Jun 8 2026
What is agentic commerce? A peek into the future of buying (with caveats)

Table of contents

Commerce has undergone several major shifts over the past few decades. What started with localized physical stores evolved into borderless, internet-driven ecommerce experiences.

Now, with the rise of AI, it is believed that commerce could be heading toward another transformation: agentic commerce, where AI agents help consumers discover products, compare options, and even complete purchases on their behalf.

Yet despite the excitement, many questions remain. Will consumers trust AI agents with buying decisions? Will businesses see enough return on investment to justify the costs? And does autonomous shopping solve a real problem, or simply add another layer of complexity to the buying journey?

Still, the technology is advancing rapidly. Imagine a shopping experience where consumers no longer jump between tabs, compare dozens of products on different websites, or manually research every purchase. Instead, AI agents understand intent, evaluate options, compare prices, and act within predefined rules to help users make purchasing decisions. What once sounded futuristic is already beginning to take shape.

In this article, we’ll explore what agentic commerce is, how it works, the technological developments driving it forward, and some of the challenges that could shape its future adoption.

Key takeaways

  • Agentic commerce represents a shift where AI agents assist consumers in product discovery, comparisons, and purchases
  • AI agents execute tasks based on user intent, simplifying the shopping journey and enhancing efficiency
  • Consumer interest is growing, with over 60% expecting to use AI in their shopping experiences by 2026
  • Technological developments like the Agentic Commerce Protocol (ACP) and Universal Commerce Protocol (UCP) are crucial for enabling agentic commerce
  • Despite its potential, agentic commerce faces challenges related to consumer trust, security, and the need for business investments.

What is agentic commerce?

In simple terms, agentic commerce refers to a commerce model where AI agents act as decision-makers on behalf of customers.

Instead of manually searching for products, comparing options, filtering results, and completing purchases, users can rely on AI agents to handle these tasks based on their intent, preferences, constraints, and buying goals.

To paint a clearer and practical picture, here’s how Alex Moss explained agentic commerce in the SEO Unplugged: Agentic Commerce with Alex Moss podcast:

So everything’s connected.

I could literally say into the into a phone to my agent, go and buy me some new shoes with that jacket I bought last week, and that’s it.

And it would go away.

It would do the research.

And of course, you can have a say in an approval in terms of part of the journey.

At its core, agentic commerce works like a digital shopping proxy. Humans define the intent or goal, while AI agents execute the process behind the scenes. While the AI handles the heavy lifting, users still remain in control of the final decision-making process.

Also read: Ensuring continuous discoverability with agentic AI for SEO

Agentic commerce is the next big thing in ecommerce

The concept of agentic commerce may still sound futuristic, but the shift has already started. Consumer behavior, AI adoption, and industry forecasts all point to a future in which AI agents become an active part of the buying journey.

Here are some numbers that highlight why agentic commerce is emerging as the next major evolution in ecommerce.

Consumers already use AI in their buying journey

Consumers have already started relying on AI-powered tools to discover products and make purchasing decisions. According to a McKinsey & Company report, more than 70% of AI-powered search users ask top-of-the-funnel questions about categories, brands, products, or services.

Example of a TOFU research performed on Claude

The same report also found that nearly 50% of consumers already use AI-powered search experiences today. As AI increasingly becomes part of product discovery, traditional search-driven traffic may face growing disruption. In fact, the study suggests that businesses could see 20–50% of their traffic shift away from traditional search experiences over time.

This highlights an important shift: consumers are no longer just searching; they are increasingly asking AI systems to guide their decisions.

Shoppers are expecting agentic commerce

Consumer interest in AI-assisted shopping is also growing rapidly. The 2025 report titled “Agentic Commerce: From Brand Loyalty to Bot Logic” explored how shoppers feel about AI agents in retail experiences.

The report found that more than 60% of shoppers expect to use agentic AI in 2026. The findings also revealed a major behavioral shift: consumers increasingly prioritize convenience, speed, pricing, and trust over platform loyalty.

Instead of browsing individual retailer apps, shoppers may rely on AI agents that can compare products across multiple platforms, evaluate reviews, identify the best deals, and complete purchases more efficiently. This changes the competitive landscape from retailer-versus-retailer competition to AI-driven discovery ecosystems.

