Modern enterprise computing ecosystems have shifted toward distributed, cross-platform business systems operating across multi-cloud infrastructures, microservices-based architectures, and containerized deployment environments. While this transition enhances scalability, interoperability, and operational agility, it also introduces significant challenges in system reliability, fault tolerance, and automated recovery. Traditional IT operations rely heavily on rule-based monitoring and human-in-the-loop incident response mechanisms, which are insufficient for handling complex, cascading failures in highly dynamic distributed environments.
This research proposes an autonomous recovery framework that integrates Generative Language Models (GLMs) with container orchestration platforms such as Kubernetes to enable intelligent, self-healing enterprise systems. The framework is designed to interpret system telemetry, logs, and event streams using semantic reasoning capabilities of GLMs, enabling automated root cause analysis and recovery orchestration without manual intervention.
A key conceptual foundation of this study is derived from self-healing multi-cloud systems and post-incident intelligence mechanisms that leverage artificial intelligence to continuously learn from failure patterns and optimize system resilience. In particular, the integration of LLM-driven reasoning with Kubernetes-based orchestration forms the backbone of automated remediation workflows, enabling dynamic scaling, rollback strategies, and service reconfiguration in real time.
The proposed architecture is further strengthened by insights from digital twin systems, AI-driven industrial transformation, and semantic knowledge fusion models. These complementary paradigms enhance system observability, contextual awareness, and adaptive decision-making in distributed computing environments.
Experimental and conceptual analysis indicates that such integrated frameworks significantly reduce mean time to recovery (MTTR), improve anomaly classification accuracy, and enhance system stability under variable workload conditions. However, challenges persist in ensuring explainability, controlling hallucinated outputs from generative models, and maintaining secure execution boundaries in autonomous systems.
Overall, this study contributes a structured approach toward building next-generation autonomous enterprise infrastructures capable of self-diagnosis, self-repair, and continuous optimization in cross-platform distributed environments.

Autonomous recovery, , Generative Language Models, Kubernetes, distributed systems,

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