Everyone wants agents. Almost nobody has the data layer to run them.

Same conversation maybe fifteen times over the last few months. It starts with "we want an agent that does X." Twenty minutes in, we're not talking about the agent at all, we're talking about where the data lives. Which is usually: some in Drive, some in the CRM, some in a knowledge tool nobody has updated in two years, and the rest in three people's heads. So teams do the reasonable thing and build one tool per use case. Agent bolted to one source. Then another agent, another source. It works. It just doesn't scale; you end up maintaining ten integrations to answer questions that all touch the same five systems. The push I keep making: build the clean data layer before the agents. One trusted source every agent reads from. We've been calling it a second brain internally, which sounds more mystical than it is; it's a company data layer with retrieval on top. What I've learned doing it: The model isn't the bottleneck. Hasn't been for a while. One CEO I work with put a company-wide data strategy at the top of his priority list because it was blocking every AI project they had. He's right. Sometimes you pause the interesting project. On one engagement, we paused agent discovery to build the boring layer first. Looked slower on the timeline. Was faster in practice, because nothing after it was a fight. Every one-off tool is debt. Fine for the demo. Then it's the reason nothing connects. Agents are mostly a data problem in the context of models. If you're planning them for next year, the unglamorous question comes first: can anything actually read your data reliably today?