Agentic Retrieval-Augmented Generation represents a significant architectural shift from traditional static RAG pipelines. While conventional systems rely on a linear sequence of chunking, embedding, and single-pass retrieval, they frequently fail to locate dispersed or contextually nuanced evidence. Agentic RAG resolves this by transforming retrieval into an iterative loop where an autonomous agent searches, evaluates evidence, and issues follow-up queries until a grounded answer is formed. Recent implementations utilizing the OpenAI Agents SDK demonstrate this capability through a policy research assistant framework. Rather than depending solely on vector similarity, the agent operates with a curated suite of tools including document listing, keyword matching, and direct text reading. When tasked with complex queries spanning multiple policy documents, the agent traces a logical execution path. It begins with targeted keyword searches, inspects document metadata to identify relevant sources, reads specific files, and synthesizes a response supported by precise citations. This iterative trace confirms the model’s ability to autonomously verify information and avoid premature conclusions. Deploying agentic RAG in production requires careful architectural planning. Engineers must first determine the appropriate level of agent autonomy, balancing controlled tool access against the risks of unrestricted file or shell operations. Data preparation also evolves beyond raw text ingestion; integrating derived knowledge layers such as metadata, summaries, or knowledge graphs significantly enhances navigation efficiency. Contrary to emerging assumptions, semantic embeddings remain valuable within agentic frameworks, often serving as the engine for the agent’s search tools alongside keyword matching. Scalability considerations further dictate system design. While single-agent architectures simplify debugging, complex enterprise workloads may benefit from multi-agent orchestration, dividing responsibilities between planners, retrievers, and synthesizers. However, this introduces coordination overhead that demands empirical validation. Ultimately, agentic RAG should not be adopted as a default solution. Its inherent latency and computational costs require justification. Organizations are advised to implement traditional RAG first and introduce agentic loops only when queries inherently demand iterative reasoning and cross-document synthesis. This measured approach ensures that the technology delivers measurable accuracy improvements without compromising system reliability or cost efficiency.