Definition
Agentic Search is search performed by an AI agent that can plan, browse, use tools, compare sources, remember context, and take steps toward a user goal. Instead of answering a single query, the agent may decompose the task, run multiple searches, open pages, evaluate options, fill forms, and return a recommendation or completed workflow.
Agentic search appears in AI browsers, deep search tools, shopping assistants, coding agents, and enterprise copilots. It changes GEO because the unit of visibility is not only an answer citation; it can be inclusion in an agent's research path, shortlist, comparison table, or action sequence.
Optimizing for agentic search means making pages easy for agents to understand and act on. Clear pricing, policies, product specs, documentation, structured data, accessible HTML, and transparent trust signals matter because agents need to compare and execute, not just summarize. This overlaps with agent experience optimization and agentic commerce.
Agentic search also changes attribution. A user may never see every source the agent consulted, and the eventual conversion may appear as direct or branded traffic, a form of zero-click attribution. Our explainer on what agentic search is covers the practical implications.
Examples of Agentic Search
- An AI browser researches accounting software, reads pricing pages, checks reviews, compares integrations, and recommends three vendors without the user opening every source.
- A shopping agent filters products by budget, availability, return policy, and reviews before sending the user to one product page.
- A coding agent searches documentation, Stack Overflow, GitHub issues, and changelogs before applying a fix in a repository.
- A GEO team tests agentic search by asking AI browsers to complete real buyer tasks and observing which sources enter the shortlist.
