Agentic AI represents a significant advancement in artificial intelligence, characterized by autonomous software entities that independently pursue goals, make decisions, and take actions with minimal human oversight. Unlike traditional AI models that are reactive and task-specific, Agentic AI systems are designed to observe their environment, learn from feedback, and execute complex sequences of tasks, thereby enhancing automation across the software development lifecycle, particularly in DevOps environments. These intelligent agents integrate the reasoning capabilities of large language models with custom code interfaces to external tools and systems, enabling them to coordinate complex workflows, such as code generation, testing, deployment, and monitoring, while maintaining a holistic view of the development and operations lifecycle. By doing so, they streamline workflows, reduce operational overhead, and accelerate digital transformation initiatives, offering transformative benefits like faster delivery cycles, enhanced quality assurance, and more resilient systems. However, challenges such as contextual limitations, security concerns, technical hurdles, and organizational impact must be addressed to successfully implement AI agents in DevOps. Various frameworks and platforms, such as LangChain, AutoGPT, and GitOps tools, provide modular components to help developers build AI agents tailored for DevOps workflows. These agents transform CI/CD pipelines into intelligent systems that adapt to development patterns, proactively address issues, and continuously optimize pipeline configurations, ultimately reducing manual overhead and system downtime.