Agentic AI

What Is Agentic Search? A Definition, and Four Things It Is Not

Two vendors can both say “agentic search” and ship very different things. The label is carrying more weight than it can hold.

Written by Tehreem FatimaReviewed by Umaid Asim

Published 20 October 20267 min read

Agentic search is a retrieval approach where an AI agent plans the searches rather than running one fixed lookup. It reads the question, breaks it into parts, searches, judges whether the results answer it, and searches again if they do not. The agent controls the loop, and that control is the whole of the difference.

Agentic search runs a loop: the agent plans a search, reads what comes back, decides whether it has enough, and searches again if it does not. Ordinary search runs question, one lookup, answer, with no loop back. — Figure 1. Ordinary search answers the question you typed. Agentic search decides what to ask next, which is where both the capability and the cost come from.

What varies is how much of that loop a product actually runs, which is why two systems sold under one label can behave nothing alike. This page pins the term down and separates it from the four it gets confused with. If your question is which approach to build rather than what the words mean, our comparison of agentic search and RAG takes that decision apart.

How Agentic Search Works

OpenSearch describes the behaviour plainly in its documentation: agentic search “lets you ask questions in natural language and have OpenSearch plan and execute the retrieval automatically,” where “a preconfigured agent reads the question, plans the search, and returns relevant results”Source [1].

In practice the loop has four moves.

Plan. The agent interprets the question and decides what to look for. Microsoft’s Azure AI Search describes its query planning, currently marked preview, as breaking “a complex query into smaller, focused subqueries for better coverage”Source [2]. A question about a customer’s refund eligibility might become separate lookups for the order record, the refund policy and the membership terms.

Search. The subqueries run, often against more than one source and often at the same time. Azure AI Search “runs subqueries in parallel” and semantically reranks each oneSource [2].

Evaluate. The agent reads what came back and judges whether it answers the question. This is the step a fixed pipeline does not have.

Decide. Either the evidence is sufficient and the agent answers, or it is not and the agent searches again with a different query or a different source.

The evaluate step is the whole mechanism. Without it you have a system that runs several queries instead of one, which is not the same thing.

Agentic Search Is Not One Thing

This is the part most definitions skip, and it matters more than the definition itself.

OpenSearch ships two kinds of agent under the same feature name. Conversational agents “support multiple tools, conversation memory, and detailed reasoning traces.” Flow agents “offer a streamlined alternative focused solely on query planning,” chosen for “Low latency: Faster query processing with fewer large language model (LLM) calls” and “Cost efficiency: Reduced computational overhead and API costs”Source [1].

Both are called agentic search. One is a reasoning agent with memory and tools. The other is a query planner that rewrites your question and then gets out of the way.

Microsoft makes the same point from the other direction: in Azure AI Search the planning step is a setting, not a fixed behaviour. At minimal effort “this step is skipped and queries are issued directly to knowledge sources”Source [2].

So when a vendor says their product does agentic search, the useful question is not whether it is agentic. It is how far along that range it sits, and what you are paying for the difference.

What Agentic Search Is Often Confused With

Semantic Search

Semantic search matches meaning rather than keywords, using embeddings to find passages that are conceptually close to the query. It is still one query returning one set of results. Nothing decides to search again. Semantic search is often the retrieval method an agentic system calls, not an alternative to it.

RAG

Retrieval-augmented generation describes where the answer’s evidence comes from: retrieve first, then generate from what was retrieved. It says nothing about who chose the query. Basic RAG retrieves once. Agentic search can be the retrieval step inside a RAG system, which is why the two names appear together so often without being the same thing.

Agentic RAG

Agentic RAG is the whole system: an agent directing retrieval inside a generation pipeline, usually with memory, tools and a defined set of controls. The 2025 survey on the pattern describes it as integrating “autonomous agents directly into the RAG pipeline”Source [3]. Agentic search is the retrieval behaviour. Agentic RAG is the architecture built around it.

AI Search And Deep Research

These are product names rather than technical terms, and they vary by vendor. Some are agentic by the definition above. Some are a single semantic lookup with a generated summary on top. The label tells you nothing reliable, so the question to ask is whether the system evaluates its own results and searches again.

What Agentic Search Costs

Each loop is more model calls and more retrieval calls than a single lookup, and the cost scales with how many times the agent decides to go around again. OpenSearch is explicit that choosing the lighter flow agent is a decision about latency and API costSource [1].

That is the trade. A system that can decide what to look for next will sometimes take twelve searches to reach an answer two would have produced, and the finished answer will not show you that it did.

When Ordinary Search Is Still The Right Answer

Agentic search earns its cost when the next useful lookup depends on what the last one returned, when a question spans several sources, or when evidence has to be checked before an answer is trusted.

It does not earn its cost when the retrieval path is already known and repeatable. If every question of a given type is answered by the same two lookups in the same order, planning adds latency and spend without adding capability. A well-configured retriever returning several passages in one pass covers more cases than teams expect.

Elastic’s agent documentation makes a related point worth keeping in view: agents “combine LLM reasoning and context engineering with built-in and custom tools that query your indices, so responses are grounded in your data”Source [4]. The grounding and the permissions are the hard part. The agent loop is the easy part to demonstrate and the expensive part to run well.

That groundwork is a project in itself. Connecting approved documents, records and knowledge sources so an answer can be traced back to its source, and corrected when it is wrong, is the work that knowledge systems are built around. Whether an agent loop sitting on top of that is worth the spend depends almost entirely on how well the layer underneath was put together.

Frequently Asked Questions

Is agentic search the same as agentic RAG?

No. Agentic search is the retrieval behaviour, where an agent plans and repeats searches. Agentic RAG is the full architecture that wraps that behaviour in a generation pipeline, usually with memory, tool access and limits on how long the agent may keep going. Agentic search is a component of agentic RAG, not a synonym for it.

Does agentic search need a vector database?

No. Vector search is a common retrieval method inside an agentic system, but the agent can call keyword search, a SQL database, an API or a document store. What makes the search agentic is that something decides which lookup to run next, not the index technology underneath it.

How do you stop an agentic search running forever?

With explicit limits set before the build: a maximum number of retrieval steps, a budget for time and cost measured per thousand questions, and a defined path when evidence is missing, which is usually to say so, ask a clarifying question, or hand to a person. Without those, an inconclusive search keeps going.

Is agentic search worth it for a small knowledge base?

Usually not. If the content fits a predictable retrieval path and questions are answered by the same lookups each time, a conventional search returns the same answer faster and cheaper. Agentic search pays off with scattered sources, multi-part questions, or evidence that must be verified before it is trusted.

How SensViz Can Help

SensViz builds retrieval and agentic AI systems, including the evaluation, access controls and spend limits that decide whether an agent loop is safe to run in production. If you are weighing agent-directed search against a conventional pipeline, talk to us about your retrieval project and we will tell you honestly which one your questions need.

Discuss your retrieval project