Agent Behavior Mining: Generative AI Agent Governance in Business Processes

Researchers introduce Agent Behavior Mining, a governance framework that applies process mining techniques to make generative AI agent decision-making observable and traceable within business processes. The approach translates agent activities into standardized process logs, enabling organizations to detect policy deviations and quantify operational variability while addressing the control challenges posed by non-deterministic AI systems.

The deployment of generative AI agents in enterprise environments creates a fundamental governance challenge: these systems offer operational flexibility but lack the deterministic behavior that traditional Business Process Management systems require. Agent Behavior Mining addresses this gap by rendering AI decision-making transparent through process mining—a well-established technique for analyzing business workflows. By converting granular agent activities including reasoning traces, tool usage, and token costs into standardized event logs, the framework enables process managers to maintain visibility into AI-driven operations.

This research reflects a broader industry recognition that AI governance cannot rely on black-box trust. As enterprises integrate AI agents into critical business processes like order-to-cash workflows, the ability to audit and validate agent behavior becomes essential for compliance, risk management, and operational control. The study's validation with 18 industry practitioners reveals strong practitioner demand for behavioral transparency and explainability as prerequisites for enterprise AI adoption.

The market implications are significant for organizations investing in process automation and AI. Companies deploying multi-agent systems face hidden operational risks if they cannot observe agent decision-making patterns. This governance framework appeals to enterprises requiring auditability and standardization alongside automation benefits. The approach bridges the gap between AI's flexibility and business process management's control requirements, creating a competitive advantage for organizations that implement such transparency mechanisms.

Looking ahead, Agent Behavior Mining could become a standard requirement in enterprise AI governance frameworks. As regulatory scrutiny around AI increases, the ability to demonstrate observable, traceable AI decision-making will likely transition from competitive differentiator to compliance mandate. Future development may focus on automating policy enforcement based on detected deviations.

• →Agent Behavior Mining translates AI agent activities into standardized process logs for governance and auditability.
• →Industry practitioners identify behavioral transparency and reasoning explainability as critical requirements for enterprise AI trust.
• →The framework enables detection of policy deviations and quantification of operational variability in AI-driven workflows.
• →Process mining techniques can address the control challenges inherent in non-deterministic generative AI systems.
• →Enterprise adoption of multi-agent automation may require governance mechanisms that provide continuous visibility into AI decision-making.