The exploration of AI-generated creative content has increasingly focused on the ability of machine learning models to produce text that resonates with human-like creativity, particularly in the domain of poetry. The novel introduction of a multi-agent framework represents a significant advancement in enhancing the poetic capabilities of language models, allowing for the generation of poems that are not only technically proficient but also exhibit deep thematic coherence and creative expression. Through the collaborative efforts of specialized agents focusing on rhyme, meter, theme, and metaphor, the framework refines initial drafts generated by the core model, producing outputs that are structurally sound and rich in lexical and artistic depth. Experimental results demonstrated the framework's effectiveness in improving rhyme quality, rhythmic consistency, and overall creative merit when compared to standard baseline models, showcasing the potential of such a system to push the boundaries of AI-driven literary generation. The significance of this work lies in its contribution to the intersection of artificial intelligence and the arts, providing a sophisticated tool for exploring and expanding the creative capabilities of machine-generated text.