发表机构
KTH Royal Institute of Technology; Department of Production Engineering（瑞典皇家理工学院; 生产工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对ERP系统过度依赖人类决策的问题，提出Agentic ERP架构，结合角色对齐的LLM智能体、人工参与框架和图编排器。通过特定决策问题表述、编排解耦及多层面评估，证明该方法优于基线，能让ERP主动执行决策，提供了参考架构与评估协议。
AI 中文摘要
企业资源规划（ERP）系统能可靠记录交易，但几乎将所有运营决策都交给人类专家。因为传统基于规则的自动化无法处理异常，整体式人工智能助手在跨功能边界协调时会退化。本文提出智能企业资源规划（Agentic ERP），一种专家系统架构，它将角色对齐的大语言模型（LLM）智能体与风险分层的人工参与框架以及基于图的编排器相结合，在生产ERP后端执行端到端业务工作流程。首先将自主ERP操作表述为结构化企业状态上的约束顺序决策问题，通过分解论证将角色对齐的智能体与每步工具选择复杂性的可测量降低联系起来。其次，基于图的规划器 - 执行器 - 反射器 - 响应器编排通过外部分级标准和冲刺合同将生成与评估解耦，将近期框架工程原则打包为可检查的专家系统工件。第三，在三个层面评估该系统：基于场景的任务套件、跨功能危机任务上六种编排范式的全面比较，以及针对基于规则的RPA和无干预基线的365天人工参与模拟。结果表明该多智能体方法明显优于基线，系统在模拟一年运营中零缺货，而基于规则的基线在相同需求流下积累数百缺货。这项工作表明在人工监督下角色对齐的LLM智能体可使ERP系统从被动记录交易转变为主动执行运营决策，并为自主企业资源规划提供了参考架构和评估协议。
英文摘要
Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries. This paper presents Agentic ERP, an expert-system architecture that combines role-aligned large-language-model (LLM) agents with a risk-tiered human-in-the-loop harness and a graph-based orchestrator to execute end-to-end business workflows on a production ERP backend. First, autonomous ERP operation is formulated as a constrained sequential-decision problem over a structured enterprise state, with a decomposition argument linking role-aligned agents to a measurable reduction in per-step tool-selection complexity. Second, a graph-based Planner--Executor--Reflector--Responder orchestration decouples generation from evaluation through externalised grading criteria and sprint contracts, packaging recent harness-engineering principles as inspectable expert-system artefacts. Third, the system is evaluated at three levels: a scenario-based task suite, a comprehensive comparison of six orchestration paradigms on cross-functional crisis tasks, and a 365-day agent-in-the-loop simulation against rule-based RPA and no-intervention baselines. Across these levels the proposed multi-agent method is significantly better than the baseline, and the system sustains a simulated year of operation with zero stockouts while the rule-based baseline accumulates hundreds under the same demand stream. The work shows that role-aligned LLM agents under human oversight can move an ERP system from passively recording transactions to actively executing operational decisions, and it provides a reference architecture and an evaluation protocol for autonomous enterprise resource planning.
