Retrieval-Augmented Generation (RAG) enhances Large Language Models by grounding their responses in external knowledge, yet existing approaches struggle with complex reasoning tasks that require combining multiple sources of information. Although recent multi-retrieval methods integrate iterative retrieval and reasoning, they still lack mechanisms to decide when to stop retrieving and how to maintain reasoning coherence, often leading to error propagation and hallucinations. This research introduces a metacognitive multi-agent framework for RAG that models reasoning as a collaborative process guided by metacognitive control and shared memory systems. The framework enables dynamic coordination between retrieval and reasoning, allowing the system to monitor its progress, assess evidence sufficiency, and revise its reasoning when inconsistencies appear. By incorporating metacognitive regulation and explicit memory interaction, the proposed approach aims to improve reasoning reliability, factual grounding, and interpretability in multi-hop question answering.