Agentic search

Most AI search discussion focuses on single-query retrieval: a user asks a question, an AI system fetches relevant passages, and an answer is generated. Agentic search works differently, and it is not a single thing. Research agents break complex queries into sequences of sub-searches, visit multiple sources, check for contradictions, and build answers iteratively. Monitoring agents run continuously in the background, scanning the web on a user’s behalf without waiting to be asked. Understanding both categories, and how they differ, explains what content structure supports each. For context on what AI agents are and how they differ from AI assistants, see What are AI agents?.

Agent readiness, the infrastructure layer that determines whether AI agents can discover and act on a site, is a prerequisite: well-structured content that agents cannot access provides no retrieval benefit.

What is agentic search?

Agentic search systems fall into two distinct categories, each with different implications for content and visibility.

Research-mode agents handle complex, user-initiated queries that require multiple retrieval passes. Perplexity Deep Research, ChatGPT’s research mode, and Google’s own research features fall into this category. A user submits a question; the agent breaks it into sub-tasks, runs a search for each, cross-references findings, and synthesises a comprehensive answer.

Monitoring agents are different in kind. They run continuously in the background without a user-initiated query, scanning blogs, news, social posts, and real-time data on the user’s behalf. When conditions match the user’s criteria, the agent synthesises a relevant update and notifies the user. The user never typed a search; the agent did it for them.

Google Information Agents, announced at Google I/O 2026, are the most significant deployment of the second category to date. Users set up agents by describing their monitoring criteria (an apartment hunt with specific requirements, a price threshold on a product, a company they follow) and the agent scans continuously, delivering synthesised updates when relevant changes are detected. Information Agents launch in summer 2026 for Google AI Pro and Ultra subscribers, running on Gemini 3.5 Flash.¹²

Both categories are distinct from standard AI Overview or RAG-based retrieval, where a single query produces a single retrieval pass. Agentic systems run several retrieval passes in sequence, each informed by the results of the previous one.

How do agents retrieve content differently?

In a standard AI search retrieval, the system fetches the passages most relevant to a query and generates a response from them. An agentic system adds two layers: planning and cross-referencing.

The agent first breaks the original query into sub-questions. It then runs a search for each sub-question, evaluating the results before deciding whether to search further or proceed to synthesis. If two sources contradict each other on a factual point, the agent may run an additional search to resolve the conflict.

Google’s SAGE research (January 2026) identified four patterns that allow deep research tasks to resolve more quickly than expected, in cases where agents avoid the full multi-step retrieval process:³

• Information co-location (35%): two or more sub-answers are present in a single document. The agent resolves the question in one hop rather than searching separately for each piece.
• Overly specific questions (31%): the query contains enough detail that the answer surfaces directly from the first result, without further retrieval.
• Multi-query collapse (21%): a single search retrieves material that resolves multiple sub-questions simultaneously.
• Superficial complexity (13%): questions that appear multi-step but resolve directly without intermediate reasoning.

Information co-location is the single most common shortcut an agent takes. A page that consolidates the sub-answers to a topic’s main questions gives the agent less reason to visit competitors for the missing pieces.

The same research found that agents typically draw from the top three ranked pages for each sub-query they execute. Traditional search ranking remains a prerequisite for agent retrieval. A page that does not rank is unlikely to be reached by an agent, regardless of how well its content is structured.³

What do information agents mean for search visibility?

Research-mode agents respond to a user who searched. Monitoring agents represent a different mechanic: the user may never initiate a search, never see a results page, and never visit a site. The agent scans, synthesises, and notifies. The relevant outcome is whether your content was included in that synthesised update, not whether it earned a click.

Several things change with this model.

Freshness is evaluated over time, not at a ranking snapshot. A monitoring agent returns to sources repeatedly. Content that was accurate when first indexed but has since become stale (outdated statistics, lapsed product details, old pricing) gets routed around in favour of sources that reflect the current state. This is a different pressure than the periodic content refreshes that traditional SEO requires.

The click may not happen. Traditional SEO optimises for a click from a ranked result. AI Overview citations already deliver brand visibility without guaranteeing a click. Monitoring agents extend this further: the synthesised notification a user receives may not include a direct link, or may link to a page the user has no reason to visit. Citation visibility is the relevant metric, not click-through rate.

Analytics cannot yet see agents. Current analytics (GA4, Search Console) record sessions from human visitors. They do not record agent evaluations: the passes an agent makes across your content to assess whether it matches a monitoring condition. Traffic data may appear stable while agents are actively using or bypassing your content. Measurement in this space is an open problem with no current solution.

These are documented directional changes from Google’s I/O 2026 announcements. The scale is limited at launch (paid subscribers only, summer 2026) and the long-term traffic implications are not yet established. The direction is confirmed; the magnitude is not.

What does agentic-ready content look like?

Content that performs well in agentic retrieval tends to have two qualities: full topic coverage and clear internal structure.

Full coverage means anticipating the sub-questions a researcher would ask when investigating a topic, going beyond the primary question. A page about a software product that covers pricing, integrations, limitations, and common use cases is more useful to an agent than a page that covers only the headline feature. The agent can resolve multiple sub-questions from one source rather than visiting four separate pages.

