Optimizing Application Support Operations with LLMs- Insights from a Self-Healing Interface

Abstract

Application support operations are increasingly challenged by complex system architectures, high service expectations, and the need for rapid incident resolution. This paper introduces an LLM-powered self-healing framework that automates routine support tasks, reduces mean time to resolution (MTTR), and enhances system resilience through proactive anomaly detection and autonomous remediation. Leveraging data observability as a foundational layer, the framework integrates telemetry aggregation, intelligent root cause analysis, and automated runbook execution to minimize downtime while maintaining transparency and trust. Positioned at the intersection of DevOps and AIOps, this approach aligns with emerging trends in autonomous incident resolution, continuous learning systems, and operational excellence. By reducing manual intervention and enabling proactive maintenance, the framework represents a forward- leaning strategy for optimizing modern application support operations.