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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.

The rapid evolution of computational and data-driven materials engineering has ushered in autonomous discovery systems that integrate machine learning, high-throughput simulations, and robotic experimentation to accelerate materials innovation. Central to these systems are decision authority frameworks, which define how authority is delegated between human operators and artificial intelligence agents, ensuring safe, ethical, and efficient operations. This review synthesizes recent literature on delegation models, human override mechanisms, responsibility assignment, and policy encoding within materials informatics ecosystems. We examine how these frameworks operate in closed-loop discovery pipelines, where active learning and uncertainty quantification guide iterative experimentation. Key areas include representation learning via graph neural networks for materials property prediction, multimodal dataset integration for simulation-experiment synergy, and inverse design strategies that balance exploration and exploitation. By analyzing delegation in autonomous laboratories, we highlight the role of human-in-the-loop paradigms in mitigating risks such as algorithmic bias or experimental failures. The review underscores the need for robust policy encodings that embed ethical constraints and regulatory compliance into AI-driven workflows. Drawing from high-impact studies, we provide an integrative perspective on how these frameworks enhance reliability in materials discovery, paving the way for scalable, trustworthy autonomous systems in computational materials science.