How Agentic Browsers Are Changing SEO

For a few years now, digital agencies and brands have been adjusting to the fact that brand discovery and product and service research are increasingly guided by LLMs, in addition to traditional search engines. But with the introduction of agentic browsers, AI agents are stepping further into the funnel, taking over from humans at the conversion stage. 

AI agents are now going beyond browsing web content and using it to synthesise answers to user queries. They are starting to interact with websites on users’ behalf – they can now search for deals under a certain budget, add items to cart, make appointments, restaurant reservations and hotel bookings.

For SEO, this is a shift from visibility to operability. As browsers move from displaying links to executing tasks, traditional ranking factors aren’t becoming obsolete – they are becoming the baseline for a more complex, agent-ready technical architecture.

What Are Agentic Browsers – and Why Do They Matter?

In a traditional browser, you typically use a search bar to land on a web page and interact with it. Many such experiences start on Google or a different search engine, but increasingly more users have AI tools like ChatGPT, Perplexity, or Claude bookmarked, and that’s where the browsing begins. These answer engines can help guide your search or answer your query straight away, without taking you further to any website. But if you need to interact with a web page, you’ll still need to move on there yourself and take action.

An agentic browser may remove the need for that last step. Instead, the user can “send” an AI assistant to visit the page on their behalf and perform the action a human would typically take, from “reading” the page’s content and summarising it for the user to performing more complex actions, like filling out forms and pressing CTA buttons.

But that’s not all. Users can also personalise their AI assistants by allowing agentic browsers access to their browsing history, and in the case of Gemini, also mailboxes, photos and YouTube. As a result, users get more relevant, tailored responses and also don’t have to switch between tabs that much.

So, the key things that set agentic browsers apart from traditional browsers are:

• Autonomy: you can literally delegate some tasks to AI assistants, like checking the menu at 10 different restaurants and giving you a recommendation based on your dietary preferences.
• Multi-tasking: you might be reading a blog on party decoration ideas in the main window, while your AI assistant is coming up with a menu for 10 people and adding groceries to your shopping cart.
• Personalization: your agentic browser can know so much more about you than a traditional browser or an AI tool like ChatGPT would, which means it can help you in so many new ways. For example, your browser can summarise those 5 videos about running shoes that you watched on YouTube last week, select the best shoes for your training purposes and find stores nearby that sell them.

At the same time, you can still use an agentic browser as a regular one and also choose what answer engine to use in it – a traditional one, like Google, or conversational. For example, in Comet, when entering a query, you can choose whether to search via Google or Perplexity, allowing a hybrid approach depending on your needs. You can even set a different search engine as your default, ensuring traditional SERPs remain accessible if desired.

Here is a quick overview of the agentic browsers currently available. 

Perplexity Comet

Perplexity was the first to release its own agentic browser, Comet, back in July 2025. Comet can help automate research workflows, browse autonomously, and continuously refine results as new information emerges. Designed as an AI-first browser, it replaces traditional search-driven interactions with proactive AI assistants that guide users toward outcomes rather than links.

Instead of juggling tools or switching contexts, Comet supports a wide range of tasks, from in-depth research and meeting preparation to coding and e-commerce, directly within the browsing experience. 

ChatGPT Atlas

ChatGPT Atlas was released in October 2025, and as of February 2026, it is only available for macOS. It blends conversational AI with a live web browser. From basic search and YouTube summaries to job hunting and shopping, with the agent mode on, you don’t need to just switch between tabs, but get things done right on the page where you are. 

Built on a Chromium-based framework, Atlas closely resembles the Chrome browsing experience, with tabs and sessions carried over from the same browser profile. On top of that familiar interface, Atlas integrates an AI chatbot that functions as a super-assistant. 

Gemini in Chrome

Chrome evolving into an agentic browser was an expected catch-up move from Google. And indeed, in January 2026, Google started rolling it out to users in the U.S. Similarly to Atlas and Comet, it helps you multitask across the web without switching between tabs. Built on the Gemini 3 model, Chrome’s agentic features let you complete workflows and pull context from multiple web apps. 

Gemini introduces auto-browse functionality that gives AI the freedom to complete tasks on your behalf, such as managing subscriptions, organising a themed party, booking flights, or even shopping on platforms like Shopify and Etsy.  

