If you've spent any time building with AI lately, you've probably heard the word "agent" thrown around a lot. But here's something that doesn't get talked about nearly as much: before you can have a real AI agent, you need a harness. I know that term might sound unfamiliar or even a little abstract. When I first came across it, I had the same reaction. But once it clicked, I couldn't unsee it — and I genuinely think it's one of the most important concepts to understand if you want to go beyond just calling an LLM API and actually building something that does things autonomously. Let's break it all down from scratch. The Problem With "Just Using a Model" Picture this: you've got API access to a powerful model like Claude or GPT-4. You send it a prompt, it sends back a response. That's great for chatbots and one-shot completions — but what if you want the model to: Browse the web and pull real-time data? Execute Python code to analyze that data? Remember what you told it last week?…