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Agentic search

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Agentic search is a retrieval approach in which an AI agent (typically driven by a large language model) actively plans and carries out a multi-step search process rather than performing a single, static lookup. The agent decomposes a query, decides which sources or tools to consult, issues multiple searches, evaluates what it finds, and refines its approach in a loop until it has gathered enough information to answer.

In short: traditional search often retrieves once; agentic search reasons, retrieves, and re-retrieves until the question is genuinely answered.

How agentic search works

Agentic search replaces the single “query in, results out” step with an iterative loop that the agent controls:

  1. Intent understanding: The agent interprets what the user actually wants, including implicit constraints and the underlying goal behind the literal query.
  2. Planning and decomposition: Complex questions are broken into smaller sub-questions or tasks that can be researched independently.
  3. Tool and source selection: The agent chooses where to look, selecting from a Web search index, a vector database, an internal knowledge base, a structured API, or a combination of these.
  4. Iterative retrieval: The agent issues searches (sometimes in parallel, sometimes in sequence) and reads the results.
  5. Evaluation: The agent judges whether the retrieved material is relevant, sufficient, and trustworthy, and it identifies gaps or contradictions.
  6. Refinement: Based on that judgment, the agent reformulates queries, drills deeper, or explores a new angle, repeating the loop as needed.
  7. Synthesis: Once it has enough, the agent combines its findings into a coherent answer, often citing its sources.

A defining feature is reflection, in which the agent can recognize when an answer is incomplete or low-confidence and decide to search again before returning an answer.

  • Keyword (lexical) search matches the literal terms in a query against documents, typically without an explicit multi-step planning loop.
  • Semantic search uses embeddings to match meaning rather than exact words, returning the most similar passages. This is often implemented as a single retrieval pass.
  • Standard retrieval-augmented generation (RAG) often retrieves relevant context once and feeds it to an LLM to generate an answer, though iterative variants also exist.
  • Agentic search adds autonomy and iteration on top of these techniques. The agent may use keyword or semantic search as tools, but it decides how many times to search, what to search for next, and when it is done.

Agentic search is closely related to agentic RAG; the difference is mostly one of emphasis. Agentic search describes the autonomous, multi-step retrieval behavior, while agentic RAG describes a full pipeline that pairs that behavior with answer generation.

Where agentic search is applied

  • AI answer engines and “deep research” tools: Synthesize information across many sources before responding.
  • Enterprise knowledge assistants: Search across documents, wikis, ticketing systems, and databases to answer employee questions.
  • Customer support agents: Look up policies, account details, and documentation across multiple systems to resolve an issue.
  • Coding assistants: Search a codebase, its dependencies, and documentation to understand context before suggesting changes.
  • Specialized research: Workflows in fields such as legal, medical, and financial analysis, where good answers depend on reconciling multiple authoritative sources.

Agentic search is most valuable when a question is complex, spans multiple sources, or cannot be answered by a single document—situations where one retrieval pass often falls short.

Retrieval-augmented generation (RAG), agentic RAG, semantic search, vector database, embeddings, query decomposition, tool use, LLM agents.