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. It is what agentic AI, autonomous systems that pursue a goal across multiple steps, looks like when applied to information retrieval. 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 began rolling out on 12 June 2026 to Google AI Ultra subscribers, and Google’s Robby Stein said at the time that they were available in all AI Mode languages and markets. Expansion to Google AI Pro subscribers was promised for later in the summer and had not been confirmed as of late August 2026.¹²³

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 paper (January 2026) is widely cited in SEO coverage as evidence about how research agents behave.⁴ What it actually measures is narrower than that use of it implies. SAGE is a pipeline for generating synthetic training questions for deep search agents. The four percentages that circulate come from its Table 6, an error categorisation of questions the pipeline’s own generator produced that turned out too easy, sampled over 100 trajectories against a fixed Wikipedia-based corpus:⁵

• Information co-location (35%): the pieces of information needed to answer the question sat in the same document in the corpus, so the agent resolved it in one hop.
• Overly specific questions (31%): the question carried enough detail that the answer surfaced without further retrieval.
• Multi-query collapse (21%): a single query retrieved material from several documents at once.
• Superficial complexity (13%): questions that looked multi-step but resolved directly.

These describe why a generated question failed to require multi-step research over a fixed encyclopedia. They are not a measurement of how often a production agent takes a shortcut on the open web. The mechanism is real and the design implication is defensible on its own terms, that a page resolving several of a topic’s sub-questions gives an agent less reason to go elsewhere for the missing pieces, but SAGE does not establish how often that happens in practice.

The same caution applies to a claim repeated widely alongside it: that agents draw from the top three ranked pages for each sub-query. The paper’s transfer experiment swapped Wikipedia retrieval for Google Search via the Serper API, “retrieving the top three snippets per search query”. That is a retrieval depth the researchers configured, not a behaviour they observed.⁵ Ranking still governs whether an agent reaches a page, for the ordinary reason that an agent running a web search sees ranked results. The number three is not a finding.

How did ChatGPT change its fan-out in August 2026?

The largest observed change to agentic retrieval in 2026 runs against the consolidation logic above.

With the GPT-5.6 rollout in early August 2026, ChatGPT began searching considerably more per answer, not less. Chris Long of Nectiv re-ran roughly 4,000 prompts against a 2025 baseline and found fan-out queries per prompt rising from 2.17 to 7.61. Peec AI, comparing 180 million sources across one million tracked prompts either side of the rollout, put sources cited per chat at 25.55 against 12.48 before. Both are single-panel measurements from AI-visibility companies rather than platform figures, and they agree on direction.

Two things follow. Agents are multi-hopping more, so a consolidated page competes inside a larger citation pool rather than shortening the agent’s path. And the searches themselves changed shape: the site: operator appeared in 64% of Long’s fan-out queries, having been near-absent before. A domain-scoped search can only reach a domain the model already knows to name, which puts being a recognised entity upstream of ranking for that share of retrieval. Both shifts are covered in full in ChatGPT Now Cites Twice as Many Sources and in query fan-out.

Microsoft’s Web IQ, announced at Build 2026, is the first publicly named production implementation of AI-native grounding infrastructure built specifically for this retrieval pattern.⁶ Microsoft’s President of Search and AI has said it is used directly in Copilot and by ChatGPT “for some of its web answers”,⁷ so it carries a substantial share of that retrieval rather than all of it. It returns passage-level evidence objects rather than full pages, and assesses retrieval quality against GDSAT (grounding satisfaction), a metric covering completeness, freshness, and authority. A passage that scores poorly on any dimension can be excluded regardless of where its parent page ranks.

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 see monitoring 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. Some AI visibility is now measurable, through Search Console’s Search Generative AI performance report and third-party citation tracking, covered in AI visibility measurement. None of it is scoped to monitoring agents, so this specific gap remains open.

These are documented directional changes from Google’s I/O 2026 announcements. The scale is limited (Google AI Ultra subscribers at launch, with AI Pro still to follow) 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.

Automated traffic already accounts for the majority of global web requests: by early 2025, non-AI bots alone were responsible for roughly half of all requests to HTML pages, seven percentage points above human-generated traffic, with AI bots adding further to that share.⁸ The 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 a browser JavaScript API (document.modelContext; the original navigator.modelContext was deprecated in Chrome 150 and retained only as a backward-compatible alias). 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 covered separately in Agentic SEO.

“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.⁹ 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 Search Rolls Out Information Agents In AI Mode For Google AI Ultra Subscribers — Search Engine Roundtable ↩

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

5. SAGE: Steerable Agentic Data Generation for Deep Search with Execution Feedback — Findings of the ACL: EACL 2026 (Table 6 error categorisation; Serper top-three-snippets configuration in the Google Search transfer experiment) ↩ ↩²

6. Announcing Microsoft Web IQ — Bing Search Blog ↩

7. Microsoft Web IQ (Microsoft names “AI platforms like Copilot and OpenAI”), with Jordi Ribas, President of Search and AI, quoted in Microsoft releases Web IQ, powered by Bing but designed for how AI-agents search — Search Engine Land ↩

8. Radar 2025 Year in Review — Cloudflare ↩

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