Mitchell Troyanovsky, a Basis co-founder, argues that individual productivity gains do not automatically translate into organization-wide gains. He identifies a competency gap for users managing agents and a process gap when individual users determine how work is performed.
Mitchell Troyanovsky describes explicit behavior specifications as a way to express desired agent conduct and assess trajectoriesEvaluation is the systematic assessment of a system's behaviour or results against explicit criteria. even when an exact ground-truth outcome is unavailable. He distinguishes this process supervision from conventional outcome evaluations.
He describes forking trajectories to investigate a decision and using cheaper simulations to test changes to an agent’s context. These methods provide additional signals, rather than proof that an agent will generalize to all production situationsModel reliability is the degree to which an AI model produces dependable behavior across repeated, varied and operationally realistic use..
Watch on YouTube
