Anyone can build an AI agent in an afternoon. Give a model some tools, write a loop, watch it call an API and summarise a web page. It feels like magic.

Then you put it in front of a real business. It touches a customer record it should not touch. It runs for forty minutes and forgets what it was doing. It passes your test suite because it wrote the tests. Finance wants to know who approved the change, and the honest answer is "the model, probably."

That gap between "it works on my laptop" and "it works on Monday morning with real data" has picked up a name: harness engineering . (You'll also hear "agentic engineering" and "context engineering" for overlapping ideas. The label matters less than the discipline.)

The idea is simple. An agent is not just a model. It is a model plus everything wrapped around it: the loop that drives it, the tools it can reach, the permissions that stop it, the files that remember for it, the tests that check it, and the people who sign off on it. That wrapper is the harness.…