Agentic AI for Autonomous Micro-Frontend User Interfaces and Microservices Evolution in Cloud Platforms

Abstract

Cloud-native organizations increasingly rely on microservices for backend modularity and micro-frontends for scalable user interface delivery. Yet, real-world systems still struggle to evolve these layers coherently under high release velocity, shifting product goals, and variable workloads. This paper presents a unified Agentic AI framework that autonomously coordinates the co-evolution of micro-frontend UIs (implemented in ReactJS and Angular) and microservices. The proposed architecture integrates reinforcement learning for continuous control, large language models for code and configuration synthesis, and a policy-governed multi-agent control plane that executes progressive delivery (feature flags, canary, blue-green) via Kubernetes and service meshes. We formalize decisions using Markov Decision Processes, propose drift detection models for UI-API compatibility, and formulate traffic-shifting optimization for safe rollouts. A mini empirical study across e-commerce, SaaS analytics, and multi-cloud migration scenarios demonstrates reductions in adaptation latency, error rates, and manual intervention relative to strong DevOps baselines. We discuss reliability, explainability, and governance challenges, and lay out future research on hybrid RL-LLM agents, knowledge-graph-aware planning, digital twins, and compliance-aware rewards.