Analysts predict explosive growth for agentic commerce

Industry analysts also expect agentic commerce to become a massive economic opportunity over the next few years. Another McKinsey report suggests that agentic commerce could fundamentally reshape the shopping experience.

Based on the growing adoption of AI-powered discovery tools and increasing merchant readiness, the report estimates that by 2030, the US B2C retail market alone could unlock an orchestrated revenue opportunity of $900 billion to $1 trillion. Globally, that opportunity could range from $3 trillion to $5 trillion.

How does agentic commerce work?

At its core, agentic commerce combines human intent with AI-driven execution. Instead of manually browsing websites, comparing products, and completing purchases, users can delegate much of the shopping journey to AI agents. These agents understand goals, evaluate options, make decisions within defined constraints, and even complete transactions on behalf of users.

What makes this different from traditional AI assistants is the ability to act. While assistive AI tools mainly provide information or recommendations, agentic AI can independently execute tasks across the shopping journey.

Also read: What is the user journey in SEO?

Here’s a step-by-step look at how agentic commerce works:

Agentic commerce step-by-step working diagram

Step 1: Capturing the intent

Every agentic commerce journey begins with intent. Instead of typing short keywords into a search bar, users interact with AI agents conversationally.

For example, a shopper might say:

  • “Find me a durable pair of running shoes under $150.”
  • “Restock groceries for a vegetarian dinner party.”
  • “Buy a formal shirt that matches the trousers I purchased last month.”

At this stage, the AI agent focuses on understanding the user’s goals, preferences, budget, delivery expectations, and constraints. If the request feels too broad, the agent may ask follow-up questions to refine the intent before moving forward.

Step 2: Autonomous instruction execution and brand discovery

Once the intent becomes clear, the AI agent begins executing the task autonomously. Instead of searching a single website, it scans multiple ecommerce platforms, marketplaces, product catalogs, reviews, pricing databases, and inventory systems simultaneously.

This is where agentic commerce begins to change traditional product discovery. Rather than showing endless product pages, the agent narrows down the most relevant options based on the shopper’s needs.

At the same time, brands with better-structured product data, accurate inventory information, transparent pricing, and machine-readable content are more likely to be discovered by AI agents.

Do read: Taxonomy SEO: How to optimize your categories and tags

Step 3: Evaluation and decision-making

After gathering options, the AI agent starts evaluating products and comparing tradeoffs. It may analyze factors such as:

  • Price and discounts
  • Product specifications
  • Customer reviews and ratings
  • Shipping timelines
  • Return policies
  • Brand trust and reputation

Instead of simply listing products, the agent reasons through the options and explains why certain products better meet the shopper’s requirements than others.

Users can also refine the decision-making process further by adding conditions such as:

  • “Only show products with free returns.”
  • “Prioritize faster delivery.”
  • “Exclude refurbished products.”

This creates a feedback loop where the AI agent continuously improves its recommendations based on user preferences.

Step 4: Purchase

Once the shopper approves a product or sets predefined rules, the AI agent can move forward with the transaction. Using APIs, commerce protocols, and secure payment systems, the agent can add items to carts, apply discounts, authenticate payments, and complete purchases.

In some cases, the purchase may happen instantly. In others, the AI agent may wait for specific conditions, such as a price drop, stock availability, or faster delivery options, before completing the transaction.

Even though the AI handles execution, users still remain in control through permissions, approval settings, and spending limits.

Step 5: Post-purchase support

The role of AI agents does not end after checkout. Agentic commerce also extends into post-purchase experiences.

AI agents can continue assisting users by:

  • Tracking deliveries
  • Managing returns or exchanges
  • Monitoring refunds
  • Sending delivery updates
  • Reordering recurring products
  • Recommending complementary products or accessories

This turns shopping into an ongoing and intelligent experience rather than a one-time transaction.

Technological developments

Agentic commerce is not powered solely by AI models. Behind the scenes, it depends on a growing ecosystem of protocols, frameworks, APIs, and payment systems that help AI agents interact with digital commerce platforms securely and efficiently.

One important concept shaping agentic AI is the Model Context Protocol (MCP). In agentic AI, MCP enables AI models to connect with external systems, tools, databases, and applications via a standardized communication layer.

Instead of building separate integrations for every AI model and every software platform, MCP creates a common framework that allows AI agents to access information and execute actions more consistently. Think of it like creating a shared operating language between AI systems and digital tools, so they can work together without requiring completely custom connections every time.