How This Shift Is Reshaping Search Behaviour

Traditional browsers have shaped how people search by acting as passive gateways to the web. You type a query, scan results, open multiple tabs, and manually piece together information. 

For decades, search behaviour has been built around navigation rather than outcomes. We’re now starting to see signs of the shift in that dynamic. 

By embedding AI directly into the browsing experience, browsers introduce memory, context awareness, and the ability to take action. Instead of moving between pages, users increasingly delegate tasks: researching, summarising, filling forms, or completing workflows without leaving the browser environment.

Personalised Discovery Loops

One of the most powerful features of agentic browsers is their ability to remember context and browsing intent. With this information, Atlas, Comet, and Gemini-powered Chrome can create what we think of as “personalised search conversations.”

Unlike traditional browsers, AI agents can track your actions, preferences, and prior searches to deliver more relevant answers over time. 

For example, if you’re researching the best laptops for video editing, the agent remembers your previous queries about performance, price range, and reviews. Over multiple sessions, it can proactively summarise insights, suggest comparisons, or even surface new products that match your interests, and you don’t have to repeat yourself twice.

However, this persistent memory layer introduces a new complexity. Because agentic browsers retain contextual data, they can also store sensitive information such as your credentials and personal details. 

Malicious actors can exploit this through techniques like memory injection or data poisoning by embedding hidden prompts or manipulated data into webpages that influence what AI remembers and later recalls. 

What happens then is the altered memory that may shape future responses, leak private information, or even enable reidentification of data that was originally anonymised. 

Potential for a Zero-Click User Journey

AI tools like ChatGPT, Perplexity, and Google’s AI overviews and AI Mode have already eroded organic click-through rates. By satisfying queries directly in the SERP or the synthesised response, they’ve commoditised information, often removing the need for a user to ever visit your site.

With the rise of agentic browsers, the traffic that does reach your website is increasingly non-human. Theoretically, the entire user journey, from discovery to conversion, can now happen without a human ever landing on a page.

This has forced a 180-degree turn from industry gatekeepers. Last year, Cloudflare introduced a one-click “block all AI bots” feature as a default defensive measure. Now, they’ve pivoted to launching Markdown for Agents. This tool allows websites to serve a machine-readable version of their content specifically for AI crawlers, effectively acknowledging that blocking bots is no longer a viable strategy for growth.

“As a business, to continue to stay ahead, now is the time to consider not just human visitors, or traditional wisdom for SEO-optimization, but start to treat agents as first-class citizens.”

Cloudflare

From Passive Research to Autonomous Action

Currently, agentic behaviour is still heavily skewed toward the top of the funnel. Data shows that nearly nine in ten agent visits focus on product research, comparison, and recommendation. While bots are excellent at synthesising options, they are not yet prolific buyers – only a small fraction of these interactions currently reach checkout or account-level execution. 

This gap in transactional autonomy is a result of three main friction points:

• Infrastructure: legacy checkout flows are designed for human eyes, not API-driven agents.
• Browser Functionality: tools like Atlas and Comet are still evolving their “hand-off” capabilities.
• User Trust: a lingering reluctance to delegate financial authority (subscriptions and purchases) to an LLM.

However, this “research-only” phase is temporary. As infrastructure catches up, facilitated by developments like Cloudflare’s machine-readable layers and Google’s WebMCP (Web Model Context Protocol), the transition from AI browsing to AI buying will happen rapidly. For agencies, the challenge is no longer just getting a brand “seen” by an LLM, but ensuring the website’s technical architecture allows an agent to complete a transaction without human intervention.

Expert Insight: Agentic Browsers Are The Future Standard

While the technical shift toward agentic browsers is clear, the implications for brand communications and SEO strategy are even more profound. As browsers evolve into agents, traditional habits like scrolling and manual page analysis are being replaced by intent-driven automation. In this clip, SUSO’s Partner Growth Manager, Cara Corbett, and PR expert Andrew Bruce Smith discuss why agentic browsers are set to become the new standard, and how this shift is compressing the funnel – from brand sentiment analysis to autonomous task completion.

