# Agent Behavior Mining: Generative AI Agent Governance in Business Processes

arXiv: https://arxiv.org/abs/2606.20669 · HTML: https://mlsanity.com/paper/2606.20669/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [AgentOps: Enabling Observability of LLM Agents](https://mlsanity.com/paper/2411.05285/) (2024)
- [AI-Augmented Business Process Management Systems: A Research Manifesto](https://mlsanity.com/paper/2201.12855/) (2022)
- [Beyond Black-Box Benchmarking: Observability, Analytics, and Optimization of Agentic Systems](https://mlsanity.com/paper/2503.06745/) (2025)
- [Agent Miner: An Algorithm for Discovering Agent Systems from Event Data](https://mlsanity.com/paper/2212.01454/) (2022)
- [Towards a Simple and Extensible Standard for Object-Centric Event Data (OCED) -- Core Model, Design Space, and Lessons Learned](https://mlsanity.com/paper/2410.14495/) (2024)

## Abstract (arXiv)

As organizations increasingly deploy generative AI agents to automate business processes, they face a governance dilemma: although these agents can increase operational flexibility, their non-deterministic nature challenges the control and standardization that Business Process Management seeks to enforce. This paper addresses this invisible autonomy risk by introducing Agent Behavior Mining, a governance capability that enables the application of process mining techniques to render generative AI agent decision-making observable and traceable. We (1) improve the understanding of generative AI agent behavior through an event data model that translates granular agent activities -- including reasoning traces, tool usage, and token costs -- into standardized process logs; (2) instantiate the data model in a multi-agent order-to-cash implementation, demonstrating how process managers can leverage agent logs to detect policy deviations and quantify operational variability; and (3) evaluate the perceived practical utility of the approach in an exploratory study with 18 industry practitioners. The results indicate that practitioners view behavioral transparency as a prerequisite for trust and consider the ability to examine agent reasoning as an important governance requirement for the next generation of AI-driven business processes.