AI learns the same way a junior engineer does. Through reinforcement. Through feedback. Through someone watching closely enough to catch the drift early and correct it before it becomes a habit.

Most people do not think about it that way. They treat an agent like a search engine with ambition — give it a task, expect a result, get frustrated when it does something unexpected. That is the wrong mental model and it produces the wrong outcomes.

You would not hire a junior engineer on a Monday and hand them a critical system to work on unsupervised by Friday. You would sit with them. Watch how they think. Point out where they are going off track. Build up a shared picture of how work gets done here, what matters, what does not, and where the lines are. Over time, through repetition, they get it. The reps are not wasted. They compound.

Agents work the same way. They just run the reps at a speed no human can match.

The mistake I see most often is skipping that stage entirely. Throwing agents at real work before anyone has done the hours to understand where they drift, what they misread, and what happens when the instruction is ambiguous. The answer to that ambiguity, by the way, is never what you hoped. A human cleaning a folder who sees a file called "Important, do not delete" will hesitate. They will read the room. An agent will not — unless you told it to. That distinction matters more than most people realise until they have seen it go wrong.

I have spent hundreds of hours watching agents work inside my own workflows. Steering them when they go off track. Watching the same failure modes appear again and again until I understood them well enough to encode the correction explicitly. That is putting the reps in. It is not glamorous and it is not fast, but it is the only way to know what the explicit instructions actually need to say.

That last point is important. The learning loop has to live in your system, not in the agent. Agents do not carry lessons from one session to the next the way a person does. What carries forward is what you write down. The do's. The don'ts. The stop conditions. The escalation rules. The boundaries that make a bounded task actually bounded. Every rep that teaches you something is only useful if it ends up encoded somewhere the next run can use it.

There is another layer worth being honest about. Agents are trained on the internet. That means they are a remix of everything out there — the good engineering practice and the bad, the careful writing and the careless. They do not arrive with a clean set of instilled values or a professional baseline you can rely on. They arrive capable and undirected. The direction is your job.

That is not a criticism of the technology. It is just an accurate description of what you are working with. And once you accept it, the path forward is obvious: you put the reps in, you watch what happens, you encode what you learn, and you build a system that keeps the agent working the way a good engineer would work when standards have to hold under pressure.

The teams that figure this out are not the ones with the most sophisticated models. They are the ones who have done the hours. Who know where their agents drift. Who have built the explicit instruction that stops drift before it starts.

That is the work. It is not optional. And it compounds faster than you would expect.

If you want to see what that looks like in practice, the public proof surface is here: exploremyprofile.com/dark-factory