Production AI systems rarely fail in neat, isolated ways. Small issues in pipelines, data quality, orchestration, or infrastructure can quickly cascade into broken models, unreliable outputs, and growing operational debt. This panel brings together data and platform leaders to discuss how teams are designing AI data infrastructure that can detect issues earlier, recover faster, and reduce the need for constant manual intervention. Key takeaways: → How leading teams are building resilience into pipelines, workflows, and data infrastructure from the start → The monitoring, alerting, and recovery mechanisms that help catch failures before they impact downstream systems → Where automation, observability, and governance can reduce firefighting and create more self-healing environments