# From Event Logs to Governed Action: A BlueSky Agenda for Agentic Process Mining

arXiv: https://arxiv.org/abs/2609.07984 · HTML: https://mlsanity.com/paper/2609.07984/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 26 references that are in the map.
- First posted on arXiv 7 Sep 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, 21 of 21)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Consistent Estimators for Learning to Defer to an Expert](https://mlsanity.com/paper/2006.01862/) (2020)
- [Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science](https://mlsanity.com/paper/1905.06169/) (2019)
- [Discovering Object-Centric Petri Nets](https://mlsanity.com/paper/2010.02047/) (2020)
- [When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints](https://mlsanity.com/paper/2206.07745/) (2022)
- [Leveraging Large Language Models (LLMs) for Process Mining (Technical Report)](https://mlsanity.com/paper/2307.12701/) (2023)
- [Agentic Business Process Management: A Research Manifesto](https://mlsanity.com/paper/2603.18916/) (2026)
- [OCEL (Object-Centric Event Log) 2.0 Specification](https://mlsanity.com/paper/2403.01975/) (2024)
- [ExoTST: Exogenous-Aware Temporal Sequence Transformer for Time Series Prediction](https://mlsanity.com/paper/2410.12184/) (2024)
- [Causal Process Mining from Relational Databases with Domain Knowledge](https://mlsanity.com/paper/2202.08314/) (2022)
- [Domain Adaptation of LLMs for Process Data](https://mlsanity.com/paper/2509.03161/) (2025)
- [A Universal Metric of Dataset Similarity for Cross-silo Federated Learning](https://mlsanity.com/paper/2404.18773/) (2024)
- [SimBank: from Simulation to Solution in Prescriptive Process Monitoring](https://mlsanity.com/paper/2506.14772/) (2025)
- [Agentic Business Process Management Systems](https://mlsanity.com/paper/2601.18833/) (2026)
- [PM4Py.LLM: a Comprehensive Module for Implementing PM on LLMs](https://mlsanity.com/paper/2404.06035/) (2024)
- [Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives](https://mlsanity.com/paper/2407.11280/) (2024)
- [ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods](https://mlsanity.com/paper/2509.00797/) (2025)
- [Formal Foundations of Agentic Business Process Management](https://mlsanity.com/paper/2604.17347/) (2026)
- [Digital Twins of Business Processes: A Research Manifesto](https://mlsanity.com/paper/2410.08219/) (2024)
- [PMAx: An Agentic Framework for AI-Driven Process Mining](https://mlsanity.com/paper/2603.15351/) (2026)
- [DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining](https://mlsanity.com/paper/2606.14283/) (2026)

## Abstract (arXiv)

Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime predictions. Agentic AI changes the target. Process-aware agents will not only ask what happened. They will ask whether a proposed action should be taken, given the available evidence, privacy budget, organizational authority, and downstream risk. This BlueSky paper proposes event-to-action process mining: a process-mining agenda for transforming heterogeneous operational event data into governed action. The goal is not another dashboard, a generic enterprise simulator, or a language interface over logs. We argue that the community needs four mineable artifacts: event-object representations, action evidence packages, governance contracts, and benchmarks where act, defer, ask, and refuse are all valid outputs. This agenda is timely because agentic business process management (BPM), LLM-assisted process mining, object-centric event standards, causal process monitoring, and privacy-preserving learning are maturing separately. Bringing them together defines a data-mining target inside process mining: mining logged organizational behavior for accountable action, not only retrospective insight.