Adoption Statistics of Agentic AI

HUMAN has verified over 20 trillion digital interactions, and here’s what they uncovered: 

• Between January and August 2025, agentic traffic increased by more than 1,300%, reaching several million monthly requests. 
• The sharpest acceleration occurred in the second half of the year, coinciding with the commercial launches of ChatGPT Agent and Perplexity’s agentic browser Comet. 
• From July to September alone, total AI agent traffic more than tripled.

What’s notable is not just the volume, but the speed at which agentic leadership shifted. Early adoption was dominated by ChatGPT Agent, which initially accounted for the vast majority of agentic activity. 

Within months, Perplexity’s Comet overtook it, driven by aggressive distribution and its positioning as a full AI-first browser rather than a feature layered onto chat. This rapid turnover highlights how fluid and competitive the agentic AI landscape still is.

Inside ChatGPT Atlas, Perplexity Comet, and Gemini in Chrome: How They Read the Web

Brands and marketers have already started switching their focus from getting ranked in traditional search engine results pages to getting cited in AI-generated responses. Agentic browsing is accelerating this shift as its users are more likely to default to conversational search interfaces over traditional search engines. 

But agentic browsers do more than just solidify the need to optimise for AI visibility as a way of retaining human traffic. The goal of AI search optimisation should now be to attract and convert agentic traffic, alongside human users.

Before we dive into how to do exactly that, it’s important to understand how Atlas, Comet, and Gemini actually interpret the web.

Content Parsing and Source Ranking Logic

Agentic browsers do not see pages like users because they don’t interpret pages visually. Instead, AI agents focus on raw text and structure. 

When an AI agent lands on a page, here’s what it does:

1. It breaks down the text into chunks and interprets the structure  

AI agents process text in structured blocks, reading key headings and paragraphs in order of relevance. This “chunking” process is guided by structural signals that tell the agent what to ignore and what to extract:

• Heading hierarchies: H1, H2, H3 
• Semantic groupings: logical paragraph clusters, bulleted and numbered lists, and tables
• Structured data: JSON-LD markup (Organization, NewsArticle, FAQ Schema, and others).

However, standard HTML is often cluttered with “noise” – navigation menus, tracking scripts, and UI elements that may confuse AI crawlers. This is why Cloudflare’s Markdown for Agents is a significant shift in technical SEO. By automatically converting a webpage into a clean Markdown version, Cloudflare allows agents to bypass the “bloat” of traditional web design and ingest the core information directly.

2. It evaluates relevance and authority signals 

Once the text is parsed, the agent begins filtering what information is worth using. This is where source quality and topical trust come into play.

AI agents tend to prioritise content that shows strong domain and clear authorship. This is why major media organisations are cited more than 27% of the time. Rather than pulling random paragraphs, the system looks for credible, high-confidence sources that align closely with the user’s intent.  

3. It extracts, cross-checks, and synthesises information

After identifying a trusted and relevant section, the agent doesn’t simply copy and paste the content word for word. Instead, it synthesises a consolidated answer or summary. Some agents leave a link to the source as well. 

That’s why precise language, clean metadata, and focused explanations perform well in AI environments. 

Risks and Vulnerabilities

Agentic browsers can evaluate content, but they aren’t immune to manipulation. The level of autonomy they receive is not without risks. Because AI agents act on your behalf with the same access privileges as a human user, they can be exploited by malicious actors hidden in web content, documents, and emails.

• Malicious workflows: Attackers can trick agents into granting access to sensitive accounts, like business emails or cloud storage.
• Prompt injection attacks: Malicious instructions embedded in trusted apps can lead the AI to take unintended actions, such as embedding harmful links in calendar invites.
• Disguised malware downloads: AI agents cannot fully inspect files, so a seemingly harmless file required for a workflow could contain malware or ransomware.

Workflow-based attacks are another real risk of using agentic browsers. AI assistants excel at getting things done, but they don’t have the common-sense instincts to avoid malicious links or unsafe downloads.

Agentic browsers are 85% more vulnerable to phishing attacks than traditional browsers. Manual browsing allows time to stop and think, while AI assistants take instant actions since they automatically process web content and may act with elevated permissions. 

Optimising for Agentic Search: The New Rules of AI-Native SEO

It’s tempting to say agentic AI will make classic SEO obsolete, but the reality is more nuanced. Early investigations into ChatGPT Atlas’s behaviour reveal that it still relies on Google’s search ecosystem, leading to the conclusion that good old SEO fundamentals still matter. 

