Agentic Retrieval-Augmented Generation (RAG) improves answer reliability by combining retrieval, reasoning, and iterative information refinement, but repeated model calls can increase latency, computational overhead, and deployment complexity. This paper introduces an embedded agentic retrieval framework enhanced by Artificial Intelligence (AI) that aims to produce efficient single-call retrieval while maintaining answer quality similar to multi-call agentic retrieval (RAG). The proposed method combines the following: query-aware retrieval, context compression, relevance prioritization, and embedded reasoning together in a single generation call. The framework combines relevant evidence into a concise context representation, eliminating the need to repeatedly invoke the language model during the retrieval and reasoning phases, and then generates the response in the end. Experimental evaluation evaluates the quality of answers, the relevance of retrieval, the time taken to respond, the number of tokens, and the efficiency of computation on a set of representative retrieval-based question-answering tasks. The proposed EARG framework achieved 98.1% accuracy while reducing latency and maintaining high answer quality. The framework is an efficient basis for effective agentic RAG systems that need scalable, low-latency, and resource-efficient generation.