The Data Behind a Useful AI Agent
While building a question-answering system over company data during my internship, I expected the hardest part to be getting the model to reason well. More often, the hard part came earlier: finding the right information and knowing whether it could be trusted.
An agent can sound confident with very little context. To do useful work, it needs the right context at the right time.
Retrieval Changes the Answer
If two documents disagree, the agent needs a way to tell which one is current. If a relationship lives across several systems, it needs a way to connect them. If a source is incomplete, it should be able to say so.
Those are data problems before they are model problems. A stronger model can sometimes work around messy input, but that is an expensive way to compensate for missing structure.
Why I Think Good Data Will Cost More
I expect the value of usable data to rise as more companies build agents. I don’t mean that every large dataset becomes valuable. Data that is outdated, duplicated, or impossible to use legally can create more work than it saves.
The scarce part is data that is accurate, current, relevant to a task, and available with clear rights to use it. Collecting it is only the beginning. Someone has to maintain it, resolve conflicts, and make it retrievable. As agents take on more work, that effort starts to look less like housekeeping and more like core infrastructure.
The Next Constraint
Model capability will keep improving. But when two teams can use similarly capable models, the team with better access to trustworthy information may build the more useful agent.
That has changed how I think about AI products. I still care about the model. I spend a lot more time asking what the system knows, where that knowledge came from, and what happens when it is wrong.