Agentic RAG: What it is, how it works, and when to use it

Agentic RAG (retrieval-augmented generation) is an advanced AI framework where an agent dynamically controls the retrieval process, unlike standard RAG's fixed retrieve-then-generate approach. This method is particularly effective for complex queries needing multi-step reasoning, cross-source synthesis, or evidence validation before response generation. Agentic RAG iteratively retrieves and evaluates data until it achieves sufficient context or reaches a stopping point, resulting in more reliable and auditable answers. The system is characterized by its ability to select tools and adjust retrieval strategies on-the-fly, which can effectively address industries such as enterprise knowledge, finance, legal, healthcare, and customer support. However, this adaptability comes with tradeoffs, including increased latency, token costs, and complexity in system evaluation and maintenance. Implementing agentic RAG involves starting with a standard RAG baseline, identifying specific failure modes, introducing agentic patterns to address these issues, and optimizing retrieval contexts using tools like GraphRAG for improved relevance and traceability.