A general agent is sharp, but it does not know your world. Ask it to build a health product, and it will guess at what doctors mean. Ask it to build for advertising, and it will guess at the words and the rules of that trade.
The guessing is the problem. The output comes back confident, clean, and sometimes just wrong. Not because the agent is careless. Because nobody told it the field it was playing on.
I see it the way I would see a new hire. Smart, fast, eager. Drop them into a business they have never touched and tell them to ship, and they will fill the gaps with their best guess. A good hire reads up first. The agents were not reading up first. They had nothing to read.
So I build the missing thing. Each product gets its own body of domain knowledge. The terms that matter, the rules it has to respect, the way people in that world actually talk. The agents read it before they touch anything.
I decide what each venture has to know. That part is mine. A health product and an advertising product do not need the same grounding, and pretending they do is how you get generic. The agents then take that grounding and work inside it instead of around it.
The change is quiet, but it is the whole thing. The agents stop inventing the domain. They start standing on it.
Learnings
AI is only as good as the context you hand it. The agents were never the weak link. The missing piece was the world they were building in, which only I could give them. Teach a product its domain and the work gets grounded. Skip that step and the agent will fill the silence with a confident version of the domain it made up.