Enterprise SaaS Evolution: From Monolithic to Cloud-Native to Agentic Platforms

Abstract

Enterprise Software-as-a-Service (SaaS) has evolved through three key stages: monolithic multi-tenant applications, cloud-native microservices, and now, the latest generation of “agentic” platforms powered by large language models (LLMs), retrieval-augmented generation (RAG), and autonomous agents. While there has been plenty of discussion around microservices, observability techniques, and frameworks for generative AI, not much has been said about how these changes tie into organizational design, business value, and governance issues with an enterprise SaaS focus. This paper presents a targeted narrative synthesis of Enterprise SaaS evolution from the late 1990s through the early 2020s and outlines emerging directions for agentic platforms. It examines the defining technologies and structural patterns of each architectural phase and analyzes their business and operational implications, including delivery performance, scalability, innovation capacity, and customer experience. The paper further introduces an outcome-oriented evaluation framework for enterprise generative AI platforms that links technical performance metrics to strategic and organizational outcomes and anchors model quality assessment in emerging standards and benchmarks. In addition, it synthesizes key implementation challenges – such as technical debt, organizational capability gaps, integration complexity, and governance risk – and reviews mitigation strategies observed in recent practice and literature. Drawing on surveys, standards, and industry reports, this study concludes with a research agenda focusing on long-term and empirical evaluation of generative AI adoption, the co-relation between technical architecture and organizational form, and governance models for increasingly autonomous systems. The findings provide a structured reference and framework for researchers and practitioners designing, operating, and governing next-generation Enterprise SaaS platforms.