Agentic AI systems that autonomously perform service tasks are entering customer service operations. However, limited evidence exists on how human interventions shape service outcomes when agentic AI failures create both cognitive and emotional consequences. We study this issue through a randomized field experiment on Alibaba's Taobao platform. Workers in the treatment condition supervised an agentic AI system that resolved AI-eligible chats while continuing to handle AI-ineligible chats, whereas control workers resolved all chats without agentic AI. The findings show that AI deployment reduces average chat duration and has limited effects on retrial rates, but substantially lowers ratings for AI-eligible chats. Moreover, human intervention effectiveness in AI-eligible chats depends on the nature of AI failure, post-escalation intervention effort, and intervention timing. Human intervention preserves service quality in algorithm-triggered technical escalations, i.e., unresolved customer issues beyond the AI's capability, but is less effective in algorithm-triggered emotional escalations, i.e., where customers express frustration or dissatisfaction. These differences are partly explained by variation in workers' post-escalation intervention effort across escalation types. In algorithm-triggered emotional escalations, workers showed lower engagement: they sent fewer messages, contributed a smaller share of total chat rounds, and showed less proactivity in information seeking and solution provision. We further find that early intervention is essential for sustaining high post-escalation intervention effort. Finally, we document a positive spillover effect on AI-ineligible chats, as treated workers adapted their multitasking workflow to devote greater attention to these chats. These findings offer implications for human-in-the-loop process design in human-AI collaboration systems.

翻译：自主执行服务任务的行动型人工智能系统正进入客服运营领域。然而，当行动型人工智能故障同时产生认知与情感后果时，人类干预如何影响服务结果的相关证据仍然有限。我们通过在阿里巴巴淘宝平台上进行随机现场实验研究这一问题。实验组工人在监督行动型人工智能系统的同时，需处理人工智能无法解决的对话，而对照组工人则需处理所有对话（无人工智能辅助）。结果显示，人工智能部署降低了平均对话时长，对重试率影响有限，但显著降低了人工智能可处理对话的评分。此外，人类干预在人工智能可处理对话中的有效性取决于人工智能故障类型、升级后干预力度及干预时机。人类干预能维持算法触发技术升级（即人工智能能力范围外的未解决客户问题）的服务质量，但在算法触发情感升级（即客户表达不满或失望）时效果较差。这些差异部分源于工人对升级类型的干预力度不同：在算法触发情感升级中，工人参与度较低（发送消息更少、占对话轮次比例更小、信息获取及解决方案提供的主动性更弱）。我们进一步发现，早期干预对于维持高水平的升级后干预力度至关重要。最后，我们观察到对人工智能不可处理对话的正向溢出效应——实验组工人调整多任务工作流程，将更多注意力分配至这些对话。这些发现对人机协作系统中的人机协同流程设计具有启示意义。