Agentic search – when AI searches, plans and acts for you

Definition

Agentic search refers to AI systems that do not just answer questions but plan and carry out multi-step tasks on their own – such as comparing, booking or summarising – without the user having to steer them.

From search to action

Traditional search is an exchange: you ask a question and the search engine returns a list of results. Conversational AI such as ChatGPT took the next step by phrasing direct answers. Agentic search goes further still: instead of firing off a single query, the system runs a series of coordinated searches one after another, evaluating the results as it goes, spotting gaps in the information and rephrasing the next query based on what it has found – until the task is done. The end result is something you can act on, not a list of links.

How agentic search works

At its core is an iterative search loop: each round of results shapes the next query. The agent examines what each set of results contains, decides what is missing and adjusts the next searches automatically – without anyone steering it. Technically, an LLM is combined with tools (web search, code execution, form interaction, API calls) and a planning capability that breaks complex tasks down into sub-goals. It is this self-correcting perception–reasoning–action loop that sets agentic search apart from a simple RAG pipeline or a chatbot answer. For a more technical explanation, see Firecrawl's walkthrough of agentic search.

Examples of agentic search in practice

• Google AI Mode – Uses multi-step reasoning to break complex questions down into sub-questions and synthesise answers from several sources in a single session.
• Perplexity Deep Research – Runs automated research tasks: it searches, reads, summarises and presents structured reports with sources cited.
• OpenAI Operator & Deep Research – Can navigate websites, fill in forms and carry out tasks directly in the browser on the user's behalf.
• Microsoft Copilot Actions – Integrates agentic search into workflows such as email management, calendar bookings and document summaries.
• Azure AI Search / OpenSearch – Offers enterprise-level agentic retrieval for conversational search experiences inside organisations.

Agentic Engine Optimization – when AEO takes on a new meaning

In April 2026 Addy Osmani, an engineering director at Google Cloud AI, published an article on what he calls Agentic Engine Optimization – using the abbreviation AEO in a new sense. Here, AEO means structuring and serving technical content so that AI agents can use it, not just render it. The distinction matters: optimising for agentic search is no longer just about appearing as a source in an AI answer, but about your content being genuinely usable by machines – something an agent can fetch, interpret and act on. According to Search Engine Land, Google's recommendations overlap with established AEO practice: shorter pages, clear semantic signals and machine-readable formats.

What agentic search demands of your content

When an AI agent completes a buying journey, compares suppliers or summarises a topic, there is no longer any guarantee that a human ever sees your page – the agent consumes the content and presents a conclusion. That makes the following factors especially critical:

• The token economy – Token count is now a concrete optimisation factor. Agents reward pages that get to the point quickly.
• Machine-readable content – Structured data, schema markup and clear HTML semantics make it easier for agents to extract correct information.
• Fact-rich, sourced text – Agents prioritise content they can verify and cite. Claims without a source risk being filtered out.
• Entity strength – A well-defined entity profile helps the agent connect the right information to the right brand, product or person.
• Direct answer format – Short, precise answers to factual questions are easier to cite than long lines of reasoning without a clear conclusion.

Brands without AI visibility lose business

In traditional search, a low ranking can be offset by the user scrolling further down. In agentic search there is no page 2 – the agent picks one or a few sources and acts on them. If AI finds your competitor easier to cite, you risk being left out of the decision altogether. The same logic drives AEO work: if an AI lacks information about your product, it tends to answer "no" rather than "I don't know" – and you lose the customer without ever finding out.