As agentic commerce evolves as a use case of agentic AI, similar commerce-focused protocols are now emerging specifically for shopping ecosystems. These protocols help AI agents understand product information, communicate with merchants, compare inventory, and securely complete transactions on behalf of users.

Here are some important developments supporting agentic commerce:

Agentic Commerce Protocol (ACP)

One of the most important developments in this space is the Agentic Commerce Protocol (ACP), an open standard introduced by Stripe in collaboration with OpenAI.

ACP is designed to help AI agents interact more naturally with ecommerce systems by creating a standardized framework for product discovery, checkout, and payment execution. In simple terms, it provides the infrastructure that allows AI agents to move beyond simply recommending products and actually complete purchases securely on behalf of users.

The protocol is still in its early stages, but its first real-world implementations are already emerging. For example, ChatGPT users in the United States can already purchase products from Etsy merchants directly within the chat experience through Stripe-powered checkout. Shopify integrations are also expected to follow.

This matters because it signals a shift from AI-assisted discovery to AI-enabled transactions happening inside conversational interfaces themselves. Instead of redirecting users across multiple websites and checkout flows, ACP aims to make the entire shopping journey more seamless and agent-friendly.

Another important aspect of ACP is its open-standard approach. Rather than creating a closed ecosystem tied to a single platform, Stripe and OpenAI position ACP as a framework that developers, merchants, and ecommerce platforms can adopt more broadly as agentic commerce evolves.

Looking ahead, protocols like ACP could become foundational infrastructure for AI-driven shopping experiences, especially as more businesses begin to optimize their product catalogs, payment systems, and checkout experiences for AI agents rather than only human users.

Also read: Boost your checkout page UX: Vital tips for online stores

Universal Commerce Protocol (UCP)

As more AI agents enter the shopping journey, a new challenge emerges: how can these agents communicate with thousands of retailers, marketplaces, payment providers, and service platforms without requiring a custom integration for each one?

This is the problem that the Universal Commerce Protocol (UCP) aims to solve.

Introduced by Google, UCP is an open standard designed to create a common language for agentic commerce. Rather than building separate connections between every AI agent and every commerce platform, UCP provides a shared framework that allows them to communicate more efficiently throughout the shopping journey.

Think of it this way: if agentic commerce becomes mainstream, millions of AI agents could research products, check inventory, compare prices, place orders, and manage returns every day. Without a standardized framework, retailers and AI platforms would need to create and maintain countless one-to-one integrations. UCP aims to remove this complexity by providing a common set of rules for all participants to exchange commercial information.

What makes UCP particularly interesting is its broad scope. Unlike protocols that focus mainly on purchasing, UCP is designed to support the entire commerce lifecycle, including:

  • Product discovery
  • Product comparison
  • Purchasing and checkout
  • Order tracking
  • Returns and post-purchase support

Google has also designed UCP to work alongside other emerging AI standards, including Agent2Agent (A2A), Agent Payments Protocol (AP2), and Model Context Protocol (MCP). This allows businesses to adopt agentic commerce without completely replacing their existing systems.

The initiative already has significant industry backing. Google co-developed UCP with major commerce companies, including Shopify, Etsy, Wayfair, Target, and Walmart. It has also received support from companies such as Mastercard, Visa, Stripe, and American Express.

Platforms that support Google's Universal Commerce Protocol
Platforms that support Universal Commerce Protocol

While agentic commerce is still in its early stages, UCP represents an important step toward a future in which AI agents, merchants, and payment providers can operate within a single ecosystem rather than through isolated platforms. In many ways, it provides the foundational infrastructure needed to make agentic commerce scalable across the broader digital economy.

Mastercard Agent Pay

While protocols like ACP and UCP focus on communication and interoperability, Mastercard Agent Pay focuses on one of the most critical challenges in agentic commerce: trust and secure payment execution.

As AI agents gain the ability to discover products, compare options, and make purchasing decisions, they also need a secure way to complete transactions on behalf of users. Mastercard Agent Pay was introduced to provide the infrastructure for exactly that.

The platform is designed to allow AI agents to execute payments while operating within user-defined permissions, authentication requirements, and spending controls. Rather than giving AI systems unrestricted access to payment credentials, Agent Pay focuses on creating verified, traceable, and authorized payment flows for agent-driven commerce.

