The Anthropocentric Fallacy in Agentic AI Governance

The Anthropocentric Fallacy in Agentic AI Governance Why Human-in-the-Loop Control Fails at Machine Speed Abstract As artificial intelligence systems transition from assistive generation to autonomous, agentic execution, governance architectures inherited from human-centric workflows increasingly impose structural limitations. This paper examines the limitations of Human-in-the-Loop (HITL) governance in high-velocity agentic systems, arguing that biological decision latency and cognitive variance introduce measurable performance degradation and systemic inconsistency. Drawing on findings from cognitive science, systems theory, and ensemble machine learning, we demonstrate that synchronous human oversight is poorly matched to millisecond-scale execution environments. We introduce a machine-speed governance framework, referred to as the Digital Senate, which replaces transactional human gating with adversarial multi-agent consensus operating under deterministic policy constraints. Human oversight is repositioned to an asynchronous audit role—Human-on-the-Rail (HOTR)—preserving accountability without constraining execution velocity. This architecture reframes AI governance as a systems engineering problem rather than a supervisory one, offering improved scalability, consistency, and auditability in autonomous decision pipelines.《智能体AI治理中的人类中心谬误——为何人在回路控制无法适配机器级速度》 摘要 随着人工智能系统从辅助生成向自主、智能体执行转型，源自人类中心工作流的治理架构正日益凸显结构性局限。本文聚焦高速智能体系统中人在回路（Human-in-the-Loop, HITL）治理的局限，论证指出生物性决策延迟与认知差异会引发可量化的性能衰减与系统性不一致。 依托认知科学、系统论与集成机器学习领域的研究成果，本文证明同步式人类监督与毫秒级执行环境存在严重适配性缺陷。为此我们提出一种适配机器速度的治理框架——数字参议院（Digital Senate），该框架以确定性政策约束下的对抗式多智能体共识替代事务性的人类准入控制。人类监督被重新定位为异步审核角色——轨道中人（Human-on-the-Rail, HOTR），在不限制执行速度的前提下保留问责机制。 该架构将AI治理重新定义为系统工程问题而非监督管理问题，可在自主决策流水线中实现更优异的可扩展性、一致性与可审计性。