Traditional search takes one query and returns a ranked list of links for a person to read. Agentic search gives an AI agent a goal. The agent plans its own queries, runs many searches, reads full pages, checks what is still missing, and repeats until it can answer with sources.
The difference is who does the work after the results appear. With traditional search, the search experience ends when the links come back, and you do the clicking, reading and comparing. With agentic search, software runs that loop for you.
For a deeper primer, see what agentic search is.
How Traditional Search Works
A traditional search engine crawls the web, stores pages in an index, and ranks them when you type a query. According to the Google Search ranking systems guide, Google uses automated ranking systems that look at many factors and signals about hundreds of billions of web pages to present the most relevant, useful results, all in a fraction of a second. What you see is a ranked page of results, mostly links.
This model is fast and cheap per query, but it assumes a human is in the loop. You decide which links to open, notice what is missing, and type a new query.
For simple questions, that works well. For multi-part research, the human does most of the work.
From Keyword to Semantic to Agentic Search
Search has moved through three stages. Each stage changed how results are matched, and the last one changed who runs the process.
Keyword Search
Keyword search matches the exact words in your query to words on a page. It is fast and predictable, but it misses synonyms and intent.
Ranking functions like BM25 weigh how often a term appears, how rare it is, and how long the document is. As the OpenSearch BM25 keyword search docs put it, "Term frequency stipulates that documents in which the search term appears more frequently are more relevant."
Semantic Search
Semantic search matches meaning instead of exact words. It turns queries and pages into embeddings, which are lists of numbers that capture meaning, and finds the closest matches. You can read more about how semantic search works. It is smarter about intent, but it is still one query in and one ranked list out.
Agentic Search
Agentic search puts a large language model (LLM) in charge of the process. The model decides what to search, which pages to read, when to dig deeper and when to stop. The main change is that the search loop itself is now automated.
How Agentic Search Works, Step by Step
This article breaks the agentic search process into a four-step loop. It mirrors how Anthropic describes its research agents, whose subagents "evaluate quality, identify gaps, and refine their next query" before a citation step attaches sources. To make it concrete, picture this question: "Which open-source vector databases added hybrid search in the last 12 months, and what licenses do they use?"
A single search will not answer that. The agent has to find candidates, check each one, and confirm dates and licenses across several sources.
1. Plan and Break Down the Question
First, the agent splits the goal into smaller sub-questions. For our example, it might search for a list of open-source vector databases, then for release notes about hybrid search, then for each project's license.
Google uses a version of this in its AI Mode. In Google's query fan-out announcement, the company wrote: "Under the hood, AI Mode uses our query fan-out technique, breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf." Writing good sub-queries is a skill of its own, covered in our guide to query expansion for AI agents.
2. Search and Read Full Pages
Next, the agent runs the searches and opens the most promising results. Search snippets are usually too short to confirm a release date or a license, so the agent needs the full page. Many of those pages are modern, JavaScript-heavy sites.
The agent works best when each page arrives as clean Markdown or JSON, not raw HTML. That is the job of a scraping layer such as the Olostep Scrapes endpoint, which can return content from publicly accessible pages in LLM-ready formats such as Markdown.
3. Evaluate, Find Gaps and Search Again
Now the agent checks its evidence against each sub-question. Did it find a license for each project? Do two sources give different release dates?
If something is missing or conflicting, it writes new queries and goes back to step 2.
This is the step traditional search leaves to people. It is also where agentic search can gain accuracy, and where much of its cost comes from.
4. Synthesize an Answer With Sources
Finally, the agent combines what it found into an answer. In production, that answer is often structured data, like a JSON list of databases, dates and licenses, with a source URL for each fact. Source links let a person or a downstream system check each claim.
Agentic Search vs Traditional Search: Side-by-Side Comparison
Traditional search returns links for a person to read, while agentic search returns an answer that an agent assembled from many sources.
| Traditional Search | Agentic Search | |
|---|---|---|
| Who drives the search | A person | An AI agent |
| Input | A short keyword query | A goal or complex question |
| Number of queries | One per attempt | Many, often in parallel |
| What gets read | Titles and snippets | Full page content |
| Output | A ranked list of links | An answer or structured data with sources |
| Typical speed | Near-instant, one round trip | Slower, many round trips |
| Cost per question | Low | Higher, due to many searches and LLM calls |
| Best for | Simple facts, navigation, quick lookups | Multi-part research, cross-checking, extraction |
| Main failure mode | Relevant answer is buried or missing | Loops, weak sources, or confident wrong answers |
What the Evidence Says About Agentic Search
Published benchmarks and usage data point to two findings: search strategy matters more than browser access, and AI summaries are changing click behavior. Most of the figures below come from the companies that build these systems, so read them as reported results for specific setups.