One of the most significant developments came through its collaboration with PayPal, where Mastercard Agent Pay is being integrated into PayPal’s wallet infrastructure. It allows AI agents to securely complete transactions on behalf of PayPal users while maintaining the security and trust mechanisms that consumers already expect from digital payments.

This partnership is particularly important because it moves agentic commerce closer to real-world adoption. Instead of existing only within experimental AI environments, agent-driven payments can potentially operate across a much larger ecosystem of merchants, consumers, and payment networks.

Together, ACP, UCP, and Agent Pay are helping lay the foundation for agentic commerce. While ACP focuses on enabling AI agents to interact with merchants and complete purchases, UCP creates a common language that allows agents, retailers, and platforms to work together at scale. Agent Pay adds the trust layer by enabling secure, authorized payments, bringing AI-driven shopping one step closer to reality.

FAQs: What is agentic commerce?

What are the benefits of agentic commerce for enterprises and users?

Agentic commerce benefits both businesses and consumers by making shopping more efficient and personalized.

For users
AI agents can reduce research time, provide tailored recommendations, monitor prices, and automate routine purchases.

For enterprises
Agentic commerce can streamline operations, improve personalization, automate repetitive workflows, support faster decision-making, and help products reach customers more quickly. Together, these benefits create a more convenient shopping experience while improving operational efficiency.

Are agentic AI and agentic commerce the same?

No, they are not the same. Agentic AI is the underlying technology that enables AI systems to understand goals, make decisions, and complete tasks autonomously. Agentic commerce is a specific application of agentic AI in shopping and commerce. In other words, agentic AI is the foundation, while agentic commerce is one of its real-world use cases.

What’s the difference between traditional commerce and agentic commerce?

In traditional commerce, the shopper remains the primary decision-maker and executor throughout the buying journey. Even when AI is present, its role is largely limited to recommending products or improving search experiences. In agentic commerce, AI agents actively participate in the shopping process by researching products, comparing options, and executing tasks on behalf of users, guided by predefined goals and preferences.

Can you share some practical, real-world use cases for agentic commerce?

Several companies are already experimenting with agentic commerce. For example, Amazon has introduced its “Buy for Me” feature, which allows AI agents to purchase products from third-party websites when items are unavailable on Amazon.

Similarly, Google is testing AI-powered shopping experiences that can monitor prices and automatically purchase products when they meet user-defined conditions. Beyond consumer shopping, businesses are also using AI agents to monitor inventory levels and automatically reorder supplies when stock runs low.

Agentic commerce still faces important questions

While the technology behind agentic commerce is advancing quickly, widespread adoption is far from guaranteed. Many consumers may not feel comfortable giving AI agents the authority to make purchasing decisions or access payment methods on their behalf. Others may question whether autonomous shopping solves a real problem or simply makes it easier to buy more things, more often.

Businesses face their own uncertainties. Supporting agentic commerce may require investments in new protocols, structured data, integrations, and AI-ready commerce experiences. Whether those investments yield measurable returns remains unclear, especially given that consumer adoption is still in its early stages.

There are also broader challenges to solve, including security, fraud prevention, AI bias, platform dependency, and the potential loss of direct relationships between brands and customers. Agentic commerce may represent an exciting new direction for digital shopping, but its long-term success will depend on whether it can create value for consumers, merchants, and the broader ecommerce ecosystem, not just the AI platforms powering it.

Avatar of Ahad Qureshi


Ahad Qureshi

I’m a Computer Science grad who accidentally stumbled into writing—and stayed because I fell in love with it. Over the past six years, I’ve been deep in the world of SEO and tech content, turning jargon into stories that actually make sense. When I’m not writing, you’ll probably find me lifting weights to balance my love for food (because yes, gym and biryani can coexist) or catching up with friends over a good cup of chai.

Ecommerce MGMT 0 Comments
Jun 8 2026
Google Ask Maps Updates – How They Impact Your Business Profile via @sejournal, @MattGSouthern

Many businesses see their Google Business Profile as a listing to verify and then leave untouched. Google’s new Ask Maps feature treats it as a conversational dataset for generating helpful answers about a business.

The questions Ask Maps answers are what make change meaningful. When someone asks for a 24-hour locksmith who can get into a car right now, they get an immediate answer. That’s one question with multiple conditions taken into account.

