AgenticAIs makes autonomous decisions, optimize, and organize workflows on their own are becoming a disruptive layer in cloud-native software delivery. Karpenter collaborated with Kubernetes to enable intelligent node provisioning, simplify resource allocation, and provide production-grade reliability at scale. As explored in this paper, Agentic AI enhances the following Kubernetes-based DevOps processes: optimizing CI/CD orchestration, forecasting resource demand, accelerating fault recovery, and improving application lifecycle management. Continuous monitoring and control of cluster behavior, advanced diagnostics, and targeted corrective actions are all aspects of the Integrated Agentic AI models that makes great independent operations with limited human control a possibility. The paper presents key technical underpinnings, including multi-agent systems (for example MCP server), reinforcement learning, operator-based AI control loops, and AI-based policy enforcement. Practical examples are examined, such as automated scaling, self-healing clusters, smart canary rollouts, drift detection, and cost-constrained resource allocation. Issues such as model reliability, governance, interpretability, and production-grade security are addressed with mitigating solutions. Combining existing practices and fresh innovations, this article can serve as a comprehensive guide for leaders in the engineering community, DevOps teams, and platform designers who want to use Agentic AI in Kubernetes-driven environments. The insights highlight how automated intelligence can greatly reduce development cycles, minimize operational friction, and enable continuous, dependable delivery in the evolving, dynamic ecosystem of production.

Agentic AI, Kubernetes, Autonomous DevOps, AI-Driven Deployment, Cloud-Native Engineering, MCP server, Kapenter

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