A Practical Guide to Harness Engineering: Building the System Around the Model

An AI coding agent can write a function in seconds. But a working feature takes more than generated code. The agent needs to understand the project, find the right files, use tools safely, and check whether its changes work. A model on its own does none of that. It sees only the text it is given, and it cannot run a test or save its progress. The system that supplies those things is called a harness, and the work of designing it is called harness engineering. In this guide, we will look at what a harness is, how its parts fit together, and how it differs from prompt and context engineering. Then we will build a small harness in Python, with a sandbox, read-only tests, a step limit, saved progress, and a final check, and watch it handle a messy agent run. What Is Agent Harness? A language model generates responses from the information it receives. That simple fact leads to several limits when you ask it to do real work: It does not know which files are in your project unless you show them. It cannot run tests, call a database, or open a browser by itself. It does…