The Agentic Oversight Tax: Human Supervision of AI Agents Has a Cost that Must be Accounted For

This position paper argues that human supervision of AI agents imposes a composite cognitive, affective and organizational cost that no existing framework jointly captures. We introduce the Agentic Oversight Tax (AOT): the aggregate burden imposed on a human supervisor by the structural mismatch between the operating characteristics of one or more AI agents and the finite neurocognitive capacities of human oversight. AOT decomposes into four measurable pillars: (i) monitoring time, (ii) handoff repair events, (iii) audit production effort and (iv) recovery effort under staged failures. We anchor each pillar in documented supervision failures from 2024 to 2026 and cross-validate against the MAST taxonomy of 1,600 multi-agent system breakdowns. We establish discriminant validity against 5 adjacent frameworks by identifying three phenomena all five structurally exclude: attribution burden, accountability gap and skill atrophy. Three falsifiable predictions distinguish AOT from these constructs. A three-tier measurement architecture (telemetry, self-report, neurophysiological sensing) specifies how the pillars can be assessed. We show that agentic AI architectures modeled on Kahneman’s System 1/System 2 metaphor generate an attribution bias that AOT instruments must control for. A pre-registered meta-analysis of 100+ human-AI experiments shows that the net negative utility threshold AOT defines is already crossed on average in decision-making tasks. Governance frameworks mandating human oversight without measuring its cost cannot verify whether that oversight remains effective.