The multi-Orchestrator demo described above additionally illustrates optionality with respect to the implementation of AIguided design of experiments. If the AI agent is intended to be a Science Manager across multiple workflows, it should be implemented as an Operator in HELAO-async. However, in the demo, the active learning engine is implemented as an Action Server that is a shared resource for the 2 Orchestrators. In this case the Science Manager is a human who configured each Orchestrator to receive guidance from the active learning Action Server, which is a prudent mode of operation when the human will routinely switch an Orchestrator from executing experiments according to AI vs human guidance. As such, this demo may be prescient in the context of instrument control using large language models that integrate human and AI design of experiments. While selfdriving labs have traditionally been constructed with the AI acquisition function as the Operator, the future of MAPs will likely relegate active learning to be a resource that facilitates but not governs operation of automated experimental workflows. The asynchronous programming and implementation of workflows as networked servers in HELAO-async are designed to enable development of individual automated workflows followed by their seamless interconnection with additional workflows that can be managed by any combination of human and artificial intelligence.