Building AI Systems We Can Trust: Reliability, Transparency, and Control

Everyone has seen impressive AI demos. Building systems that continue to work reliably after they leave the demo stage is where things get interesting.

As AI applications grow beyond single prompts, problems like maintaining context, debugging failures, reproducing outputs, and understanding what the system is actually doing become much harder. Over time, it's easy for these systems to turn into black boxes that are difficult to reason about and trust.

In this talk, I'll share lessons from building and working with production AI systems. We'll look at why observability matters, how to deal with failures and retries, and why transparency and human control become increasingly important as AI systems become more capable.

The goal of this session is not to discuss the latest models or prompting tricks, but to focus on the engineering practices that make AI systems easier to understand, maintain, and trust.

• Why AI systems become harder to debug and maintain as they grow beyond simple prompts.

• Practical approaches for improving reliability, observability, and reproducibility.

• Common failure patterns in AI systems and strategies for handling them gracefully.

• How context and state influence the behavior of AI applications.

• Principles for building AI systems that remain transparent, maintainable, and under human control.