责任真空：规模化智能体系统中的组织失灵

The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems

January 21, 2026

作者: Oleg Romanchuk, Roman Bondar

cs.AI

摘要

现代集成智能体生成代码的CI/CD管道存在责任归属的结构性失效。决策通过形式合规的审批流程执行，却没有任何实体同时具备批准这些决策的权限与理解其决策依据的认知能力。 我们将这种状态定义为"责任真空"：决策持续产生，但由于审批权限与验证能力相互分离，导致责任无法有效归属。研究表明，这并非流程偏差或技术缺陷，而是当决策生成吞吐量超过人类有限验证能力时，部署系统固有的结构性特征。 通过分析标准部署假设（包括并行智能体生成、基于CI的验证机制和个性化人工审批环节），我们识别出一个临界吞吐量阈值。超越该阈值后，验证机制不再发挥决策筛选功能，转而依赖代理信号形成仪式化审批。在此机制下，个性化责任归属在结构上已无法实现。 研究进一步揭示了CI放大效应动态：自动化验证覆盖率的提升虽然增加了代理信号密度，却未增强人类认知能力。在固定的时间与注意力约束下，这加速了广义上的认知卸载，扩大了形式审批与认知理解之间的鸿沟。因此，额外自动化非但不能缓解责任真空，反而会加剧这一现象。 我们得出结论：除非组织重新设计决策边界，或将责任从个体决策转向批次/系统级归属，否则责任真空将持续成为规模化智能体部署中隐形却顽固的失效模式。

English

Modern CI/CD pipelines integrating agent-generated code exhibit a structural failure in responsibility attribution. Decisions are executed through formally correct approval processes, yet no entity possesses both the authority to approve those decisions and the epistemic capacity to meaningfully understand their basis. We define this condition as responsibility vacuum: a state in which decisions occur, but responsibility cannot be attributed because authority and verification capacity do not coincide. We show that this is not a process deviation or technical defect, but a structural property of deployments where decision generation throughput exceeds bounded human verification capacity. We identify a scaling limit under standard deployment assumptions, including parallel agent generation, CI-based validation, and individualized human approval gates. Beyond a throughput threshold, verification ceases to function as a decision criterion and is replaced by ritualized approval based on proxy signals. Personalized responsibility becomes structurally unattainable in this regime. We further characterize a CI amplification dynamic, whereby increasing automated validation coverage raises proxy signal density without restoring human capacity. Under fixed time and attention constraints, this accelerates cognitive offloading in the broad sense and widens the gap between formal approval and epistemic understanding. Additional automation therefore amplifies, rather than mitigates, the responsibility vacuum. We conclude that unless organizations explicitly redesign decision boundaries or reassign responsibility away from individual decisions toward batch- or system-level ownership, responsibility vacuum remains an invisible but persistent failure mode in scaled agent deployments.