Self-Healing Enterprise Infrastructure Through Agentic AI, Continuous Observability, and Autonomous Threat Response

Abstract

Enterprise infrastructure is increasingly exposed to sophisticated cyber threats, dynamic workloads, configuration drift, service failures, and operational complexity across hybrid and multi-cloud environments. Conventional infrastructure management relies heavily on predefined rules, manual intervention, and reactive incident handling, which can delay recovery and increase operational risk. This research proposes a self-healing enterprise infrastructure framework that integrates agentic artificial intelligence, continuous observability, and autonomous threat response to enable adaptive, proactive, and resilient IT operations. The proposed framework employs autonomous AI agents to continuously analyze telemetry from applications, networks, cloud resources, containers, endpoints, and security platforms. Continuous observability combines logs, metrics, traces, events, behavioral indicators, and security signals to establish contextual awareness of infrastructure conditions. An intelligent decision layer correlates detected anomalies and threats, evaluates operational risk, and selects appropriate remediation actions. Autonomous response mechanisms subsequently perform controlled activities such as workload restart, configuration correction, traffic isolation, credential restriction, resource scaling, and security policy enforcement. The methodology emphasizes event-driven architecture, machine learning-based anomaly detection, policy-guided agentic reasoning, feedback-driven remediation, and human oversight for high-impact decisions. The framework is designed to reduce mean time to detection and recovery while improving availability, security, scalability, and operational efficiency. The research provides a foundation for resilient enterprise infrastructure capable of continuously detecting, reasoning, responding, and adapting to changing operational and cybersecurity conditions.