Agentic AI systems that execute tasks on behalf of users face a recurring design decision: when should the agent proceed autonomously, and when should it pause to request human confirmation? Interrupting too frequently negates the efficiency gains that justify delegation in the first place, while interrupting too rarely produces runaway actions, trust violations, and the psychological costs of unchecked automation documented in earlier work. This essay examines the variables that should inform that threshold, including action reversibility, financial exposure, data sensitivity, deviation from user intent, agent confidence, and user behavioral history. Drawing on established research in interruption science, levels of automation, mixed-initiative interaction, adaptive interfaces, notification management, and trust in automation, the paper proposes a framework for determining where confirmatory friction should be placed in agent-mediated workflows. The essay further examines how thresholds might adapt over time based on user behavior, the design of the confirmation interaction itself, and failure modes that arise when agents cannot reach users or when chained actions create cascading dependencies. This manuscript synthesizes existing literature and proposes a design framework. It does not report original experimental results. Keywords: human-in-the-loop; agentic AI; interruption thresholds; friction; levels of automation; mixed-initiative interaction; confirmation design; adaptive interfaces; trust calibration; zero-UI Author: Akash Narayan (akashnarayan.com)
Akash Narayan (Sun,) studied this question.