In this talk, Michael Giannangeli shares lessons from developing agentic capabilities for Amazon’s Nova foundation models. Drawing on real-world deployments and conversations with customers building complex agent workflows, he explores the challenges that emerge when moving from single-turn models to autonomous systems. Topics include how to evaluate agents beyond benchmarks, why reliability is the gating factor for adoption, the role of reinforcement learning “gyms” and simulated environments, and architectural patterns for balancing autonomy with control in high-stakes applications.
