Large Language Models (LLMs) have emerged as powerful conversational interfaces, and their application in process mining (PM) tasks has shown promising results. However, state-of-the-art LLMs struggle with complex scenarios that demand advanced reasoning capabilities. In the literature, two primary approaches have been proposed for implementing PM using LLMs: providing textual insights based on a textual abstraction of the process mining artifact, and generating code executable on the original artifact. This paper proposes utilizing the AI-Based Agents Workflow (AgWf) paradigm to enhance the effectiveness of PM on LLMs. This approach allows for: i) the decomposition of complex tasks into simpler workflows, and ii) the integration of deterministic tools with the domain knowledge of LLMs. We examine various implementations of AgWf and the types of AI-based tasks involved. Additionally, we discuss the CrewAI implementation framework and present examples related to process mining.

翻译：大型语言模型（LLM）已成为强大的对话式接口，其在过程挖掘（PM）任务中的应用已展现出良好前景。然而，当前最先进的LLM在处理需要高级推理能力的复杂场景时仍面临困难。现有文献提出了两种利用LLM实现过程挖掘的主要方法：基于过程挖掘制品的文本抽象提供文本化洞察，以及在原始制品上生成可执行代码。本文提出利用基于人工智能代理的工作流（AgWf）范式来增强LLM在过程挖掘中的效能。该方法能够：i）将复杂任务分解为更简单的工作流，ii）将确定性工具与LLM的领域知识相结合。我们考察了AgWf的多种实现方式及所涉及的各类基于人工智能的任务。此外，我们探讨了CrewAI实现框架，并展示了与过程挖掘相关的应用实例。