Turning AI agents into reliable, production-ready tools that deliver tangible business results requires more than just great models. It demands robust evaluation frameworks that ensure agents perform at scale, align with organizational objectives, and continuously improve in dynamic environments. This session provides an executive-level perspective on evaluating AI agents at scale. We’ll explore practical strategies for designing evaluation processes that drive measurable impact, identifying and mitigating performance bottlenecks, and implementing observability practices to maintain reliability over time. Through insights from real-world deployments, we’ll highlight common pitfalls, share best practices for iterative improvement, and demonstrate how effective evaluation frameworks can transform experimental agents into enterprise-grade solutions. Whether you're shaping your organization’s GenAI strategy or looking to unlock the full potential of AI agents, this talk offers actionable insights to ensure your agents work—and scale—successfully.