A system is approved for one purpose. The record says what it is supposed to do. The controls say what it is allowed to reach. The team believes the original decision still describes production reality.
Then the operating environment changes. Inputs shift. users learn how to ask different questions. Upstream data changes. A workflow expands. A business team begins relying on outputs in a way nobody reviewed. The system is not broken. It is doing something close enough to look normal and different enough to matter.
That distance is the Intent Gap. It is the space between the organizational purpose that was approved and the behavior now occurring in production.
Public AI incidents show why this matters. Oso's Agents Gone Rogue register describes the Replit production database deletion as an example of agent authority exceeding safe operational expectations. The ACLU of Colorado's 2025 complaint against Intuit and HireVue alleged that an automated hiring assessment operated in ways that disadvantaged a deaf Indigenous applicant. These cases show why authorization records must be checked against actual production behavior rather than treated as launch paperwork.
Intent Gap is the unplanned divergence between what an organization genuinely intended an AI system to do and what the system actually does in production. It is not measured at launch. It is measured after use begins, when real users, real data, and real workflows create behavior the original authorization record may no longer describe.
The concept matters because most governance records are static. Production behavior is not. A system can remain inside its technical permission boundary while drifting away from organizational intent. That makes the gap difficult to see if the organization only monitors access, uptime, and incident tickets.