Showing up as one of the answers depends on having accurate and up-to-date business data. While Google hasn’t said how it chooses businesses to recommend in Ask Maps, it’s clear that the data it pulls from is increasingly important.

What Google Says About Ask Maps

Google calls Ask Maps a helpful tool for asking detailed, real-world questions and receiving conversational responses with a personalized map.

Google describes the feature as drawing on fresh information about the world. It taps into over 300 million places and reviews from more than 500 million contributors. Responses are personalized based on signals like the places you’ve searched for or saved in Maps.

The announcement doesn’t include any details about how Ask Maps chooses or ranks the businesses within an answer.

What Multi-Variable Queries Demand From Business Data

The Ask Maps examples Google provided include multiple conditions. For instance, finding a “lit tennis court available tonight” requires checking several factors at once: the court must exist in the data, be public, have lights, and be open at the time of your search.

Each condition relies on a different layer of local data, making it all more connected. Entity and location data come directly from the listing. Amenities such as lighting might be based on structured place information, reviews, photos, or other data from Maps. Whether a place is available tonight depends on accurate operating hours.

None of this explains how Ask Maps weighs those fields, but it shows the kind of data an answer might need. So, a profile that ranks well in traditional Search for simple queries might not be detailed enough to show up for a question with multiple conditions.

The Profile Completeness Gap

Both Google’s local ranking guidance and independent survey data point to the same idea. Having complete and timely business information matters. Per Google’s guidance, businesses that keep their information up to date are more likely to be matched with relevant local searches.

Whitespark’s Local Search Ranking Factors survey gathered insights from about 50 experts, who rated the importance of various signals that influence local rankings. Many of the top-scoring signals are related to whether business data is true and current.

Whitespark provides local SEO software and services, and the survey showcases the insights of experts rather than being directly confirmed by Google. It has been conducting this survey in various forms since 2008, making it one of the most enduring references in the field.

In BrightLocal’s breakdown, experts say being open at the time of search is a key local pack signal. Reviews carried more weight in the 2026 survey than in 2023, rising from 16% to 20%.

The survey also shows that it’s likely unnecessary to fill out every field. Respondents indicated that some inputs, like keywords in the Business Profile description and the number of questions asked through Google Q&A, don’t significantly impact local pack rankings. Instead, the signals that matter most are those that demonstrate a business is genuine, active, and accurately represented.

It’s really about quality over quantity, focusing on signals that show Google your business is authentic and active.

What Local SEO Professionals Are Seeing

Even though Google hasn’t shared much about how they rank places, local search experts continue to find clues.

Mike Blumenthal, co-founder of Near Media, tied the change back to data. Speaking on the Whitespark Local Update Podcast, he said:

“I think Google always loves more data, and clearly Q&A had become unwieldy.”

He added that Google is leaning on businesses to supply that data. That support lasts only as long as the data stays useful.

Greg Sterling, co-founder of Near Media, shared a similar perspective on where the answers come from. In his Local Dialog newsletter, he discussed Google’s in-profile conversational feature, which is a precursor to the Ask Maps button.

He mentioned that the information was “drawn from GBP, user reviews, the business website, and third-party sources.” That aligns with the factors the Whitespark survey rated highly for AI search visibility.

Darren Shaw, founder of Whitespark, took the point wider. In a post about Google’s AI Mode, he wrote that this kind of discovery reaches past the sources a business controls. In his words, it pulls from “what the entire internet says about you.”

None of this is officially confirmed by Google. It’s based on observations from people who monitor local search closely, and it matches what survey data shows.

What’s Still Unknown

One question that comes up throughout all of this is something Google hasn’t answered yet. How does Ask Maps decide which businesses to include in an answer? And how does it compare a business profile with reviews, a website, or third-party sources?

Until Google shares more details, any claims about the ranking process in Ask Maps should be seen as educated guesses.

We don’t know the status of the public Q&A feature. Google ended the My Business Q&A API in November, as noted in its developer changelog. It hasn’t explained what the new Q&A experience will look like. For now, businesses don’t have a programmatic way to manage Q&A.

Monetization is another unknown. At launch, Google didn’t mention advertising in Ask Maps, and executives chose not to comment on potential ad placements.

