Agentic RAG systems represent a significant evolution in AI application development by integrating the knowledge retrieval capabilities of traditional Retrieval-Augmented Generation (RAG) systems with the decision-making prowess of AI agents. This combination allows for more autonomous and efficient handling of complex queries that require multi-step reasoning, which traditional RAG systems struggle with. Agentic RAG systems break down complicated queries into simpler sub-questions, dynamically choose the best information sources, validate retrieved data, and iteratively refine answers, resulting in higher accuracy and adaptability. While these systems incur higher costs and complexity due to increased token usage and the need for more capable models, their ability to manage complex problem-solving across multiple domains is a notable advantage. The guide provides a practical implementation using CrewAI for agents and Helicone for monitoring, highlighting the benefits and trade-offs of adopting Agentic RAG over traditional RAG. As LLM models become more affordable, adoption of Agentic RAG systems is expected to rise, offering enhanced capabilities for challenging queries in AI applications.