让生成式AI在业务流程中的行为可追踪，解决其不可控风险。
Agent Behavior Mining: Generative AI Agent Governance in Business Processes
- 构建事件数据模型，将AI的推理、工具使用等转化为标准日志。
- 在订单到收款场景中验证，可发现政策偏差并量化操作差异。
- 从业者认为透明推理是建立信任的关键，适合流程治理者参考。
随着组织越来越多地部署生成式AI代理来自动化业务流程，面临治理困境：尽管这些代理能提升运营灵活性，但其非确定性特性挑战了业务流程管理所追求的控制与标准化。本文通过引入「代理行为挖掘」这一治理能力，使生成式AI代理的决策过程变得可观测和可追溯。我们（1）通过事件数据模型，将代理的细粒度活动——包括推理轨迹、工具使用和令牌成本——转化为标准化流程日志，以增进对生成式AI代理行为的理解；（2）在多代理订单到收款系统中实现该数据模型，展示流程管理者如何利用代理日志检测策略偏离并量化操作变异性；（3）通过对18位行业实践者的探索性研究评估该方法的实用性。结果表明，从业者视行为透明为信任前提，并认为审查代理推理能力是下一代AI驱动业务流程的重要治理需求。
原文摘要 · Abstract (English)
As organizations increasingly deploy generative AI agents to automate business processes, they face a governance dilemma: although these agents can increase operational flexibility, their non-deterministic nature challenges the control and standardization that Business Process Management seeks to enforce. This paper addresses this \emph{invisible autonomy risk} by introducing \emph{Agent Behavior Mining}, a governance capability that enables the application of process mining techniques to render generative AI agent decision-making observable and traceable. We (1) improve the understanding of generative AI agent behavior through an event data model that translates granular agent activities -- including reasoning traces, tool usage, and token costs -- into standardized process logs; (2) instantiate the data model in a multi-agent order-to-cash implementation, demonstrating how process managers can leverage agent logs to detect policy deviations and quantify operational variability; and (3) evaluate the perceived practical utility of the approach in an exploratory study with 18 industry practitioners. The results indicate that practitioners view behavioral transparency as a prerequisite for trust and consider the ability to examine agent reasoning as an important governance requirement for the next generation of AI-driven business processes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。