A modular large language model agent architecture for adaptive and autonomous process-aware execution

Business Process Management (BPM) is evolving rapidly. Building on a rich research portfolio that spans from early workflow automation to robotic process automation (RPA), large language models (LLMs) are the most recent disruptive technologies to influence all BPM capability areas. Particularly, these technologies fuel the emerging research fields of AI-enhanced business process management systems and autonomous process execution. In a design science research approach, we develop a modular LLM agent architecture that effectively enables the adaptation and execution of business processes. The architecture integrates a Frame Agent that can generate process descriptions and an Operational Agent that autonomously executes processes based on the Frame Agent’s process descriptions. It further enables a future Tactical Agent for autonomous process adaptations. We demonstrate the architecture’s applicability and utility with the real-world case of a meter-to-cash process, comparing it to the performance of an RPA bot. Our findings provide early insights into the challenges faced by autonomous process execution and its relation with RPA in terms of adaptability, flexibility, and complexity in autonomous process execution. While researchers can build on our findings to establish modular LLM-agent architectures for autonomous process execution, practitioners can derive early insights into the design of LLM agents for process automation. • Properties and perspectives of AI-enhanced business process management systems. • Modular LLM agent-based system design with process frame for adaptive and autonomous process-aware execution. • Evaluation based on real-world meter-to-cash process data.