Agentic AIOps for Cloud-Native Enterprise Systems: A Governance-Centered Framework for Software Reliability, Security, and Deployment Optimization
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I1P147Keywords:
Agentic Aiops, Cloud-Native Systems, Software Reliability, Secure Software Development, Observability, Microservices, Deployment Governance, Defect Prediction, AI Risk Management, Enterprise ArchitectureAbstract
Cloud-native enterprise systems increasingly operate through distributed microservices, event-driven data flows, container orchestration, software supply chains, and multi-layer observability pipelines. Although these architectures improve modularity and deployment flexibility, they also introduce operational complexity, hidden failure propagation, security exposure, and fragmented governance across software development, deployment, and runtime operations. Recent advances in artificial intelligence, large language models, and autonomous software agents create an opportunity to move beyond conventional monitoring dashboards and rule-based automation toward agentic AIOps systems that can reason over telemetry, correlate defects with runtime behavior, recommend remediation, and support secure deployment governance. However, existing approaches remain insufficient because many of them treat software defect prediction, anomaly detection, incident response, cybersecurity controls, and deployment optimization as separate functions rather than as integrated lifecycle capabilities. This paper proposes a governance-centered Agentic AIOps Framework for cloud-native enterprise systems. The framework combines predictive software reliability models, graph-based dependency reasoning, secure software development controls, runtime observability, AI risk governance, and human-supervised remediation workflows. The major contribution of this paper is a vendor-neutral reference architecture that connects AI-driven defect prediction, cloud-native telemetry, policy-based security, deployment risk scoring, and enterprise governance into a unified model. The paper argues that agentic AIOps can improve operational decision support when it is designed with traceability, explainability, guardrails, and measurable reliability objectives rather than unrestricted automation. The proposed framework is conceptual and architecture-based, and its value lies in structuring future enterprise implementations, comparative evaluations, and research on trustworthy AI-enabled operations.
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