Most people waiting for AI to get good at investing are waiting for the wrong thing. They are waiting for a bigger model, the next release, the version that finally reasons well enough to be trusted with money. The research points somewhere less convenient. The difference between an AI that produces real analysis and one that produces confident filler is rarely the model. It is the process the model is asked to follow.

It is worth being clear about what is being compared. A one-shot prompt asks for the whole answer in a single pass. Chain-of-thought asks for the reasoning in that same single pass, the model narrating its steps before committing to an answer. A structured workflow does something different in kind. It breaks the task into stages, lets the model retrieve, check or evaluate between them, and allows it to abandon a line of attack rather than ride the first one to the end. The three look like cousins. On a hard problem they behave like different algorithms.

If you click a button promising the research behind a product, you deserve the first finding up front: the model inside Tesseract Stock Agent is the same one you could rent yourself by this afternoon. There is no secret brain. What there is instead is a set of decisions about how to make an ordinary model behave like a careful researcher, and those decisions are not arbitrary, they follow a fairly clear direction in the AI literature of the last few years. This page is the honest version of that, what the research supports and, just as importantly, what it does not. I would rather you trust the second half than the first.