Search

Closed-loop systems have become foundational to computational and data-driven materials engineering, integrating automated experimentation, machine learning inference, and orchestration software to compress the design-make-test-analyze cycle. These pipelines rely on continuous flows of data, models, and decisions, yet the mechanisms governing the transfer of decision authority between human experts and autonomous agents remain conceptually underdeveloped. Existing infrastructures emphasize optimization and execution but offer limited interpretive frameworks for how authority is dynamically delegated across epistemic states and pipeline stages. This manuscript presents the Decision Authority Delegation Cascade (DADC) Framework, an original systems-level architecture that formalizes delegated experimentation as a structured cascade of authority transfer. The framework delineates layered pipelines—from data representation through model inference and steering logics to execution and feedback—while emphasizing infrastructure trade-offs in representation fidelity, uncertainty quantification, and delegation thresholds. Synthesizing advances in Bayesian active learning, self-driving laboratories, and orchestration platforms, the DADC Framework interprets authority transfer not as a binary handover but as a continuous, computationally steered process that modulates discovery dynamics. The framework offers interpretive insights into scalable computational ecosystems, highlighting pathways to align human epistemic oversight with autonomous operation and to mitigate bottlenecks in closed-loop materials discovery. Its application reframes infrastructure design around explicit delegation logics, with implications for the next generation of autonomous materials platforms.