The defining feature here is that there can be multiple copies of every tenant without racing conditions. Building shared MAPs, where instruments no longer belong to a single group, allow the utilization of research instruments for a variety of research questions and fields. This is not just important for ensuring fault tolerance and around-the-clock operation, but it also creates incentives for cost reduction. We also believe that the intention-agnostic sharing of research instrumentation will allow for significantly better utilization of instruments and allocation of institutional funding. By the collaborative nature of MAPs as described herein, community building is an integral part of the design process. The current funding schemes in the European Union (EU) and associated national or bilateral funding agencies in fact foster this collaborative approach to research, and we believe that there is the need to create more cross-country funding lines that can help in building global research workflows. With these design considerations in mind, we will describe the technical layout of our MAP, its tenants, results, and lessons learned in the following sections. A defining design decision is to disable direct communication between tenants, and to channel all communication through a brokering service we refer to as the fast intention agnostic learning server, or FINALES in short. FINALES is our take on interoperability and constitutes an open interface brokering between hard-, and software as well as humans. All simulations and experiments can be requested as a service through the FINALES APIs that enables abstraction with standardization in data, communication, and information retrieval through being fully ontology linked. Interoperability to FINALES is moved to a translation layer developed by the respective tenants such that no existing workflow needs to be altered upon addition of a new agent. To the best of our knowledge this is the first multi-tenancy, multi-modal and international demonstration of a MAP.