Looking Ahead

Ask Maps is in its early stages on mobile, with a desktop version coming.

As it rolls out, your job is to observe the businesses appearing and see what you can learn from them. Note the common traits such as accurate hours, recent reviews, complete attribute information, and a website that explains their offerings.

In the past, a thin or stale profile might have caused a weaker listing that could still rank. Now, with Maps providing AI-assisted answers, it could make the difference between being recommended and being left out.

More Resources:

Featured Image: CL STOCK/Shutterstock

Ecommerce MGMT 0 Comments
Jun 8 2026
WordPress Announces Initiative To Secure All Plugins And Themes via @sejournal, @martinibuster

WordPress announced a new security initiative called Protect The Shire that aims to secure plugins and themes. The announcement also said a temporary 24-hour delay will be imposed before plugin and theme updates are distributed through auto-updates.

Temporary 24 Hour Update Delay

In the past, plugin and theme updates were pushed out to WordPress users autonomously: A theme or plugin author would update their software and push it live to their users immediately. That’s no longer the case for the time being.

WordPress is temporarily delaying updates for 24 hours in order to have time to check the updated plugins to ensure that they are secure before allowing them to be sent to WordPress users. WordPress anticipates that this delay will, in time, become dramatically shorter so that it’s only a matter of minutes.

This new step is being taken in light of increasing incidents of software supply chain attacks, a scenario where a hacker sneaks a malicious payload into an open-source library that is subsequently distributed to every piece of software, plugin, and theme that depends on it. Hackers are targeting these libraries of useful code because they are frequently maintained by a single volunteer.

WordPress describes this moment as a “liminal period,” which means that the project is in a moment of transition, neither doing things the same way as in the past nor doing things as they intend to do in the near future.

The WordPress announcement explains:

“We’re in a liminal period now, and I believe 2026 will be a year of tension between two approaches: updating as quickly as possible to stay secure, and holding back on updating to stay secure.

We’ve seen clever and dangerous supply chain attacks across the npm, PyPI, GitHub, and RubyGems ecosystems, and we even had our own mini-version with the Essential Plugins debacle, where good plugins were unknowingly sold to a new author who had malicious intent.

How to balance security updates and securing updates?”

Protect The Shire Initiative

WordPress also announced a security effort called Protect The Shire for making all of the code in the WordPress.org directories and repositories secure.

WordPress did not describe specific technical details about how the initiative will operate, only that it will improve security across its ecosystem of plugins and themes. The announcement also says the work will happen behind the scenes, with success measured by vulnerabilities and attacks that never reach users.

WordPress Plugin Team Automation

WordPress has been using automated tools to assist plugin reviews for some time. In January 2026, the Plugins Team disclosed that its internal scanner, used to review plugin submissions, had been expanded with AI-assisted capabilities and dozens of new automated checks. According to the team, the scanner helps identify potential issues for human reviewers to investigate and is used to automate repetitive tasks.

The blog post explains:

“If there is one thing worth highlighting this year, it is how AI has impacted the WordPress plugin ecosystem. This impact is evident both in the number of submissions sent for review to be published in the directory, and in how the team is implementing AI-based analysis processes to help deliver improved workflows with a certain level of automation.

…The internal scanner is the in-house tool that the team uses to review plugins. It searches for hundreds of possible issues that the reviewers either confirm or dismiss when creating a report. As part of the improvements to this central tool for our day-to-day plugin reviews, we have worked on reducing review time, particularly for highly repetitive and time-consuming processes such as:

  • Verifying that the plugin name does not conflict with existing published plugins.
  • Ensuring branding is used correctly and complies with guidelines.
  • Verifying plugin ownership.”

Response On Social Media Is Positive

The response on social media was largely positive.

@Usmank11 tweeted:

“24 hours seems a good amount of time especially for small devs. I hope we won’t forget our releases after 24 hours of release to public..”

@enqueue_russ asked a question about how this would be timed with emails sent out by plugins:

“I’m curious to know how this will change the marketing strategy for many freemium plugins. They might no longer be able to time emails with releases on .org.”

Others agreed that this was a good decision and agreed that this would be good for improving the security of the WordPress ecosystem although a few people had concerns.

@adampreiser tweeted:

“Am I the only one thinking this is going to create some problems as well?

What if there is an urgent bug fix? Welp, you have to wait 24 hours.

