Agentic Search
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Agentic search is an approach to information retrieval in which AI agents are equipped with a set of tools to progressively and iteratively find and reason over context, as opposed to performing a single, one-shot lookup. It is frequently contrasted with RAG (Retrieval-Augmented Generation), which it is positioned to supersede for demanding use cases.
Definition and Core Mechanics
Kuba Rogut (Turbopuffer) defines agentic search as "giving the agents a set of tools to kind of progressively and iteratively find and reason over context" — explicitly distinguishing it from naive approaches like merely grepping through a file system. (source) The emphasis is on multi-step, tool-mediated reasoning rather than a single retrieval pass feeding a context window.
Relationship to RAG
Rogut frames agentic search as an alternative to conventional RAG, summarizing the thesis of his talk as "RAG is dead — how hybrid, tool-rich retrieval is becoming a default for serious agentic search." (source) The argument is not that retrieval disappears, but that the retrieval layer becomes richer: hybrid (combining multiple retrieval strategies) and tool-mediated, rather than a simple one-time vector search.
Adoption Trajectory
Rogut characterizes agentic search as an emerging default among advanced practitioners. Simple, one-shot vector search RAG was the norm in 2023 and early 2024, but by the time of his talk, "a lot of the more sophisticated customers are doing agentic search and it's giving like real real big performance gains." (source) This positions agentic search as a marker of maturity in agentic systems development — something early adopters have validated but that has not yet fully diffused across the field.
Points of Disagreement
No speaker in the available material explicitly argues against agentic search. The framing of "RAG is dead" is introduced as a provocative thesis by Rogut himself (source), suggesting the claim is contested in the broader community even if no direct counterargument is recorded here.