Agentic AI for Self-Healing Microservices: An LLM-Orchestrated Framework for Autonomous Fault Detection and Remediation in Regulated Enterprise Environments

Abstract

: Enterprise-scale distributed microservices in regulated environments operate under stringent availability, latency, and auditability requirements. Traditional monitoring approaches detect anomalies reactively, after service degradation has already impacted end users or regulatory SLA (Service Level Agreement) obligations. This paper presents the Cognitive Agentic Self-Healing Platform (CASP): an LLM-orchestrated agentic system that continuously monitors distributed microservice environments, autonomously diagnoses fault conditions across six failure categories, and executes targeted remediation actions without human intervention. CASP integrates a structured four-phase autonomous loop comprising detection, diagnosis, remediation, and verification, with an LLM decision engine supporting both cloud-hosted and locally deployable backends, enabling deployment in air-gapped environments subject to data residency requirements. Evaluation on a controlled synthetic workload, calibrated against published benchmark profiles, demonstrates 88.3% overall diagnostic accuracy, an 83% reduction in mean recovery time compared to no self-healing, and a 53% reduction compared to static rule-based approaches. Post-recovery latency stabilizes within 7 minutes. The locally deployable LLM backend achieves p95 decision latency of 4.9 seconds, within the 5-second operational SLA threshold, making it viable for production deployment in regulated environments subject to data residency requirements.