Clear internal structure means each sub-topic is addressed in its own section with a direct heading and a direct opening sentence. An agent evaluating whether a section resolves a sub-question does so quickly. Sections that bury the relevant information after several sentences of context are harder to evaluate accurately.

Specific, sourced claims. Agents cross-reference sources and favour content that cites primary evidence: studies, official documentation, original data. Vague claims without attribution are weaker candidates for synthesis.

Structured data as agent infrastructure. Schema markup helps agents identify entities, content types, and relationships without relying solely on parsing prose. Article, FAQPage, Product, and Organization schema reduce ambiguity when agents cross-reference sources and evaluate whether content resolves a specific sub-question. In agentic contexts this matters more than in standard retrieval: an agent synthesising across multiple sources needs to identify what each source is and who produced it, not just whether the text is relevant.

The relationship to GEO and traditional SEO

Agentic SEO is not a separate discipline from GEO or traditional SEO. It extends both.

Traditional SEO creates the ranking that puts content within an agent’s retrieval reach. GEO focuses on passage-level quality so individual sections are selected for citation. Agentic SEO adds the question of whether a page can resolve multiple sub-questions in one place, reducing how much an agent needs to look elsewhere.

The content practices are largely the same across all three: accuracy, clear structure, direct writing, credible sourcing. What differs is the level of analysis. GEO asks whether a passage stands alone. Agentic SEO asks whether a page covers enough of a topic’s sub-questions to be the document an agent stops at, rather than one of many it visits.

AI crawlers have now surpassed traditional search bots in volume, representing over half of all crawler traffic: Cloudflare data shows AI crawlers account for 51.69% of bot traffic, up from a small fraction before the generative AI era.⁴ Total bot traffic across the web stands at roughly a third of HTTP requests, with Cloudflare’s CEO forecasting that bots will surpass human traffic by 2027. The practical implication is not that human visitors matter less, but that content increasingly needs to be interpretable by systems that do not browse, skim, or follow navigational patterns the way humans do. Structured, unambiguous, machine-readable content serves both.

Beyond retrieval: the action layer

The agents described in this article retrieve and synthesise content. A second category of agent does something different: it acts. Rather than reading a page and summarising what it says, an action agent calls a function on a site and completes a task on the user’s behalf.

At Google I/O 2026, Sundar Pichai described Search evolving into an “agent manager”: a system running multiple agent threads simultaneously, handling ongoing tasks rather than responding to individual queries. Google’s agentic booking feature, which places calls to local businesses on a user’s behalf, is an early example. Gemini Spark, announced at the same event, runs continuously on cloud infrastructure and integrates with MCP (Model Context Protocol) for tool access.

Two protocols address the action layer from different angles. WebMCP is a browser-native adaptation of Anthropic’s MCP standard that lets sites declare callable tools (search, availability checks, booking functions) via Chrome’s navigator.modelContext API. NLWeb takes a server-side approach: it converts a site’s existing schema.org structured data into queryable endpoints (/ask for REST queries, /mcp for agent-to-agent communication), making the site’s content accessible to any MCP-compatible agent without custom JavaScript. Where agentic SEO covers the content retrieval layer, both WebMCP and NLWeb cover the action layer. For most informational and editorial sites the action layer is not yet relevant. For e-commerce, travel, booking, and media sites with large content catalogues, it represents the next meaningful infrastructure question.

A note on terminology

“Agentic SEO” is used in two distinct ways in 2026, which is why this article uses “agentic search” instead.

The dominant industry usage — as used by Ahrefs, Search Engine Land, and Siteimprove — defines agentic SEO as using AI agents to execute SEO workflows: autonomous keyword research, content audits, technical fixes, and implementation. That is a distinct topic not covered here.

“Agentic engine optimisation” has also been used in a narrower sense by Google Cloud’s director of engineering, Addy Osmani: structuring developer documentation so AI coding agents can parse and act on it.⁵ This applies to a specific content format, not general web content or search visibility.

This article covers the content retrieval question: how AI research agents find, evaluate, and synthesise content across multi-step tasks, and what content structure supports that process.

Frequently asked questions

Does Agentic SEO affect standard search rankings?
Not directly. Agentic retrieval draws from pages that already rank well for each sub-query the agent runs. Improving topic coverage may improve standard rankings, which indirectly improves agent reach, but there is no separate ranking signal for agent retrieval.

Is this the same as GEO?
Related but distinct. GEO focuses on individual passages being selected as citations in a single-query AI answer. Agentic SEO focuses on a page’s ability to resolve multiple sub-questions in one place, reducing agent multi-hopping. Both reward accurate, well-structured content; the emphasis differs.

Do agents cite sources the same way standard AI search does?
Generally yes: research mode outputs typically include inline citations linked to source pages. Monitoring agents may synthesise updates without citing each source individually. The brand visibility implication differs: a cited source in a research answer is visible to the user; a source used by a monitoring agent may not be attributed in the notification the user receives.

Footnotes

1. Google Search’s I/O 2026 updates: AI agents and more — Google Blog ↩

2. Google Search gains information agents and improved agentic experiences — Search Engine Land ↩

3. Google’s SAGE Agentic AI Research — Search Engine Journal ↩ ↩²

4. ‘Google Zero’ misses the real problem: Your next visitor isn’t human — Search Engine Land ↩

5. Agentic engine optimization — Google AI director — Search Engine Land ↩