Autonomous Cloud Native Release Engineering for Enterprise Platforms Using Agentic AI and Continuous Delivery Intelligence
DOI:
https://doi.org/10.15662/IJARCST.2025.0805041Keywords:
Agentic AI, Cloud Native, Release Engineering, Continuous Delivery, CI/CD Intelligence, Autonomous Deployment, DevOps, Kubernetes, Intelligent Automation, Enterprise PlatformsAbstract
Autonomous cloud-native release engineering is becoming increasingly important for enterprise platforms that require rapid, reliable, secure, and continuously optimized software delivery. Traditional continuous integration and continuous delivery pipelines depend heavily on predefined rules, static quality gates, manual approvals, and reactive troubleshooting, which can limit scalability and increase operational complexity. This study proposes an autonomous cloud-native release engineering framework that integrates agentic artificial intelligence with continuous delivery intelligence to enable adaptive pipeline orchestration, intelligent deployment decisions, automated failure analysis, and proactive release optimization. The proposed approach combines machine learning, large language model-based reasoning, observability data, infrastructure telemetry, deployment history, and policy-driven controls to create intelligent software delivery agents capable of analyzing pipeline conditions and recommending or executing context-aware actions. The framework incorporates automated testing, security validation, infrastructure provisioning, deployment monitoring, rollback management, and post-release learning within a unified lifecycle. A methodology is presented for evaluating release reliability, deployment frequency, lead time, failure recovery, security compliance, and resource utilization. The proposed architecture is designed to support Kubernetes-based, hybrid-cloud, and multi-cloud enterprise environments while maintaining governance and human oversight for high-impact decisions. The study demonstrates how agentic intelligence can transform conventional CI/CD pipelines into adaptive release systems capable of continuous learning, autonomous coordination, and resilient enterprise software delivery.
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