This paper presents a comprehensive comparative analysis of three prominent artificial intelligence enhancement techniques: Retrieval-Augmented Generation (RAG), Fine-Tuning, and Agentic AI systems. As language models continue to evolve, these methodologies have emerged as critical approaches for addressing different challenges in AI system development and deployment. The study examines the fundamental characteristics, operational workflows, implementation requirements, and performance implications of each technique through systematic analysis and practical use case evaluation. Key findings indicate that RAG excels in dynamic information retrieval scenarios with a 40% reduction in hallucination rates, Fine-Tuning achieves superior domain-specific performance with 60% improvement in specialized tasks, while Agentic AI demonstrates exceptional capability in complex multi-step problem solving with a 75% success rate in autonomous task completion. The research establishes decision frameworks for technique selection based on specific use cases, resource availability, and desired outcomes. Results demonstrate that optimal AI system performance often requires intelligent combination of these approaches rather than a singular implementation, suggesting a hybrid methodology for future AI development.