用于智能业务流程执行的组织记忆
Organizational Memory for Agentic Business Process Execution
AI总结：
研究LLM智能体执行商业流程面临的问题，提出需组织记忆，推导其要求，给出构建与使用架构，并在采购场景中验证其有效性。
摘要：
基于LLM的智能体为自动化业务流程执行带来新机遇，但通用LLM缺乏可靠执行所需的组织特定知识。这种知识分散在政策、流程模型等中，现有编码方法不适用企业。本文提出用于智能业务流程执行的组织记忆，推导其要求，给出架构并在采购场景中证明其有效性。
英文摘要：
LLM-based agents offer new opportunities for automating business process execution beyond the limits of rule-based systems. However, general-purpose LLMs lack the organization-specific knowledge required for reliable execution, which is typically fragmented across human-oriented artifacts such as policies, process models, and standard operating procedures. While such knowledge can technically be encoded in individual prompts or agent-specific retrieval setups, this approach does not scale in enterprises, as it gives rise to knowledge silos and rule duplicates, and makes consistent updates and learning across agents difficult. We argue that this calls for an organizational memory for agentic business process execution: a shared, governed, and agent-consumable reference layer of evolving organization-specific procedural knowledge about how work should be executed. We derive requirements for such a memory, propose an architecture for its curation and consumption, and demonstrate its effectiveness in a proof-of-concept based on a procurement scenario.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
