# Agent Miner: An Algorithm for Discovering Agent Systems from Event Data

arXiv: https://arxiv.org/abs/2212.01454 · HTML: https://mlsanity.com/paper/2212.01454/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2022.

## Built on by (most cited first, 9 of 9)

- [Agentic Business Process Management: Practitioner Perspectives on Agent Governance in Business Processes](https://mlsanity.com/paper/2504.03693/) (2025)
- [AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation](https://mlsanity.com/paper/2408.08571/) (2024)
- [Agentic Business Process Management: A Research Manifesto](https://mlsanity.com/paper/2603.18916/) (2026)
- [Formal Foundations of Agentic Business Process Management](https://mlsanity.com/paper/2604.17347/) (2026)
- [Organizational Memory for Agentic Business Process Execution](https://mlsanity.com/paper/2607.03228/) (2026)
- [Working My Way Back to You: Resource-Centric Next-Activity Prediction](https://mlsanity.com/paper/2508.19016/) (2025)
- [How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestamps](https://mlsanity.com/paper/2509.14536/) (2025)
- [Agent Behavior Mining: Generative AI Agent Governance in Business Processes](https://mlsanity.com/paper/2606.20669/) (2026)
- [Design and Implementation of Agentic Orchestrations and Orchestration of Agents](https://mlsanity.com/paper/2606.31518/) (2026)

## Abstract (arXiv)

Process discovery studies ways to use event data generated by business processes and recorded by IT systems to construct models that describe the processes. Existing discovery algorithms are predominantly concerned with constructing process models that represent the control flow of the processes. Agent system mining argues that business processes often emerge from interactions of autonomous agents and uses event data to construct models of the agents and their interactions. This paper presents and evaluates Agent Miner, an algorithm for discovering models of agents and their interactions from event data composing the system that has executed the processes which generated the input data. The conducted evaluation using our open-source implementation of Agent Miner and publicly available industrial datasets confirms that our algorithm can provide insights into the process participants and their interaction patterns and often discovers models that describe the business processes more faithfully than process models discovered using conventional process discovery algorithms.