Michael Perscheid

Hoang Vu, Maximilian Körner, Adrian Rebmann et al.

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.

Franziska Dobrigkeit, Christoph Matthies, Ralf Teusner et al.

The most prominent Agile framework Scrum, is often criticized for its amount of meetings. These regular events are essential to the empirical inspect-and-adapt cycle proposed by Agile methods. Scrum meetings face several challenges, such as being perceived as boring, repetitive, or irrelevant, leading to decreased cooperation in teams and less successful projects. In an attempt to address these challenges, Agile practitioners have adopted teamwork, innovation, and design techniques geared towards improving collaboration. Additionally, they have developed their own activities to be used in Scrum meetings, most notably for conducting retrospective and planning events. Design thinking incorporates non-designers and designers in design and conceptualization activities, including user research, ideation, or testing. Accordingly, the design thinking approach provides a process with different phases and accompanying techniques for each step. These design thinking techniques can support shared understanding in teams and can improve collaboration, creativity, and product understanding. For these reasons, design thinking techniques represent a worthwhile addition to the Scrum meeting toolkit and can support Agile meetings in preventing or countering common meeting challenges and achieving meeting goals. This chapter explores how techniques from the design thinking toolkit can support Scrum meetings from a theoretical and practical viewpoint. We analyze Scrum meetings' requirements, goals, and challenges and link them to groups of techniques from the design thinking toolkit. In addition, we review interview and observational data from two previous studies with software development practitioners and derive concrete examples. As a result, we present initial guidelines on integrating design thinking techniques into Scrum meetings to make them more engaging, collaborative, and interactive.

Arian Treffer, Michael Perscheid, Matthias Uflacker

With back-in-time debuggers, developers can explore what happened before observable failures by following infection chains back to their root causes. While there are several such debuggers for object-oriented programming languages, we do not know of any back-in-time capabilities at the database-level. Thus, if failures are caused by SQL scripts or stored procedures, developers have difficulties in understanding their unexpected behavior. In this paper, we present an approach for bringing back-in-time debugging down to the SAP HANA in-memory database. Our TARDISP debugger allows developers to step queries backwards and inspecting the database at previous and arbitrary points in time. With the help of a SQL extension, we can express queries covering a period of execution time within a debugging session and handle large amounts of data with low overhead on performance and memory. The entire approach has been evaluated within a development project at SAP and shows promising results with respect to the gathered developer feedback.