# PMAx: An Agentic Framework for AI-Driven Process Mining

arXiv: https://arxiv.org/abs/2603.15351 · HTML: https://mlsanity.com/paper/2603.15351/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 13 of 13)

- [OpenAI GPT-5 System Card](https://mlsanity.com/paper/2601.03267/) (2025)
- [ExpertPrompting: Instructing Large Language Models to be Distinguished Experts](https://mlsanity.com/paper/2305.14688/) (2023)
- [LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation](https://mlsanity.com/paper/2409.20550/) (2024)
- [LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models](https://mlsanity.com/paper/2303.02927/) (2023)
- [We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs](https://mlsanity.com/paper/2406.10279/) (2024)
- [Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control](https://mlsanity.com/paper/2505.18279/) (2025)
- [DeepAnalyze: Agentic Large Language Models for Autonomous Data Science](https://mlsanity.com/paper/2510.16872/) (2025)
- [Process Modeling With Large Language Models](https://mlsanity.com/paper/2403.07541/) (2024)
- [Agentic Business Process Management: Practitioner Perspectives on Agent Governance in Business Processes](https://mlsanity.com/paper/2504.03693/) (2025)
- [Re-Thinking Process Mining in the AI-Based Agents Era](https://mlsanity.com/paper/2408.07720/) (2024)
- [Leveraging Large Language Models for Enhanced Process Model Comprehension](https://mlsanity.com/paper/2408.08892/) (2024)
- [PM4Py.LLM: a Comprehensive Module for Implementing PM on LLMs](https://mlsanity.com/paper/2404.06035/) (2024)
- [On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2504.21074/) (2025)

## Built on by (most cited first, 1 of 1)

- [From Event Logs to Governed Action: A BlueSky Agenda for Agentic Process Mining](https://mlsanity.com/paper/2609.07984/) (2026)

## Abstract (arXiv)

Process mining provides powerful insights into organizational workflows, but extracting these insights typically requires expertise in specialized query languages and data science tools. Large Language Models (LLMs) offer the potential to democratize process mining by enabling business users to interact with process data through natural language. However, using LLMs as direct analytical engines over raw event logs introduces fundamental challenges: LLMs struggle with deterministic reasoning and may hallucinate metrics, while sending large, sensitive logs to external AI services raises serious data-privacy concerns. To address these limitations, we present PMAx, an autonomous agentic framework that functions as a virtual process analyst. Rather than relying on LLMs to generate process models or compute analytical results, PMAx employs a privacy-preserving multi-agent architecture. An Engineer agent analyzes event-log metadata and autonomously generates local scripts to run established process mining algorithms, compute exact metrics, and produce artifacts such as process models, summary tables, and visualizations. An Analyst agent then interprets these insights and artifacts to compile comprehensive reports. By separating computation from interpretation and executing analysis locally, PMAx ensures mathematical accuracy and data privacy while enabling non-technical users to transform high-level business questions into reliable process insights.