Harness engineering is how AI developers are now trying to make agents work reliably | Explained

The story so far: For the past few years, improving AI largely meant improving what the model was told and what it could see. Prompt engineering emerged as a way of giving models better instructions. As tasks grew more complex, the focus expanded to context engineering, giving a model the right information at the right point rather than simply giving it more information.

AI agents take this a step further. An agent can plan a task, use software tools, retrieve information and take actions, rather than simply generate an answer for a person to act on. This means developers have to consider not just what the model knows, but what it can access, what it is allowed to do and how its work is checked. This is where harness engineering comes in.