Pablo Castro

This talk explores the three categories of knowledge that power AI agents: intrinsic, extrinsic, and learned. Intrinsic knowledge, embedded within models through training data, has been a primary driver of recent AI advancements, particularly in coding. Extrinsic knowledge, accessed through retrieval-augmented generation (RAG) patterns, allows agents to ground themselves in organizational data and external information. Learned knowledge emerges from agents' ongoing work, enabling continuous improvement and the capture of unique organizational capabilities.

This talk explores the evolving landscape of AI, focusing on how AI systems can better understand and interact with human knowledge. It delves into the challenges and opportunities in building AI that can reason, learn, and collaborate effectively, moving beyond simple pattern recognition to a deeper comprehension of context and intent. The core thesis centers on the development of AI that acts as a knowledgeable partner, capable of complex problem-solving and informed decision-making.