By 2029, permission to deploy high-risk AI could depend less on benchmark performance and more on mathematically verified execution environments. The goal would not be to prove every answer correct, but to verify that an agent cannot exceed defined permissions or escape its isolation boundary.
In this scenario, high-risk agents may operate only inside runtimes whose security properties can be checked against formal specifications. Developers respond by reducing privileges, narrowing tool interfaces, and separating critical actions from general reasoning. Some capable models remain unavailable because their behavior or surrounding infrastructure cannot satisfy the required proof. Competition shifts from raw performance toward systems that are both useful and demonstrably containable.
At 10:40 p.m. in a Seoul hospital, safety engineer Jisoo watches two agents complete a simulated medication-order workflow. One is faster and gives clearer explanations, but its supplier cannot prove that outbound network access remains blocked in every permitted runtime state. Jisoo marks it ineligible and approves the slower agent whose execution boundary can be verified.
Formal verification proves only the properties included in a specification. It does not establish that a model's advice is true, fair, clinically sound, or safe against threats the specification failed to anticipate. Verification costs could also favor large vendors unless shared tools and standards lower the barrier for smaller developers.