Agentic search is AI-driven search in which an agent plans queries, browses and reads many sources, and may act on results, instead of returning a link list.
What is Agentic Search?
Agentic search is search performed by an AI agent that plans a series of queries, browses and reads multiple sources, evaluates what it found, searches again to fill gaps and sometimes takes actions, then delivers a synthesized result. The user delegates a research or task goal rather than reading results themselves. It extends AI search from one-shot answers to multi-step investigation.
The term became common in 2025 as vendors shipped products built on this loop. OpenAI launched deep research in ChatGPT in February 2025 and combined it with Operator’s browsing in ChatGPT agent in July 2025. Google described Deep Search in AI Mode, able to run hundreds of searches for a cited report, and agentic task features built on Project Mariner. Perplexity’s Comet browser and other AI browsers added agent modes that navigate websites directly.
How it works
- Goal intake. The user states a goal, such as “shortlist three SIEM vendors under a mid-market budget that support on-prem log collection.”
- Planning. The agent breaks the goal into sub-questions and search queries.
- Iterative retrieval. It searches, opens pages, reads documentation and pricing, and notes gaps.
- Reflection. It decides whether it has enough evidence or needs more searches.
- Action (optional). Some agents fill in forms, book appointments or add items to a cart, usually pausing for user confirmation.
- Report. It returns a summary with citations, a comparison table or a completed task.
This loop is closely related to query fan-out, but runs over many rounds and can change direction based on what it reads.
From SEO to AEO and GEO
Classic SEO assumed a human scanned a results page and clicked. AEO and GEO assume an AI answer sits between the user and your page. Agentic search goes one step further: the AI not only answers but does the research, visiting many pages, including deep ones like pricing, docs and changelogs.
| Model | Who reads your page | What wins |
|---|---|---|
| Classic SEO | A human after clicking | Rankings, persuasive copy |
| AEO / GEO | An AI composing an answer | Quotable, corroborated passages |
| Agentic search | An AI agent doing a task | Complete, structured, accessible facts |
What carries over: crawlability, clear information architecture and accurate content. What changed: agents read pages humans rarely reach, so gaps or contradictions on a pricing or integration page can remove you from a shortlist without anyone visiting your homepage.
What to do about it
- Make decision facts machine-readable. Put plan names, limits, integrations, deployment options and compliance certifications in HTML text and tables, not only in images or PDFs.
- Reduce gated essentials. Agents usually cannot fill in “contact us for pricing” forms. If you do not publish prices, state your pricing model plainly.
- Keep docs public and current. Technical buyers’ agents read documentation to verify capabilities.
- Check server logs. Look for agent and assistant user agents, and make sure bot protection is not blocking the ones you want.
- Test with agents yourself. Give a deep research tool a realistic buyer brief and read how it describes you.
Common mistakes
- Optimizing only the homepage. Agents go deep; a stale integrations page can cost a shortlist slot.
- Blocking all automated traffic. Overly aggressive bot rules can stop legitimate assistant visits.
- Assuming agent actions are mainstream. Many agentic features remain in preview or limited to paid tiers; plan for growth, not universality.
Related terms
- AI Agent
- Agentic Commerce
- Query Fan-Out
- AI Mode
- Model Context Protocol (MCP)
- Guide: How AI engines cite sources
Sources
- AI in Search: Going beyond information to intelligence, Google (May 20, 2025)
- Overview of OpenAI crawlers, OpenAI
- Building effective agents, Anthropic (Dec 2024)
- Comet browser, Perplexity
Frequently asked questions
How is agentic search different from AI search?
Standard AI search answers one prompt with one retrieval round. Agentic search runs a loop: the agent plans, searches, reads, decides what is missing and searches again, sometimes dozens or hundreds of times, and may fill in forms or compare options before reporting. It is slower but more thorough.
Do AI agents respect robots.txt?
It depends on the agent. Crawlers used for indexing, such as OAI-SearchBot, respect robots.txt. Agents acting on a specific user's request, such as ChatGPT-User, may not, according to OpenAI's documentation. Some agents browse with a standard browser user agent, which makes them hard to distinguish from people.
How should B2B websites prepare for agentic search?
Publish the facts agents need in plain, crawlable HTML: pricing model, plan limits, integrations, security certifications and documentation. Avoid hiding key details behind forms or heavy scripts. Clear tables and consistent facts across pages reduce the chance an agent reports something wrong.
Guides that cover Agentic Search
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Generative Engine OptimizationHow Cybersecurity Companies Can Dominate AI Search for Compliance Terms
Stop chasing blue links. Learn how to optimize your cybersecurity compliance content for AI search engines using Generative Engine Optimization (GEO) tactics.
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Agentic search optimization for LLM-based answer engines
Agentic search optimization structures content so multi-step AI agents can find, verify and cite it: crawlable pages, explicit facts and machine-readable data.
