Designing Reliable AI Systems: Why Most AI Projects Fail in Production
Hosted by Deb Paul
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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