Abstract
<jats:p>The central task of physical characterization tools is to render unknown physical environments observable and explorable. Traditionally, progress in this field has been framed in terms of the physical instrumentation, including novel modalities, better probes, detectors, and electronics, stability, resolution, and sensitivity. However, there is an equally important control problem: how the instrument should act, at what time scale, and with what level of complexity to extract useful scientific information. Here, we introduce the utility of control as a general principle for autonomous experimentation, defined as maximizing useful scientific outcome while minimizing the required control bandwidth, action-space dimensionality, command description length, and fragility under uncertainty. This naturally defines a Pareto problem in which learning, robustness, physical cost, and control complexity must be balanced. We discuss these issues using scanning probe microscopy as an example and show how they motivate the design of low-dimensional, physically meaningful control planes and operational primitives that compress complex low-level actions into short semantic commands while preserving the degrees of freedom needed for discovery. This perspective connects bottom-up optimization, top-down agentic planning, and tool creation, where optimizers operate within selected control planes, agents choose which primitives should be used and why, and tool creation builds new compressed primitives that generate scientific utility without exposing unnecessary low-level bandwidth. We argue that the development of the next generation of autonomous microscopes and physical characterization tools more broadly will be defined by utility of control.</jats:p>