AI systems operate in changing environments. Data shifts, assumptions evolve, dependencies update, and models continue running long after conditions have changed. When those changes are not anticipated in the system’s design, small issues can compound into larger consequences.

This session focuses on treating failure as a design consideration rather than an exception. We will examine how architectural decisions influence what happens when models drift, thresholds are misaligned, or upstream data changes unexpectedly. The discussion will cover containment strategies, escalation design, and approaches to limiting downstream impact when systems are under stress.

Participants will leave with a practical, systems-level perspective on building AI architectures that can absorb disruption, reduce unintended harm, and remain accountable when conditions change.