Service-Oriented Evolution of Modern AI: A Position Paper
Zheng Li, Christopher McKie, Hui Wang, Hamza Shakeel, Rajiv Kumar Ranjan · 2025
[Context]: It is well known that understanding the evolution of technologies and its cause is essential for more discoveries and innovations. In the Artificial Intelligence (AI) domain, it has also been identified that scrutinising the development context and path of AI will be able to help both academia and industry better understand the current AI limitations, reveal future AI trends, and facilitate AI/digital transformations. [Objectives]: Given the dramatic boom of modern AI, this research aims to unearth the evolution pattern along the recent three AI waves (namely predictive AI, generative AI and agentic AI), and accordingly to guide AI research and development to focus on the most promising directions. [Method]: We employed analogical reasoning as the research method and referred to the existing software architectural styles to inspire our understanding of the architectural evolution of modern AI technologies. [Results]: We see a service-oriented trend in modern AI's working mechanisms, and the offering of AI power seems to be transiting from a heavyweight and monolithic paradigm to an organisational and collaborative paradigm with more and more specific separation of concerns. Following this service-oriented evolution trend, we borrow software architecture lessons and foresee opportunities to grow the current AI wave to a further height, e.g., standardising AI agent-friendly APIs and developing serverless AI agents. [Conclusions]: What is happening in the AI domain has happened before in the software engineering domain. It is worth reusing software architecture knowledge to evolve the architecture of AI technologies.