What if there is a pro version that needs to be available at the same time? Good luck timing that right.

Likely other issues.”

@themergency responded with a Gandalf “You shall not pass” animated gif, expressing their support:

“I support Protect The Shire!

A request from plugin devs: open up a way to integrate Gandalf-AI-style pre SVN commit scans into dev workflows.”

Reviewing Plugin Updates A Great Idea

WordPress security has long been one of the things that many users have been concerned about. The massive size of the WordPress user base makes WordPress plugins and themes a larger target for hackers, although the WordPress core itself has a fantastic track record for security. This is going to make users more confident in WordPress and will likely win back some users who have been concerned about security.

Featured Image by Shutterstock/GreenTech

Ecommerce MGMT 0 Comments
Jun 8 2026
AI Visibility Used To Mean Citation. Late June 2026, It Starts To Mean Transaction via @sejournal, @slobodanmanic

For two years, the AI visibility question has been one question: Does your website get cited? Late June 2026, the question becomes two. Does your website get cited, and when the agent shows up to book the appointment on a user’s phone, can the agent actually complete the booking?

On May 12, 2026, Google announced that Chrome auto-browse, the agentic browsing feature that fills forms, books appointments, reserves parking, schedules visits, renews licenses, and runs comparison shopping, lands on Android phones in late June 2026. The first wave hits Samsung Galaxy S26 and Google Pixel 10. The rest of the year rolls out to watches, cars, glasses, laptops. The agent has been living on desktop in preview since January. Late June, it moves to 200 million pockets.

The critical detail is what kind of release this is. Auto-browse on Android does not ship as an app, a browser extension, or an opt-in feature. It is part of the operating system itself. Google’s own framing puts it plainly: Android is moving “from an operating system into an intelligence system.” The agent is baked in. Every Pixel 10 and Galaxy S26 user gets it by default. AppFunctions, the underlying API for agent-to-app communication, will reach over 200 million Android devices by the end of 2026.

This is not a feature launch. It is the mobile distribution layer for the entire agentic-web stack Google has shipped over the last six months, dropped into the operating system itself. Read alone, the May 12 announcement looks like a Gemini update. Read against the timeline, it closes the architecture.

OS-Level Integration Is The Differentiator That Reshapes The Stakes

Every prior consumer agent has shipped as an app or a website. ChatGPT, Claude, Perplexity, and, until today, Gemini all lived in apps. Apps have to compete for installation, retention, and daily use, depend on the user remembering to open them, and sit in the userland of the phone behind every other thing the user has to think about.

OS-level integration is a different category. When the agent ships with the operating system, it does not need to be opened, remembered, or to win against other apps for screen time. It is available by default the moment the user picks up the phone. Default availability on hundreds of millions of devices is not the same as “the most popular app.” It is closer to what default search has been for desktop browsers for two decades. Whoever owns the default owns the traffic.

That default-availability matters for two reasons. The first is reach. The agent is going to be tried by a much larger population than any opt-in agent has reached. The Pixel 10 user does not have to install anything to delegate the haircut booking. The Galaxy S26 user does not have to choose an agent product. They say what they want, and the OS-level agent does it.

The second reason is authority. An OS-level agent has system-level permissions to navigate apps, accept notifications, read the screen, and operate the browser. It has access to the password manager. It has access to the user’s contact information through Personal Intelligence. It has the credentials and the context to actually complete the tasks it is asked to complete. App-level agents can only do what their permissions allow, and on Android, those permissions historically end at the app boundary.

The combination of default-availability and system-level authority is what makes late June 2026 different from January’s auto-browse desktop launch. The scale shift, not the feature shift, is what matters.

Late June 2026 Is When Chrome Auto-Browse Lands On Android Phones

Google’s May 12 announcement calls the shift “Gemini Intelligence” and describes Android as moving from “an operating system into an intelligence system.” Behind the marketing language, the operational changes are concrete. Chrome auto-browse handles appointment booking and parking reservations. Intelligent Autofill pulls from Google Password Manager and Personal Intelligence to populate form fields across the web. Multi-step task automation chains app actions across food and rideshare. Rambler converts spoken text to polished messages. Create My Widget generates custom home screen widgets from natural language.

