What Broke First? Lessons From Building Production AI Systems at Scale
About this session
AI systems often look great in demos, but production introduces a different set of challenges. Context grows, costs rise, tools fail, and small issues can quickly become reliability problems.
In this session, I'll share lessons learned from building and operating AI systems at scale, including what broke first, what didn't work, and the design patterns that helped. We'll cover practical approaches to state management, observability, and failure isolation, along with the metrics that matter when moving from prototype to production.
Attendees will leave with concrete ideas for building AI systems that are more reliable, scalable, and easier to operate.
Speaker
Key takeaways
- Identify the most common failure modes that emerge when AI systems move from prototype to production.
- Learn practical architectural patterns that improve reliability, scalability, and observability.
- Understand which metrics matter and how to avoid costly operational surprises.