To address the aforementioned challenges, we discuss here the versatile and ontologylinked Fast INtention-Agnostic LEarning Server (FINALES) framework capable of providing Application Programming Interface (API)-access to a distributed MAP enabling fully autonomous operation. The use of clearly defined data structures permits a unified and unambiguous communication without the need for human intervention. Since the framework passively enables the communication without actively triggering actions within the MAP, new clients (tenants) may be added without necessitating alterations in the internal processes and workflows. This agile construct leads us to expect facile scalability of MAPs based on the FINALES framework. Integrating patterns and automated analysis frameworks like the Modular and Autonomous Data Analysis Platform (MADAP) empowers even non-expert users to deploy the capabilities offered in the MAP. The link to the BattINFO ontology enables the datasets to be found by semantic searches while the connection to the BIG-MAP Archive ensures a timely dissemination of the generated data using similar technology as the Materials Cloud Archive .