An Adaptive AI-Driven Framework for Optimizing Core Web Vitals in Large-Scale Digital Platforms

Abstract

Web performance optimization has become one of the biggest challenges faced by large, scalable web applications. Core Web Vitals established a standard set of metrics to measure user experience across different conditions and deployments. Largest Contentful Paint measures loading performance, Interaction to Next Paint measures responsiveness, and Cumulative Layout Shift measures visual stability during the page lifecycle. Improvements often depend on fixed rules and settings, which may not adjust well to different situations, like using various devices, networks, and changing content needs from the application. An alternative or complementary approach is a learning-based smart system that leverages monitoring data from real-world users in production systems. Lightweight decision models based on machine learning can optimize frontend actions depending on the current workload. The proposed framework is distributed across geographically distributed infrastructure. Edge-based processing nodes can reduce the delay between observing performance and enacting a performance optimization. Experimental results show significant performance gains in all Core Web Vitals metrics when compared to standard static solutions, and its ability to scale and adapt confirms that we are ready for real-world use. The adaptive framework provides the foundation for the role of AI in web performance engineering today.