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Self-Healing AI Automation Frameworks for Highly Resilient Cloud-Native Software Delivery Pipeline Management

Praiselin Lydia Gladston

Rogers Communications, Calgary, Alberta, Canada

Abstract

Cloud-native software delivery has transformed application development through microservices, containers, Kubernetes orchestration, infrastructure as code, continuous integration, and continuous delivery. However, the increasing complexity and dynamism of cloud environments also create new reliability challenges, including configuration drift, resource exhaustion, deployment failures, service dependency problems, security incidents, and infrastructure instability. Conventional DevOps automation primarily executes predefined procedures and often depends on human intervention when unexpected conditions occur. Self-healing artificial intelligence (AI) automation frameworks offer an emerging approach in which intelligent systems continuously observe delivery pipelines, identify abnormal conditions, diagnose probable causes, predict failures, and automatically execute corrective actions. This study examines the conceptual design and operational characteristics of self-healing AI automation frameworks for resilient cloud-native software delivery pipeline management. The proposed research methodology integrates systematic literature analysis, framework modelling, scenario-based experimentation, and quantitative evaluation using resilience-oriented metrics. The study focuses on integrating observability, machine learning, anomaly detection, causal diagnosis, automated remediation, policy enforcement, and continuous feedback into a closed-loop delivery architecture. Particular attention is given to balancing autonomous remediation with safety controls, explainability, security, and human oversight. The research argues that self-healing pipelines can reduce recovery time, improve deployment reliability, minimize operational intervention, and strengthen service continuity when designed with appropriate guardrails. The resulting framework provides a foundation for developing adaptive, resilient, and intelligent cloud-native software delivery ecosystems

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 11 No. 4 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2026.1104005

Pages

21602-21611

Published

July 20, 2026
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Praiselin Lydia Gladston (%2026). Self-Healing AI Automation Frameworks for Highly Resilient Cloud-Native Software Delivery Pipeline Management. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 4 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 21602-21611. https://doi.org/10.15662/ijetemr.2026.1104005

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