  This paper introduces a novel approach to integrating large language model
(LLM) agents into automated production systems, aimed at enhancing task
automation and flexibility. We organize production operations within a
hierarchical framework based on the automation pyramid. Atomic operation
functionalities are modeled as microservices, which are executed through
interface invocation within a dedicated digital twin system. This allows for a
scalable and flexible foundation for orchestrating production processes. In
this digital twin system, low-level, hardware-specific data is semantically
enriched and made interpretable for LLMs for production planning and control
tasks. Large language model agents are systematically prompted to interpret
these production-specific data and knowledge. Upon receiving a user request or
identifying a triggering event, the LLM agents generate a process plan. This
plan is then decomposed into a series of atomic operations, executed as
microservices within the real-world automation system. We implement this
overall approach on an automated modular production facility at our laboratory,
demonstrating how the LLMs can handle production planning and control tasks
through a concrete case study. This results in an intuitive production facility
with higher levels of task automation and flexibility. Finally, we reveal the
several limitations in realizing the full potential of the large language
models in autonomous systems and point out promising benefits. Demos of this
series of ongoing research series can be accessed at:
https://github.com/YuchenXia/GPT4IndustrialAutomation
