Orchestrating generative AI-based multi-agent systems for complex knowledge work automation: A design science research approach

The pursuit of automation has been a key objective throughout industrial history. Advancements in information technology, such as robotic process automation, accelerated this progress for knowledge work. However, complex knowledge work was off-limits to automation. The emergence of generative artificial intelligence (GenAI) coupled with multi-agent systems (MAS) pushes the boundaries of what is technically feasible and economically viable. While several technical frameworks for developing GenAI-based MAS are available, the systems’ orchestration remains largely based on ad-hoc trial and error. The paper addresses this gap by developing design knowledge for GenAI-based MAS. Based on design science research, we present a morphological box of orchestration options and derive four propositions regarding GenAIbased MAS orchestration. Our research contributes to academic and practical understanding by offering design knowledge for GenAI-based MAS development, facilitating the automation of complex knowledge work