Agentic Systems: Architectures at Microsoft (E. 29)

The conversation delves into Victor Dibia's career journey, global experiences, transition to a PhD, and strategic career planning. It also explores his focus on AI tooling and frameworks, as well as the evolution of Autogen and the Microsoft Agent Framework. The conversation delves into the actor-first paradigm in multi-agent systems and the concept of ensembling in machine learning. It explores the benefits of the actor-first approach and the considerations for using multiple agents in complex tasks. Additionally, it discusses the power of ensembling in complementing the biases of individual models and the potential for mixture of experts in achieving better performance.TopicsCareer progression through diverse experiencesActor-first paradigm in multi-agent systemsAutogen and Semantic KernelChapters00:00 Career Journey and Global Experiences11:03 Focus on AI Tooling and Frameworks19:11 Evolution of Autogen and Microsoft Agent Framework28:59 Actor-First Paradigm in Multi-Agent Systems36:39 Ensembling in Machine Learning