When teams deploy AI systems into production, they expect the same behavior they saw in testing. What they get instead is drift, decay, unpredictable responses, and a long list of incidents no one has tooling for. AI systems fail differently from traditional software: models degrade silently, pipelines grow stale without warning, data contracts bend under real-world pressure, and guardrails that looked solid in staging collapse when they meet real traffic patterns.

In this talk, I break down the failure patterns I keep seeing across AI deployments: domain shift, inconsistent input semantics, runaway feedback loops, brittle fallback logic, and human-in-the-loop workflows that don’t scale beyond the first hundred users. We’ll explore what actually goes wrong in production, why observability has to look different for ML components, and how to design architectures that contain failure instead of amplifying it.

Attendees will leave with a practical toolkit for stabilizing AI systems: drift detection strategies, guardrail design patterns, resilient deployment workflows, testing approaches that simulate change, and a roadmap for building AI features that survive unpredictable environments.