Nested Learning introduces a new way for models and agents to think—by recursively evaluating, refining, and improving their outputs. This session demystifies how nested loops enhance long-horizon reasoning, reduce hallucinations, and enable agents to break down complex tasks into structured, verifiable steps. We connect the concept to real agentic patterns such as self-reflection, iterative planning, memory recall, and environment-aware decision cycles. Participants learn why Nested Learning is becoming the backbone of reliable, high-accuracy, multi-step autonomous systems running in enterprise environments.