Design Cycle Methodology for Developing AI Microservices Frameworks: A Case Study

Abstract

The integration of Artificial Intelligence (AI) and microservices is increasingly recognised as a pathway to building scalable, reusable intelligent systems. Yet much of the existing work remains implementation-driven, with limited methodological grounding, which restricts reproducibility and generalisability. This paper presents a case study applying the Design Cycle Methodology (DCM) to the systematic development of an ontology driven AI microservice framework, the AI Microservice Agent. The study addressed four research questions: whether semantic registration improves service discovery efficiency, how it supports scalability under load, what computational trade-offs are introduced, and how well the approach generalises across domains. A proof of-concept text classification microservice was semantically described using OWL service descriptors and retrieved via SPARQL queries, illustrating the operational role of semantic registration. Comparative experiments against a monolithic system demonstrated a reduction of up to 35% in discovery latency, stable throughput under increasing client requests, and robustness under failure conditions with only minor reasoning overhead. Cross-domain validation with text and image services achieved 100% successful integration, confirming generalisability. To our knowledge, this is the first study to embed AI microservice development within DCM, providing methodological traceability between objectives, design stages, and empirical findings. By releasing ontology files, service descriptors, and containerisation artefacts, the work contributes a reproducible framework that advances discovery efficiency, scalability, and adaptability in AI microservices research.