Agentic search is a retrieval process where an AI agent breaks a question into searches, evaluates the evidence it finds, and decides whether to refine the query, inspect another source, or stop. Unlike one-shot search, it treats retrieval as a loop that can adapt when the first results are incomplete.
The practical decision point is between searches: an agent should know what evidence is missing before it asks again.
How does agentic search differ from standard search?
Standard search sends one query and ranks results. Agentic search plans a set of queries, retrieves evidence, compares coverage, then chooses the next step. The agent evaluates retrieval before it writes an answer.Query rewriting may turn an ambiguous question into a better search phrase, while semantic search can find relevant passages that do not share the user’s wording.
When should a team use it?
It earns the extra work on questions that span sources, need corroboration, or have a high cost of a confident gap. A narrow question with a strong index usually needs one retrieval pass, not an agent loop.Agentic RAG applies the same idea to an internal knowledge base. Deep research agents extend it into longer research tasks with explicit source review and synthesis.
Frequently asked questions
Is agentic search the same as web search?
No. Web search can be one tool an agent uses. Agentic search describes the decision loop that chooses and evaluates multiple retrieval steps before answering.
Does agentic search always produce better answers?
No. It can waste time or collect weak evidence when a simple query already works. It needs stop rules, source-quality checks, and evaluation on real questions.
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