The Anatomy of an AI Agent: Memory, Tools, Planning, and Execution Explained

Everyone's talking about AI agents. But most explanations jump straight to frameworks — LangChain, CrewAI, AutoGen — without explaining what an agent actually is under the hood. Before you pick a framework, you need to understand the four building blocks every agent is made of: memory, tools, planning, and execution. Get these right in your head and every framework, every paper, every architecture diagram suddenly makes sense. Let's break it down. What Makes Something an "Agent"? A regular LLM call is stateless. You send a prompt, you get a response, it's done. No memory of what came before. No ability to take action in the world. No loop. An agent is different. At its simplest, an agent is an LLM in a loop — one that can observe its environment, decide what to do next, take an action, and then observe the result of that action before deciding again. Observe → Think → Act → Observe → Think → Act → ... That loop is what separates a chatbot from an agent. And the four components below ar