AI systems increasingly delegate decisions to specialized models, evaluators, tools, and supervisory controllers. The central AI problem is no longer only model accuracy, but uncertainty-aware governance: how much autonomy to grant, which evidence should calibrate trust, what performance ceiling a delegated AI system can sustain, and when human intervention becomes necessary. We propose the Minimum Sufficient Oversight Principle (MSO), a variational principle for principled autonomy delegation: minimize governance burden on the Fisher information manifold subject to a delivery constraint. The resulting Euler-Lagrange solution yields a water-filling allocation of governed delegation across the task space. Building on a revealed-action governed delegation channel model, we prove a capacity theorem for stationary symbolwise review policies, derive a local first-order approximation relating workflow complexity to quality degradation, and give a drift-dominated autonomy-time scaling law linking intervention timing to effective capacity, complexity, and drift. Within this framework, masking appears as a structural AI-governance pathology: corrected performance can hide the competence signal needed to calibrate trust. Synthetic simulations and a semi-real reconstructed workflow support design prescriptions including upstream-first correction, sensitivity-based intervention, and explicit feasibility checks before autonomy is expanded. The result is a computable framework for uncertainty, planning, and oversight in delegated AI systems. A companion Python package is available at https://github.com/crbazevedo/delegation-lab.

翻译：人工智能系统日益将决策委托给专门模型、评估器、工具和监督控制器。核心AI问题已不再仅是模型精度，而是不确定性感知治理：应授予多少自主权、哪些证据可用于校准信任、委托式AI系统可维持的性能上限，以及何时需要人类干预。我们提出最小充分监督原则（MSO），这是一种用于原则性自主委托的变分原理：在满足交付约束条件下，最小化Fisher信息流形上的治理负担。由此得到的欧拉-拉格朗日解给出了任务空间上的注水式委托治理分配方案。基于显性行为委托治理信道模型，我们证明了平稳符号级审查策略的容量定理，推导出关联工作流复杂度与质量退化的一阶局部近似关系，并建立了漂移主导的自主权-时间标度律，将干预时机与有效容量、复杂度和漂移联系起来。在该框架内，遮掩表现为一种结构性AI治理病态：修正后的性能可能掩盖校准信任所需的能力信号。综合仿真与半真实重构工作流支持如下设计准则：优先上游修正、基于灵敏度的干预，以及在扩展自主权前进行显式可行性检验。研究结果为委托式AI系统中的不确定性、规划与监督提供了可计算框架。配套Python工具包发布于https://github.com/crbazevedo/delegation-lab。