The problem it solves
People increasingly want an AI agent that does more than chat: one that reasons over a real problem, predicts what happens next, and acts on the answer. Two things stand in the way. The first is privacy. The second is the gap between a prediction and an action.
The privacy problem
Most AI tools ask you to send your questions, your data, and your intent to someone else's server. For casual use that may be fine. For anything sensitive, how you think about risk, what you are about to do, the positions you hold, it means handing your edge and your plans to a third party and trusting them with it.
intenthybrid AI is built so the sensitive parts never leave your machine in the first place. The conversation and its history stay in your browser, and your prompts run on infrastructure you control. This removes the question of trust rather than asking you to answer it. Privacy is the core, which is why it comes first in this documentation.
The action gap
A good prediction that you cannot act on is just information. The traditional split is that one tool predicts, and you act somewhere else, by hand, hoping you are at the keyboard when the moment arrives. The world does not wait for you to be online.
intenthybrid AI closes this gap by letting the same agent that produces a prediction also act on it, inside hard limits you set. You decide the boundaries once, and the agent carries the action out when the conditions hit, whether or not you are watching.
Why one private agent
Solving these separately recreates the problem. A private predictor that cannot act leaves the action gap. An agent that can act but is not private asks you to trust a server with the most sensitive thing of all, the actions you intend to take. intenthybrid AI is a single agent that predicts and acts, with privacy holding across the whole thing, so neither gap is left open.
Seeing it concretely
The clearest way to understand the agent is through its first application, reading risk in perpetual futures markets. There, the privacy problem and the action gap are both sharp: your read on a crowded market is sensitive, and the moment to act is fleeting. See How it works for the three steps the agent runs, and Application: perp-market risk for the flagship use case.