When pages are opened through Atlas, analysts have observed that Googlebot sometimes crawls those pages immediately afterwards, often identifying those visits as coming from Google’s crawler rather than a unique Atlas user agent. This suggests Atlas relies heavily on Google’s infrastructure and indexing behaviour to surface web content. 

This overlap means that traditional signals like crawlability, structure, and authority still play a role in making your content discoverable, even within AI‑driven browsers. But at the same time, Atlas and other AI agents are changing how discoverability works.

This hybrid environment, where classic SEO still influences what AI systems see, but AI readability and citation potential determine what they answer with, has given rise to a new concept often called AI‑Native SEO.

AI-Native SEO doesn’t abandon traditional search optimisation but also optimises for how AI systems read, understand, and summarise content.

1. Structure for Readability, Not Just Indexing

AI systems reward clarity. Use a clear semantic hierarchy (H1–H3) and include bullet summaries, TL;DR boxes, and section highlights to make content scannable.

Keep paragraphs short and context-rich as AI models favour well-structured text and are more likely to lift it for summaries or answers.

2. Prioritise E-E-A-T and Entity Signals

Factual credibility remains crucial. Include prominent author bios, brand mentions, and outbound citations to reinforce trustworthiness.

Leverage schema markup for Organisation, Author, and Article, and integrate verified Linked Data to help AI systems understand the relationships between entities, authors, and sources.

3. Optimise for AI Quotations

Write with the expectation that AI agents may lift your content directly into answers. Use concise, quote-ready phrasing, short definitions, and structured explanations that can be naturally extracted.

Answer key questions clearly and provide bite-sized insights that are easy for AI assistants to summarise for users.

4. Improve Crawlability and Page Context Density

Even AI-native strategies rely on solid technical foundations. That’s why ensuring fast load times, clean code, and proper metadata is also essential for both AI comprehension and user experience.

Use internal linking and site structure to group semantically related concepts together so AI models can capture the full topic coverage and provide accurate, contextual summaries.

If your website runs on Cloudflare, consider enabling Markdown for Agents to make it easier for AI bots, crawlers, and agents to consume your content.

5. Track AI Visibility

Traditional SERP metrics aren’t enough anymore. Start monitoring AI visibility to understand how often your brand or content is cited or referenced in LLM-generated responses.

Build internal dashboards to track visibility across ChatGPT, Perplexity, Gemini, and other AI agents, and use this data to refine your AI-native SEO strategy over time.

Additionally, start monitoring AI referral traffic in Google Analytics. Your GA4 dashboards should also show you if AI traffic is driving conversions and revenue, provided the key events are set up correctly.

Besides tracking the general AI citations number and visibility share, it is important to monitor the sentiment and accuracy of these mentions and citations. With AI actively summarising and quoting your content, there’s a growing need for AI content verification, watermarking, and bias-aware optimisation. Monitoring how your content is interpreted ensures your brand maintains visibility safely and ethically. By proactively auditing AI citations and ensuring your content is responsibly structured, brands can protect their reputation while thriving in agentic ecosystems.

6. Prepare for the Agent-Ready Web with Structured Action APIs

Agentic browsers are already moving beyond discovery towards direct action. New standards like WebMCP (Web Model Context Protocol) let websites expose structured tools that AI agents can interact with reliably. 

WebMCP is available in early preview within Google’s ecosystem, which is a positive shift toward a more agent-friendly web. Rather than relying on AI to scrape pages and simulate clicks, WebMCP bridges the communication between websites and AI agents.

In essence, WebMCP allows an easier way for agents to:

• Trigger defined actions such as bookings, purchases, and support requests 
• Navigate complex workflows using structured data instead of fragile DOM automation 
• Execute tasks faster, with greater accuracy and reliability

For brands, this marks the next evolution of AI-Native SEO. Success will no longer be defined by just being readable by AI; it will also depend on being operable by AI.

To enable this shift from reading to interacting, WebMCP introduces two structured layers that make websites agent-ready: 

• Declarative APIs: Allow standard actions to be defined directly in HTML (e.g., completing common tasks such as sign-ups, bookings, and other support requests). 
• Imperative APIs: Handle more complex and dynamic tasks through JavaScript (e.g., execute advanced workflows, configure products, apply filters).   

