A question the market is asking after a summer of agent headlines: if an AI agent learned to behave safely on the tasks it was trained on, will it behave safely on a task it has never seen?
Our CTO Nadav Cohen's group at Tel Aviv University answered it in a paper this spring, and the answer is no, and not because of a training shortcut. They proved that safe execution of a task is mathematically more sensitive to what the task is than execution alone. Doing the job transfers. Doing it safely does not.
They showed it three ways: in a classical control problem, in a simulated quadcopter, and in a language-model agent working a CRM system.
The consequence for a plant is direct. A general-purpose agent that was safe somewhere else is not, by that fact, safe on your unit.
We built the other way. A closed-loop DLPC® is built per unit, runs inside the envelope its engineers set, and operators can take over at any time. The safety is in the design, not in the hope that training carried over.
Read the paper: "Why Does Agentic Safety Fail to Generalize Across Tasks?" (Slutzky, Alexander, Slor, Nagel, Cohen)