Strategy Matters More Than Browser Access
OpenAI built BrowseComp, a set of hard questions designed so the answers do not appear on the first pages of search results. In OpenAI's BrowseComp benchmark, the company reported: "Enabling browsing for GPT‑4o led to a modest improvement in accuracy (from 0.6% to 1.9%), but performance remained low." OpenAI's Deep Research agent scored 51.5%, though OpenAI notes it was trained on data aimed at these kinds of tasks.
For builders, the lesson is that a search tool alone does not make a model agentic. OpenAI concluded that browsing alone is not sufficient; the model also has to plan search paths and interpret what it finds.
AI Summaries Are Changing Click Behavior
People are also changing how they use search. In a Pew Research click study, an observational analysis of the web browsing data of 900 U.S. adults in March 2025 that counted only Google searches, Pew found: "Users who encountered an AI summary clicked on a traditional search result link in 8% of all visits. Those who did not encounter an AI summary clicked on a search result nearly twice as often (15% of visits)."
Shopping data shows a similar pattern. According to vendor survey and platform data about shopping in the Salesforce State of Commerce report, "Use of agentic search as the first step in the shopping journey grew 200% year over year." The two figures measure different things, but they point in the same direction: fewer people start or finish with a list of links.
The Trade-Offs: Cost, Latency and Failure Modes
Agentic search can answer harder questions, but every extra search, page read and model call adds cost and time.
- Cost: Writing about Anthropic's multi-agent research system, Anthropic reported: "In our data, agents typically use about 4× more tokens than chat interactions, and multi-agent systems use about 15× more tokens than chats." Anthropic adds that this only pays off for tasks valuable enough to cover the extra spend.
- Latency: A single ranked search returns almost instantly, while an agent that plans, reads and searches again makes many round trips and takes noticeably longer. Our guide to search depth versus latency covers how to tune this.
- Runaway loops: Without clear stop rules, an agent can keep searching long after it has enough evidence.
- Weak or stale sources: The agent is only as good as what it reads. Outdated pages and low-quality sites can lead to confident wrong answers.
- Pages that are hard to read: JavaScript-heavy pages, blocked pages and messy HTML can leave gaps in the evidence.
- Hidden instructions: Retrieved pages can contain text meant to steer the model, so treat web content as untrusted input.
When to Use Agentic Search vs Traditional Search
The right choice depends on the question. Production systems can use both and route each query to the cheaper option that can still answer it.
Use traditional search when:
- Navigation: The user wants a specific site or page.
- Simple facts: One good source answers the question.
- Speed matters most: The response has to come back almost instantly.
- High volume: You run many similar lookups and need low cost per query.
Use agentic search when:
- Multi-part questions: The answer depends on several facts from different sources.
- Cross-checking: You need to confirm details across sources before acting.
- Broad research: The first results are incomplete or the topic is wide.
- Structured extraction: You need a clean table or JSON output, not a list of links.
One practical hybrid: try one search first, and escalate to the agentic loop only when the first results cannot answer the question.
What AI Agents Need to Search the Live Web
An agent's answers can only be as good as its access to the web. Each step of the loop needs a specific capability:
- Discovery: Search results for many sub-queries, run in parallel.
- Full page content: The complete text of each result, including modern and JavaScript-heavy sites.
- Clean formats: Markdown or JSON that an LLM can read without extra parsing.
- Freshness: Live pages, not stale copies, for anything that changes.
- Sources: A URL attached to every claim so answers can be checked.
Building this yourself means running browsers, proxies, parsers and retries. Olostep provides these pieces through one API.
The AI web search API handles discovery and can return page content with results. The Olostep Answers endpoint runs the full loop in one call: it searches, reads publicly accessible pages, and returns a structured answer with sources.
FAQ
What Does Agentic Search Mean?
Agentic search is search run by an AI agent that plans queries, reads results, finds gaps and searches again until it can answer a goal with sources.
Is Agentic Search the Same as Agentic RAG?
Closely related: the IBM agentic RAG explainer defines agentic RAG as "the use of AI agents to facilitate retrieval augmented generation (RAG)." In this article, we use agentic search for the open-web case and agentic RAG for retrieval that grounds a model's answer, often over private documents.
Is ChatGPT an Example of Agentic Search?
It can be. In OpenAI's deep research launch, OpenAI says ChatGPT will "find, analyze, and synthesize hundreds of online sources" in a multi-step research process, which is agentic search.
What Is Query Fan-Out?
Query fan-out is when a system splits one question into many smaller queries and runs them at the same time. Google uses this technique in AI Mode.
Will Agentic Search Replace Traditional Search?
Not fully, because agents use traditional search as a building block. Simple lookups will stay faster and cheaper with a single query.
Is Agentic Search More Expensive?
Usually yes, per question, because it runs more searches and reads more content. It is worth the cost when the question is complex and the answer is valuable.