But a harness is static; it doesn’t learn the enterprise. As conditions change, the harness or the context has to be amended by hand, which usually means a human in the loop monitoring inputs and outputs, even for relatively straightforward tasks. That undermines much of the value agentic AI is supposed to deliver.

So, a harness is a good place to start, but it only goes so far. GenAI is probabilistic: give a model the same prompt twice and it will not give you the same answer twice. Almost everything in your organization, by contrast, was built for humans, who at least try to behave deterministically. An incorrect output from a model is not a bug; it is a signal that the context needs modifying, ideally dynamically and automatically, until the machine gets it right. Therefore, you need a loop: context goes in, output comes out, you evaluate it against a framework you have set, and the context is refined before it goes back into the machine. That is what turns a static process into something that continually improves. Experimentation has to happen inside that loop, and stakeholders have to see what it produces early and often, so you know where the model falls short before there is real money committed.

Put the people who hold the knowledge inside the pilot

An agent needs context in the way a trained, experienced SME has it and a new graduate does not. The problem is that most knowledge in most organizations is tacit. Enterprise information isn’t just data in the sense of bits and bytes you can put in a spreadsheet and feed to the machine. A lot of it is in people’s heads, and sometimes the deepest expertise is in the heads of a handful of people.