AI systems behave very differently from traditional software.

Once AI moves beyond prototypes, teams quickly discover that the biggest challenges are rarely about the model itself. They are about the systems around it: architecture, evaluation, observability, guardrails, and the engineering discipline required to make probabilistic systems dependable.

This talk shares ten practical lessons learned from building and operating AI systems in production. We will explore why the model is only one piece of the architecture, why classic engineering fundamentals remain essential, and why teams must become comfortable working with ambiguity when building systems that are inherently non-deterministic.

We will also examine how a failure-mode mindset shapes reliable AI systems. High-performing teams design guardrails, evaluation loops, monitoring, and feedback mechanisms from the beginning rather than treating them as afterthoughts.

If you are building AI-powered applications, this talk will highlight the engineering patterns and practices that help teams move from impressive demos to dependable production systems.

Because the hardest part of AI is rarely getting the model to respond.
It is building the system around it so people can trust it.