The field of AI and data center architectures is witnessing a significant shift towards composable workflows and disaggregated architectures. Recent research has focused on developing service fabrics that can optimize and execute complex AI workflows as shared services, enabling improved efficiency, scalability, and cost-effectiveness. Additionally, there is a growing interest in redesigning data center ecosystems to accommodate hardware disaggregation, which has the potential to transform resource allocation, scheduling, and infrastructure optimization. Noteworthy papers in this area include FlowMesh, which proposes a novel service fabric for composable LLM workflows, and Disaggregated Architectures and the Redesign of Data Center Ecosystems, which provides an overview of the motivations and recent advancements in hardware disaggregation. These developments are expected to have a significant impact on the design and operation of future data centers and AI systems.