The primary advantage of this architectural approach is its adaptability, which enables the automation and the communication between a wide range of scientific equipment in a structured manner. This encompasses synthesis stations, sampling tools, analytical equipment, laboratory robotics, and all connecting robotics. Each layer is dedicated to a specific level of operational control. The system can be designed to control a specific group of tasks, such as all transfer robots, or it can be optimized to work with automation controllers, such as those produced by Beckhoff, Siemens, or Arduinos. Alternatively, it can be employed to integrate external vendor scientific equipment or a group of scientific equipment with its own controlling software, open-source external controllers such as Chemspyd 39 and proprietary sample schedulers (e.g., Agilent Sample Scheduler from Open Lab CDS). A second advantage is that the Main Laboratory Scheduler is an independent layer in the GLAS architecture, allowing for adaptation to any laboratory requirement or habit. Scheduling can be executed in a linear manner, adhering to the First-In First-Out (FIFO) principle. Alternatively, it can be more complex, with optimized time allocation of instruments to minimize the overall time required to complete experimental procedures. In this context, the GLAS architecture is inherently parallel, as any groups of instruments can be controlled independently and can therefore be used to extend FIFO schedulers to a certain degree of parallelization. To illustrate this possibility, we present the use of a REST API-based scheduler as a main laboratory in our Swiss Cat+ laboratory. However, we emphasize that this is merely one potential option and that the selection of the Main Laboratory Scheduler, despite being pivotal in terms of scheduling performance, is independent of the use of a GLAS architecture. A third advantage of this architectural approach is its systematicity, which is enabled by the container approach. Every set of functions and workflows, can be tailored to a specific equipment or groups of equipment, is encoded into a conservative architecture and can be called upon by the upper scheduling layer with higher-level commands. Finally, the system's Git-based architecture allows for rapid deployment in a new laboratory setting, with the capacity to encompass the entire laboratory and to incorporate new groups of equipment and functions with minimal effort. This is achieved by only developing the newly needed layer and referencing it in the upper one.