Autonomous DevOps Automation Through Agentic AI Across Distributed Cloud Cybersecurity Environments
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Abstract
The increasing complexity of distributed cloud infrastructures has created significant challenges for conventional DevOps teams, particularly in continuous integration, continuous delivery, infrastructure management, incident response, security monitoring, and operational optimization. Agentic Artificial Intelligence (AI) introduces a new paradigm in which AI systems can autonomously interpret objectives, plan tasks, interact with development and cloud-management tools, execute actions, observe outcomes, and adapt subsequent actions. This research proposes a framework for autonomous DevOps automation through agentic AI operating across distributed cloud cybersecurity environments. The proposed architecture integrates AI agents with continuous integration and continuous delivery pipelines, infrastructure-as-code, observability platforms, security operations, vulnerability management, policy engines, cloud orchestration, and zero-trust access controls. Multiple specialized agents are coordinated through an orchestration layer to perform software testing, deployment optimization, infrastructure remediation, anomaly detection, security analysis, and incident-response activities. The methodology follows a design-oriented research approach involving requirement analysis, architecture development, agent workflow modeling, cybersecurity-policy integration, prototype-oriented evaluation, and comparative performance assessment. Evaluation criteria include deployment speed, automation coverage, incident-response time, security-policy compliance, false-positive rates, resource utilization, reliability, and human intervention requirements. The proposed framework emphasizes bounded autonomy, least-privilege access, continuous monitoring, approval gates for high-impact actions, and comprehensive audit trails. The research argues that agentic AI can transform DevOps from predominantly rule-driven automation toward adaptive, context-aware, and continuously optimizing operations while maintaining cybersecurity and human oversight.
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