Most AI products perform well in demos. Far fewer perform reliably in production. As organizations race to operationalize generative and agentic AI, product teams are discovering that traditional product development approaches break down when systems become probabilistic, adaptive, and increasingly autonomous. In this main stage session, Balachander Keelapudi and Madhuri Peri from AWS explore the emerging shift toward spec-driven and evaluation-driven AI product development — and why these frameworks are quickly becoming foundational for building trustworthy, scalable AI systems. Drawing from real-world experience building reliable AI agents and enterprise AI workflows, the session explores: - why evals are becoming a core product management capability, - how leading teams are moving beyond prompt engineering toward systematic AI quality frameworks, - the operational realities of building reliable AI agents, - and how non-technical product leaders can successfully drive AI initiatives without needing deep ML expertise. Attendees will gain a practical understanding of how modern product organizations can move from AI experimentation toward scalable, measurable, production-ready AI systems.