Definition
Agentic searchAgentic search is when an AI system runs its own research loop instead of a single search. It plans queries, reads the results, decides what's still missing, and searches again until it can answer. Deep research modes in ChatGPT, Gemini, Perplexity, and Claude work this way, and they read far more pages than one search returns.
How Searchable measures this
For prompts run on ChatGPT, Gemini, Claude, Grok, and DeepSeek, Searchable's response history shows the search queries the engine actually ran, as well as the prompt you track, next to the sources it read and cited. That shows which follow-up searches find your pages, and which find a competitor's instead.
How agentic search works
A traditional search engine takes one query and returns one list of results. Agentic search treats the question as a task. An AI agent works through it in a loop:
- It breaks the question into smaller questions it needs to answer.
- It writes and runs searches for those questions.
- It reads the full pages that come back.
- It decides what's missing or contradictory, and searches again.
- It stops when it has enough, then writes an answer with citations.
The loop is what makes it agentic. The model makes decisions along the way instead of following a fixed retrieval step.
Agentic search vs traditional search and RAG
| Traditional search | Standard RAG | Agentic search | |
|---|---|---|---|
| Searches per question | One | Usually one | Many, decided as it goes |
| Who picks the queries | The user | The system, once | The model, repeatedly |
| Reads full pages | No, the user does | Retrieved passages | Yes, and follows up |
| Output | A list of links | An answer | An answer or report |
Retrieval-augmented generation is the base that agentic search builds on. The difference is who controls the retrieval and how many rounds it runs.
Agentic search vs query fan-out
Query fan-out splits one question into several related searches and runs them at the same time. Google's AI features documentation says AI Overviews and AI Mode may use it, "issuing multiple related searches across subtopics and data sources."
Fan-out is usually one round. Agentic search can include fan-out, then looks at what came back and decides whether to search again. In practice the line is blurry, and most AI engines now do some of both.
How much AI engines research before answering
Engines differ a lot in how often they search, and in how much they read when they do. In Searchable's July 2026 test of 1,374 prompts, ChatGPT searched on 89% of them and Claude on 48%. When Claude searched, it read about seven pages and cited about three, according to the Claude Lite study.
Research depth also changes overnight. In Searchable's research depth study across more than 5,000 tracked brands, pages ChatGPT showed it had consulted per answer went from 11.6 to 22.5 on August 8, 2026. Its sub-queries using a site: operator to search one named domain went from 0.2% to 18%.
What agentic search means for brands
An engine that reads more pages and runs follow-up searches changes what it takes to show up:
- Being read isn't the same as being cited. The engine may read 20 pages and credit only a few, so track sources and citations separately.
- Follow-up questions need answers too. An agent researching your category will look for pricing and comparisons. If your site doesn't answer them, it'll use someone else's page.
- Your own domain gets searched directly. When an engine runs a
site:search against your domain, it's looking for a specific fact. Clear pages for each key fact make it easy to find.
Consistency matters more as engines check more sources. If your pricing page and a review site disagree, an agent comparing them has to pick one.
Frequently asked questions
Standard retrieval-augmented generation (RAG) retrieves once and then writes. Agentic search puts the model in charge of retrieval: it decides what to search and searches again if the results aren't enough. This is sometimes called agentic RAG.
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