EP 36 | CME295: Agentic LLMs

What happens when an AI stops just answering questions and starts taking action? In this episode, we explore Agentic LLMs. We break down how language models are evolving from passive text-generators into autonomous agents capable of planning, using tools, and interacting with the digital world.Key Topics:Tool Use: How models learn to trigger external APIs, run code, or browse the web to gather information they weren't explicitly trained on.Planning and Reasoning: Exploring frameworks like ReAct (Reasoning and Acting) that allow a model to break down a complex goal into a multi-step execution plan.Autonomous Loops: The shift from single-prompt interactions to agents that can self-correct, loop through tasks, and evaluate their own progress.Note: This is an AI-generated study resource created via NotebookLM based on the Stanford CME295 curriculum and personal study notes.