examine the engineering decisions that determine whether an AI solution becomes useful or remains a demo.

Through concrete examples and failure scenarios, attendees will learn how to move beyond "it works on my prompt" and build AI systems that are measurable, controllable and ready for real-world use.

The goal is not another perfect AI demo. It is the part of the story that usually gets edited out: what broke, why it broke, and what we changed to make it work.