Combining accelerated autonomous discovery capability with a device level perspective has very recently been introduced for opto-electronic systems , but has been out of reach for electrochemical system discovery. Electrochemical materials discovery and development is a complex, multi-pronged process spanning prediction, synthesis, characterisation, and device testing processes. Related critical research tasks often hinge on the access to bespoke equipment, e.g., AI-supercomputers for generative design of new materials using foundation models , or high-fidelity synthesis and characterisation equipment in large-scale facilities . Such resources often have limited and delayed access that requires peer-reviewed applications for compute resources or beamtime. Com-bining such infrastructure within a MAP requires a novel direction in MAP architecture in terms of distributed operation and decentralisation by design. Surpassing pure acceleration and establishing reliability of discovery , such a MAP will encourage the thorough characterisation and testing of full systems rather than individual materials or components. This also provides dual benefits of higher throughput and better reproducibility by removing human error and providing improved automated documentation of experiments including negative results. The practice of publishing complete datasets from SDL operation alongside the adoption of standardised machine readable data formats, holds the promise of boosting data reusability and, consequently, further resource efficiency. By leveraging carefully designed autonomous experiments in conjunction with experiment planning algorithms, it is possible not only to reduce material usage but also enhance efficiency by minimising downtime of the instruments.