Designing Self-Healing AI Agentic Systems: A Framework for Autonomous Detection and Response

Abstract

As AI agentic systems become more prevalent in distributed computing environments, they present unique challenges in terms of resilience, fault tolerance, and self-sustaining operations. In modern AI infrastructures, which are frequently subjected to cascading failures, communication disruptions, resource overload, and adaptive instability, particularly in the case of dynamic and large-scale environments. The existing methods in the recovery area are primarily fault detection and rule-based recovery, and provide little support to autonomous self-healing, adaptive recovery learning, and intelligent reconfiguration of the systems. In this work, the authors aim to fill in those missing pieces by proposing a new scientific paradigm, the Autonomous Cognitive Self-Healing Layer (ACSHL), which enables autonomous anomaly detection, cognitive fault diagnosis, orchestration of adaptive responses, and reinforcement-based recovery in AI agentic ecosystems. The proposed framework is developed under the Autonomous Resilient Agentic Intelligence (ARAI) theory, which intends to come up with a common model for resilient autonomous intelligence infrastructures. ACSHL brings together Intelligent Monitoring Agents, Cognitive Failure Analyzers, Adaptive Recovery Orchestrators, and Resilience Learning Engines, thereby allowing operations to adapt and self-recover continuously. The framework is then experimentally evaluated in the context of distributed fault injection environments, synthetic anomaly scenarios, Google cluster traces, and NASA system failure datasets. The performance of the systems was assessed using the metrics of detection accuracy, recovery latency, uptime stability, and efficient adaptive recovery. From experimental results, the proposed ACSHL framework has demonstrated recovery accuracy with 96.2% and less downtime by 41% as compared to conventional static and rule-based recovery systems and has also exhibited better adaptive recovery performance. The study introduces a new scientific object – the Autonomous Recovery Efficiency Score (ARES) – a quantitative measure of autonomous resilience, as well as a supporting foundation for future autonomous self-healing AI agentic infrastructure.