A Framework for Intelligent Observability and Predictive UI Performance in Web Applications

Abstract

Modern web applications increasingly rely on rich, dynamic user interfaces, making UI performance a critical determinant of user satisfaction, engagement, and business outcomes. However, existing observability approaches remain largely reactive, detecting performance degradations only after users are already impacted. This paper proposes a unified framework for intelligent observability and predictive UI performance monitoring in web applications. The framework integrates real-user monitoring, infrastructure telemetry, and contextual deployment signals into a feature-enriched observability pipeline that can forecast UI performance degradations in advance. By combining temporal modelling, lightweight machine learning predictors, and explainability mechanisms, the system not only anticipates performance regressions but also provides actionable insights into their root causes. A closed-loop feedback mechanism enables proactive remediation, such as automated alerts, deployment rollbacks, and adaptive optimisation strategies. Experimental evaluation demonstrates that the proposed framework improves early-detection accuracy, reduces the mean time to resolution, and significantly mitigates user-facing performance impact compared to traditional threshold-based monitoring systems.