Abstract The evolution of enterprise software systems has entered a new phase, driven by the convergence of microservices architectures, cloud-native technologies, and artificial intelligence. While the transition from monolithic systems to microservices has enabled greater scalability, flexibility, and deployment agility, it has also introduced new complexities in system design and operation. More recently, the integration of artificial intelligence—particularly machine learning and Large Language Models—has begun to reshape the role of software systems, transforming them from deterministic execution environments into adaptive, context-aware platforms. This paper explores the architectural transformation from traditional monolithic systems to intelligent microservices ecosystems, emphasizing the engineering patterns required to integrate AI capabilities within cloud-native infrastructures. It examines how microservices architectures, supported by containerization and orchestration platforms, provide a modular and scalable foundation for embedding intelligent components into enterprise systems. Particular focus is given to the role of AI as an intrinsic system layer, enabling real-time decision-making, automation, and enhanced user interaction. The study further investigates key architectural patterns for AI augmentation, including service-based AI integration, embedded intelligence within domain services, and orchestration mechanisms that coordinate interactions between distributed components and AI models. It also addresses critical challenges related to data architecture, scalability, performance optimization, and system reliability in AI-driven environments. The interplay between DevOps and MLOps practices is analyzed as a necessary evolution for managing both application and model lifecycles within a unified operational framework. In addition, the paper highlights the importance of security, compliance, and responsible AI practices, particularly in regulated industries where data privacy and decision transparency are paramount. Through an examination of real-world system design perspectives, the research demonstrates how intelligent microservices architectures can be applied across diverse domains to achieve operational efficiency and strategic advantage. By synthesizing principles from software engineering, distributed systems, and artificial intelligence, this paper presents a comprehensive framework for designing and implementing AI-augmented cloud-native systems. The findings contribute to the understanding of how organizations can successfully navigate the transition toward intelligent, scalable, and adaptive software architectures.
AMIL USLU (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: