In January 2025, Aditi Singh and coauthors published a survey titled Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG , revised in April 2026. They describe agentic retrieval augmented generation, or agentic RAG, as a search system with an AI agent inside it. The agent can plan its next step, check its own results, and use other programs, such as a calculator or a search box, while it looks for information. The same survey lists evaluation and efficiency among the open challenges. That is the part I wanted to measure.

Retrieval augmented generation, or RAG, means that a language model, an AI system that writes text, answers a question using text it first retrieved from a collection of documents. RAG with plain search looks the question up once, as written. Agentic RAG adds an agent. A language model reads the first results and decides whether to stop or to write a new search.

More searches look like they should find better evidence, meaning the paper that actually answers the question.…