Building Blocks to Breakthroughs: Algebra to Agentic Systems

The session touches on progression of AI from foundational mathematics to advanced agentic systems. It highlights how core mathematical principles, like linear algebra and calculus, laid the groundwork for deep neural networks (DNNs). It then explores the transformer architecture, which revolutionized DNNs with attention mechanisms, paving the way for powerful large language models (LLMs). Finally, it discusses agents—data agents, foundry agents, and copilot studio agents—that leverage LLMs to form integrated, autonomous agentic systems for diverse, context-driven applications.