What does agentic search mean?

There is no single official definition, and the term is used in two ways. OpenAI's API docs use it for search run by a reasoning model: "Agentic search with reasoning models is an approach where the model actively manages the search process" (OpenAI web search guide, read 1 November 2026). The model searches as part of its reasoning, looks at what came back and decides whether to keep going.

Others use the term more widely, for AI agents that search and then do something with what they find: compare options, fill in forms or buy. Google said in May 2025 it was "bringing the agentic capabilities of Project Mariner into AI Mode", with buying event tickets as the example (Google). Perplexity calls its Comet browser "the AI browser that acts as a personal assistant" (Perplexity).

How does agentic search work?

  1. The assistant splits the question into parts. Google calls this query fan-out: "breaking down your question into subtopics and issuing a multitude of queries simultaneously" (Google).
  2. It runs the searches and reads the pages that come back.
  3. It checks whether it has enough, and searches again if not. OpenAI says its deep research models "can find, analyze, and synthesize hundreds of sources" (OpenAI).
  4. It writes the answer with cited sources or, in agent products, takes the next step for the user.

Anthropic describes the same loop for Claude: Claude decides when to search, and the process "can repeat multiple times throughout a single request" before Claude gives a final answer with cited sources (Claude docs, read 1 November 2026).

Why does agentic search matter for B2B SaaS?

Software buyers ask questions with many parts, such as a CRM for 15 people that syncs with Outlook and fits a budget. An agentic system turns that into several searches and can read more pages than a person would. Each search is a new chance to be found, or missed.

Two things follow. Your facts must be easy to find and check on pages an agent can read: what you do, who it is for, integrations, and your price or how to get one. And the third-party pages the agent reads count as much as your own. Foundation and AirOps looked at 5.1 million AI responses for 50 B2B brands. On category questions that did not name the brand, only 2.24% of citations went to the brand's own pages.

What does agentic search look like? (hypothetical example)

This example is hypothetical. A sales lead asks an assistant: "Find a CRM for a 15-person B2B team that syncs with Outlook and costs under $40 a user." The agent searches for CRM lists, then each tool's pricing page, then each tool's Outlook integration docs. It cannot confirm the price for two tools, so it leaves them out and returns a shortlist of three, with links. A tool that is missing from the lists it read never reaches that shortlist.

Agentic search vs AI search vs AI agents

  • AI search: an assistant answers from search results, sometimes after one search. Google's AI Mode is one example.
  • Agentic search: the model runs a loop of several searches and decides when it has enough.
  • An AI agent is software that takes actions for a user. Search is often one step of the job.
  • Agentic commerce is agents that compare products and buy them.
  • Be indexed. Google says "There are no additional requirements to appear in AI Overviews or AI Mode": a page must be indexed and eligible for a snippet (Google Search Central, read 1 November 2026).
  • Let search crawlers in. OpenAI says sites opted out of OAI-SearchBot "will not be shown in ChatGPT search answers" (OpenAI).
  • Make your own pages machine-friendly with plain facts. Microsoft's open NLWeb project goes further: "Every NLWeb instance is also a Model Context Protocol (MCP) server", so agents can query the site (Microsoft, 19 May 2025).
  • Get into the third-party lists and reviews that agents read, and keep those entries current.

The LLM SEO guide covers how B2B SaaS brands get named in AI answers, step by step.