Agentic RAG Lets AI Models Control When and How They Retrieve Information

Agentic RAG is an evolution of retrieval-augmented generation (RAG) in which the AI agent — rather than a fixed pipeline — decides when to retrieve data, how to frame the query, and whether the returned results are adequate. Unlike classic RAG, which embeds a user's question, fetches top results once, and passes them directly to the model, agentic RAG treats retrieval as a repeatable tool the model can call multiple times until it has sufficient context. The agent can rewrite queries, judge the relevance of results, switch between different data sources such as vector indexes, SQL databases, or APIs, and retry searches before generating a final answer. This added flexibility improves recall and handles complex, multi-step questions better than a linear pipeline, but comes at the cost of higher latency and less predictable compute usage. Classic RAG remains the more efficient choice for straightforward lookups over a single corpus, and the agentic approach is best reserved for scenarios where retrieval quality genuinely needs dynamic oversight.