AI Agents

Designing, Orchestrating, and Governing LLM-Based Systems
Erscheinungsjahr 2027
ISBN: 978-3-032-46871-0
Verlag: Springer

Buch, Englisch, 461 Seiten, Format (B × H): 155 mm x 235 mm

This book covers the design, operation, and governance of AI agents built on Large Language Models. It works through the architecture of an agent, the model at its core, the tools it calls, the memory it keeps, and the flow that coordinates them, and then turns to the organizational layer: how agents are integrated into existing systems, what they cost, which business processes suit them, and what policies they require. A third part treats operations, covering the orchestration of several agents, runtime governance, maintenance over time, and security against attacks that target the model itself.

The treatment is organized around one framework that connects model behavior to architectural components, orchestration logic, governance mechanisms, and organizational context. Executable Python examples anchor every mechanism and are written to be vendor-neutral, so they outlast the tooling they demonstrate.

The book grew out of research, teaching, and applied projects at the Institute for Process Management and Digital Transformation at FH Münster. A recurring pattern there shaped it: obtaining useful results from an AI agent is easy, while designing a robust, auditable, and maintainable system around one is not.

Zielgruppe

Research

Autoren/Hrsg.

Bücker, Michael

Hewing, Michael

Fachgebiete

• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Technik Allgemein Modellierung & Simulation

Inhaltsverzeichnis

Part I Design and Architecture.- AI models and LLMs.- Tools.- Memory.- Agent flow.- Part II Organization and Economics.- Integration.- Strategy.- Economics.- Organizational policies.- Business processes.- Part III Operations.- Orchestration.- Runtime governance.- Build and maintenance.- Security.

Über Autor(innen)

Michael Bücker is Professor of Data Science, Mathematics, and Information Systems at the Münster School of Business (MSB) at FH Münster, where he also serves as Study Programme Director for Digital Business and Innovation Management and as a board member of the Institute for Process Management and Digital Transformation. His research and teaching focus on applied data science, machine learning, explainable AI, and statistical methods that bridge theory and organizational practice. Before entering academia, Michael worked as a consultant and project manager with McKinsey & Company, where he developed data-driven solutions and advised clients on complex analytics and transformation initiatives. This blend of strategic consulting and academic scholarship informs his work on AI adoption, governance, and digital innovation.

Michael Hewing is Professor of Information Systems and Digital Technologies at the Münster School of Business (MSB) at FH Münster and board member of the Institute for Process Management and Digital Transformation. He researches and teaches on topics including information system management, digital transformation, business process management, and the integration of advanced technologies into organizational contexts. Prior to his current academic role, Michael gained extensive industry experience as a Senior Managing Consultant at IBM, where he led digitalization and IT transformation projects for public-sector and enterprise clients, blending technological insight with strategic execution. His work spans both academic research, including contributions to business process and digital innovation literature, and applied consulting practice, giving him a deep understanding of how information systems shape modern business models and operational design.