<![CDATA[Artificial intelligence research has long framed conversational systems as reactive tools responding to human prompts, a view increasingly insufficient to explain recent developments in autonomous AI. The emergence of Agentic AI signals a shift toward systems capable of planning, acting, and evaluating outcomes independently within complex digital environments. This study aims to conceptualize Agentic AI as a distinct paradigm beyond chatbot-based architectures and to examine its implications for human–AI interaction and governance. The research employs a qualitative conceptual design based on systematic analysis of secondary literature, comparative frameworks, and documented case studies of autonomous AI agents. Analytical synthesis is used to examine autonomy, system architecture, and modes of control across implementations. The results demonstrate that Agentic AI exhibits measurable autonomy through goal persistence, multi-step planning, and self-directed execution, enabling performance advantages in complex tasks while introducing new risks of misalignment and responsibility diffusion. Comparative analysis confirms that autonomy emerges from system-level integration rather than model scale alone. The study concludes that Agentic AI represents a substantive transformation in artificial intelligence practice, requiring revised evaluation metrics, governance structures, and theoretical frameworks. Recognizing Agentic AI as an operational actor rather than a conversational interface is essential for design, deployment, and future research.]]>
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