Abstract : Precision Agriculture has come a long way with the integration of the Internet of Things (IoT), data analytics, and automation. However, most existing systems are still reactive, rule-driven, and limited in their capacity to adapt to new environmental conditions. This paper introduces a novel Agentic Retrieval-Augmented Generation (Agentic RAG) architecture for closed-loop precision farming that combines Large Language Models (LLMs), autonomous multi-agent reasoning, dynamic knowledge retrieval, and feedback guided learning. The proposed architecture implements a cognitive Sense–Retrieve–Reason–Act–Evaluate–Learn cycle, enabling adaptive, explainable, and continuously improving agricultural decision-making. As a theoretical and architectural study, this work presents the system structure, evaluation metrics, qualitative analysis, and illustrative scenarios to establish feasibility and guide future empirical validation.

Keyword : Agentic AI, Agriculture 4.0, Closed-Loop Control, Explainable AI, IoT, Precision Agriculture, Retrieval Augmented Generation.