Getting a GenAI system to run once is easy keeping it reliable, efficient, and cost-effective at scale is another story. In this deeply technical panel, engineering leaders reveal what happens when GenAI systems meet real-world conditions. They’ll unpack infrastructure design, model orchestration, and observability strategies for keeping latency low, costs predictable, and performance consistent even as models evolve and data drifts. Key takeaways: → How to design scalable, fault-tolerant architectures for genAI workloads. → The evaluation and monitoring techniques used by top engineering teams. → Practical ways to handle unstable APIs, model updates, and unpredictable behavior.