Designing Reliable AI Systems: Why Most AI Projects Fail in Production
Hosted by Deb Paul
Registration
Past Event
About Event
While AI demos look impressive, production systems often fail due to instability, reward misalignment, and poor evaluation design. This session bridges reinforcement learning theory with real-world business risk. We analyze common failure modes in agent systems, including hallucination, reward hacking, tool misuse, and long-horizon degradation.
Attendees will learn:
How reward design influences system behavior
Why evaluation is a strategic moat
Stability principles for long workflows
What separates prototypes from production AI