Agentic Search
Agentic search is a search paradigm in which an AI agent breaks a single question into smaller sub-tasks and then plans, searches, and verifies results in a loop to construct an answer on its own. Unlike traditional search, which returns a list of documents matching a query in one pass, it cross-checks multiple sources and reasons about what to look for next, assembling the answer step by step.
- Agentic search is a search paradigm in which an AI agent decomposes a question into sub-tasks and then iterates through planning, searching, and verification to build an answer.
- Unlike traditional search, which returns a list of documents from a single query, the agent reads results, judges what is missing, and decides its next search on its own.
- Features such as ChatGPT Deep Research, Perplexity, and Google's AI Mode, which compose answers by cross-checking multiple sources, are the canonical examples.
- According to Google's SAGE research, an agent takes an average of 4.9 steps per query to search, compare, and evaluate.
- Because consistency and depth across many sources matter more than any single ranking position, content strategy needs to be rethought from a GEO perspective.
What Is Agentic Search
Agentic search is a search paradigm in which an AI agent retrieves and evaluates information on the user's behalf and, when needed, even takes action. The key point is that it does not stop after a single query. The agent splits a given goal into several sub-tasks, then searches, reads the results, reasons about what is still missing, and searches again, repeating this cycle until it has gathered enough evidence.
The emphasis here is not on how eloquently the model phrases an answer, but on the fact that it performs the act of searching itself across multiple stages. The broader notions of AI agents and the agentic web encompass a wide range of task execution such as booking, payments, and code execution, but agentic search is the subset focused specifically on the search behavior of finding and verifying information.
Traditional Search vs. Agentic Search
| Dimension | Traditional Search | Agentic Search |
|---|---|---|
| Processing | One query → one set of results returned | Goal decomposed into sub-tasks → multi-step iteration |
| Search flow | One-shot search that ends in a single pass | A loop of searching, reading, reasoning, and re-searching |
| Output | A list of links and documents (ranked) | A synthesized answer cross-verified across multiple sources |
| User's role | The user visits and judges directly | The agent handles comparison, verification, and judgment |
| Verification | Verification is left to the user | The agent cross-checks contradictions between sources |
| Representative examples | Keyword search on Google or Naver | ChatGPT Deep Research, Perplexity, Google AI Mode |
How It Works and the Evidence
Academically, agentic search stands in contrast to traditional one-shot RAG (retrieval-augmented generation). RAG typically performs a single fixed retrieval before generation, whereas agentic search uses a cycle of plan → retrieve → reflect → iterate to decide, through real-time reasoning, when, what, and how to search. A survey paper on reinforcement learning-based agentic search (arXiv:2510.16724) explains that this approach endows LLMs with the ability to plan, search, and reflect through multi-step interaction.
This multi-step nature is also borne out by real behavioral data. According to the Google SAGE research cited by Semrush, an agent takes an average of 4.9 steps to handle a single query, searching, comparing, and evaluating across multiple sources. In an example highlighted by Backlinko, Perplexity examined 15 sources, including the company's own site and third-party verification bodies, to confirm a brand's sustainability claims.
Representative implementations include ChatGPT's Deep Research, Perplexity, Google AI Mode, and Microsoft Copilot. These visit dozens of sources and compose answers over minutes rather than seconds, and some extend to action steps such as booking and payment. Perplexity has also released a 'Search as Code' architecture, in which the agent writes its own Python scripts to assemble a search pipeline, along with an Agent API.
SEO and GEO Implications
Because agentic search changes how content gets surfaced, it calls for a response framed around GEO (generative engine optimization). The key shifts are as follows.
- Any single ranking position carries less weight. Agents deliberately pull in a diverse set of sources, so no one ranking slot dictates the search.
- Depth and breadth matter at the same time. An LLM will read a 45-page explanation without tiring, so content depth becomes a competitive edge; at the same time, passing third-party verification and trust assessment requires consistent mentions elsewhere (breadth).
- Accessibility requirements change. Core information such as pricing, FAQs, and forms must exist as plain HTML rather than being hidden behind JavaScript interactions, so that agents can read it.
- Information consistency across sources matters. If the information on review platforms, directories, and your own site contradicts itself, you can be excluded from agent recommendations, so structured data and entity clarity act as signals.
Semrush proposes the axes that govern agent visibility as brand discovery (when an agent searches our category, are we included in the answer), brand clarity (when compared against competitors, is the information accurate and backed by external sources), brand authority (is there evidence across independent sources to support a recommendation), and brand trust (is it possible when an agent attempts a real action). The biggest risk is being filtered out before a human ever sees you.