Let's explore practical strategies for building and scaling agents in production. Discover how to move from local MCP implementations to cloud-scale architectures and how engineering teams leverage these patterns to develop sophisticated agent systems. Expect a mix of demos, use case discussions, and a glimpse into the future of agentic services! About Antje Barth Antje Barth is a Principal Developer Advocate at AWS, based in San Francisco. She frequently speaks at AI engineering conferences, events, and me ...

AI Infrastructure Challenges - AI workloads (inference, training, RAG) competing for resources can cause performance bottlenecks and delays [1] - Mixed-tenant AI loads can lead to noisy-neighbor issues, impacting performance [1] Solutions & Benefits - Next-gen AI infrastructure provides full control over the environment, regardless of workload complexity [1] - Dynamic resource isolation prevents noisy-neighbor issues [1] - Efficient scaling of AI infrastructure while maintaining performance is achievable [1] Key Learning Objectives - Guarantee performance under heavy, mixed-tenant AI loads [1] - Prevent noisy-neighbor issues with dynamic resource isolation [1] - Scale AI infrastructure efficiently while maintaining performance [1]

[Music] Um, yeah. So, my name is Mike Chambers. I'm going to pick you up a little bit there. So, I'm from Queensland in the eastern part of Australia, but that's okay. Um, yeah, very happy to be here. So, I'm a developer advocate for Amazon Web Services. Um, and I completely and utterly and only and totally spe specialize in generative AI. Used to be machine learning. Now it's generative AI. Um, I'll be talking about why this slide is up here in a moment. Any tabletop RPG players in the room? There's got to ...