This YouTube lecture explains Large Language Model (LLM) agents, contrasting them with LLMs. Agents exhibit four key patterns: reflection (questioning the request), tool use (leveraging external resources), planning (breaking tasks into steps), and multi-agent collaboration. The presenter details an agent equation (Agent = LLM + Observation + Thought + Memory + Action) and builds a framework for creating agents, demonstrating how each component impacts cost (primarily through increased prompt tokens). Several agent frameworks (AutoGen, MetaGPT, and Crew AI) are compared, highlighting their approaches to generating the complex prompts needed for agent behavior. The lecture concludes with a discussion of multi-agent systems and their implementation.