Monitoring Agent Activity

When optimising for agentic search, it’s also important to understand how to spot, interpret, and measure the impact of AI agents once they interact with your website. Tracking AI bot traffic introduces several nuances that traditional analytics tools were not built to handle, which is why this topic deserves its own section. 

How AI Agents Appear in Analytics Tools

Monitoring Agent activity is undoubtedly important, but it’s also challenging because these agents don’t behave exactly like traditional bots. In Google Analytics 4, AI agents can appear as:

• Referral traffic from sources such as chat.openai.com or perplexity.ai 
• Direct traffic when no referrer is available 
• Unassigned when the platform cannot classify the visit 

In some cases, activity from AI systems like OpenAI’s agent has been reported as Bing organic or paid traffic because the agent may perform a search through Microsoft Bing before visiting your website. 

These nuances make monitoring the traffic source harder to interpret, especially since AI agents can mimic human browsing patterns. As a result, analytics data may feature high engagement rates, unusually low bounce rates, or misleading session duration metrics. 

Limitations of Tracking AI Agents in GA4

Tracking this activity in analytics platforms is also limited by privacy and consent requirements. AI agents cannot legally provide cookie consent, and even if they technically click “Accept,” it doesn’t count as valid consent for analytics tools. 

The sessions that come from AI agents are often only captured if:

• Tracking scripts run before consent is obtained
• Your website uses server-side or cookieless tracking setups that do not rely on consent-based cookies

Because of these limitations, many AI agent visits either appear as direct traffic, are partially tracked, or are filtered out entirely by analytics systems.

Using Log Files to Detect AI Agents

To gain more accurate insights, analysing server log files is often the most reliable method.

Log files capture every request made to a website in its rawest form, including data that analytics platforms may filter out or never record. Access logs typically contain the following:

• Visitor’s IP address
• Timestamp
• Requested URL
• HTTP status code returned by the server
• Referral URL
• User agent string that identifies the browser or bot

With this information, it may be possible to detect AI agents, analyse how they navigate a site, and understand how frequently they access specific pages. 

Additional Ways to Monitor AI Agent Traffic

Beyond analytics tools and log files, there are a few other ways to monitor AI agent activity on your website. 

• CDN with bot insights

Using a Content Delivery Network (CDN) with bot insights can help detect and label traffic from AI agents, monitor how automated systems interact with your website, and block or rate-limit certain crawlers if necessary. 

• Server-side analytics without cookies 

Server-side analytics without cookies can also provide anonymised insights into AI agent visits while remaining compliant with regulations like GDPR, although this approach typically offers less detailed behavioural data.

• Analysing user behaviour 

Instead of only looking at traffic sources, you can identify AI agents by how they behave on your site. For example, sessions with an engagement time of 0 seconds, where a visitor immediately clicks a link or moves to another page, often indicate automated activity rather than a real user.

You can also find cases where users appear inactive and simply navigate from one page to another without meaningful interaction, which is another sign of bot traffic. 

Creating segments in analytics platforms based on these behavioural signals or unusual traffic sources can help identify and monitor potential AI agent visits more effectively. Additionally, some website backends show “wordpress.com” as a traffic source, which can indicate visitors coming from ChatGPT-related browsing sessions.

Monitoring AI traffic is currently one of the bigger analytics challenges, but in the near future, it will become a critical issue for businesses. Traffic generated by AI will continue to grow, which makes it essential to distinguish between “reading bots” and modern “shopping bots” that perform real tasks on behalf of customers.

We need to prepare for entirely new attribution and remarketing rules. A situation where an AI agent browses products on Monday, and the final conversion appears a few days later from a normal source, will become standard. Without precise monitoring of these agents’ activity in server logs, our data on user behaviour and the real effectiveness of sales will simply become distorted.

Marcin Walkowiak, SEO Expert at SUSO

Building for the Browsers That Think and Act

Search has evolved into conversation. Atlas, Comet, and Gemini-powered Chrome are the first generation of tools that understand intent and can be instructed to act on it.

The brands that win will go beyond optimising for AI extraction and prepare for AI-first user journeys where agents are treated with the same attention as human visitors.