arXiv subjects

Gregor Berg

Publications and source records attributed to Gregor Berg.

3 recordsLinked to original sources

arXiv2026

Organizational Memory for Agentic Business Process Execution

LLM-based agents offer new opportunities for automating business process execution beyond the limits of rule-based systems. However, general-purpose LLMs lack the organization-specific knowledge required for reliable execution, which is typically fragmented across human-oriented artifacts such as policies, process models, and standard operating procedures. While such knowledge can technically be encoded in individual prompts or agent-specific retrieval setups, this approach does not scale in enterprises, as it gives rise to knowledge silos and rule duplicates, and makes consistent updates and learning across agents difficult. We argue that this calls for an organizational memory for agentic business process execution: a shared, governed, and agent-consumable reference layer of evolving organization-specific procedural knowledge about how work should be executed. We derive requirements for such a memory, propose an architecture for its curation and consumption, and demonstrate its effectiveness in a proof-of-concept based on a procurement scenario.

cs.AI↗

arXiv2026

Agent Behavior Mining: Generative AI Agent Governance in Business Processes

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 \emph{invisible autonomy risk} by introducing \emph{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.

cs.AI↗

arXiv2025

Enriching Object-Centric Event Data with Process Scopes: A Framework for Aggregation and Analysis

Object-Centric Process Mining enables the analysis of complex operational behavior by capturing interactions among multiple business objects (e.g., orders, items, deliveries). These interactions are recorded using Object-Centric Event Data (OCED) formats, such as the Object-Centric Event Log (OCEL). However, existing formats lack explicit definitions of process scopes, which restricts analysis to individual processes and limits insights to a low level of granularity. In practice, OCED often spans multiple interrelated processes, as shared objects connect events across organizational functions. This structure reflects how value is created along the organizational value chain, but introduces challenges for interpretation when process boundaries are not clearly defined. Moreover, process definitions are typically subjective and context-dependent; they vary across organizations, roles, and analytical goals, and cannot always be discovered automatically. To address these challenges, we propose a method for embedding analyst-defined process scopes into OCEL. This enables the structured representation of multiple coexisting processes, supports the aggregation of event data across scopes, and facilitates analysis at varying levels of abstraction. We demonstrate the applicability of our approach using a publicly available OCEL log and provide supporting tools for scope definition and analysis.

cs.DB↗