The web-facing feature that matters most is auto-browse. Auto-browse uses Gemini 3’s multimodal capabilities to read pages, identify what is on them, fill forms, navigate flows, and complete transactions. Google does not publish the exact technical pathway. Vision-based understanding, plus DOM access, plus accessibility tree reads, is the inferred composition, but the company has deliberately not specified it. What Google has specified is the behavior. The agent operates the website the way a user does, except faster and without the user tapping anything.

Auto-browse is gated to Google AI Pro at $19.99 per month for 20 tasks per day, or AI Ultra at $249.99 per month for 200 tasks per day. It pauses for explicit user confirmation on purchases and social posts. The early use cases Google cites are quotidian: scheduling appointments, filing expense reports, comparing hotel prices, managing subscriptions, renewing driving licenses, getting plumber quotes.

These are the tasks that drive most local-business booking traffic.

The 6-Month Arc Is 9 Google Moves That Compose To 1 Stack

The Gemini Intelligence Android announcement is the 10th move in a series, not the first. Each move closed a different layer of the agentic-web architecture. Together, they cover the full path from discovery to citation to action to commerce to agent identity.

Jan. 29, 2026: Chrome auto-browse launched in preview on desktop in the U.S.

Feb. 25, 2026: AppFunctions for Android, a Model Context Protocol-style API that lets Android apps expose actions to Gemini natively, with Uber, DoorDash, and OpenTable as launch partners.

April 16, 2026: AI Mode Chrome integration rolls out to U.S. English users, making AI Mode reachable from the Chrome address bar.

April 29, 2026: Google replaces the “Search” button on Android globally with “Ask Google,” ending the assumption that “search” means typing keywords.

April 2026: Google web.dev publishes “Build agent-friendly websites,” the first vendor-published design-pattern guidance for agent-readable web architecture.

April 2026: Gemma 4 and Gemini Nano 4 ship as local agentic intelligence on-device, up to 4x faster than the previous generation and 60% less battery.

April 2026: Universal Commerce Protocol (UCP) launches, co-developed with Shopify, Etsy, Wayfair, and Target, defining how agents transact with merchant websites.

April 2026: Google Cloud Next ships the Gemini Enterprise Agent Platform, the Agent-to-Agent (A2A) protocol for cross-platform agent communication, and Workspace Studio for no-code agent building.

May 12, 2026: Gemini Intelligence Android and the late-June auto-browse rollout on phones, embedded at the OS layer.

Late June 2026: Chrome auto-browse becomes available on Android.

The composition is the story. Project Mariner, DeepMind’s web-browsing research agent that scored 83.5% on the WebVoyager benchmark, became Chrome auto-browse. Auto-browse needs a way to handle commerce flows. That is UCP. UCP needs agents to identify themselves to merchant systems. That is A2A. The agent needs local inference for mobile latency. That is Gemini Nano 4. The agent needs design patterns to know what a “booking flow” looks like across millions of websites. That is web.dev’s agent-friendly guidance. The agent needs distribution. That is what OS-level integration delivers.

Each piece, on its own, looked like a product update. Stacked, they are the operating layer of the agentic web on the dominant mobile platform.

The Transaction Shift Changes What “Agent Visibility” Means

For two years, AI visibility has been a discovery problem framed around citation eligibility across ChatGPT, Perplexity, and Google’s AI Overviews. The retrieval-eligibility frame Mike King and others have written about this year is the right frame for that problem. Citation eligibility is upstream of citation share, and citation share is upstream of brand presence in AI-mediated discovery.

Late June 2026, the frame extends. Citation is still in play, but a second question stacks on top of it. When the agent shows up at the website on the user’s phone, can it complete the action?

The Machine-First Architecture pillars all still apply. Identity tells the agent which business the website represents. Structure tells the agent what is on the page and where the actions are. Content tells the agent what the page actually says. Interaction tells the agent how to complete what it came to do.

Through the citation lens, Identity, Structure, and Content carried most of the weight. The fourth pillar, Interaction, was the one most teams paid least attention to. Through the transaction lens, Interaction goes from least-discussed to load-bearing. The agent does not only read your booking page anymore. It clicks the “Tuesday 6pm” button, fills the phone number field, accepts the booking confirmation modal, and navigates the multi-step checkout. Every one of those moves can fail in a way the agent cannot recover from, on websites that work fine for human users.

What Breaks Under Auto-Browse Clusters Into 8 Failure Modes

A pattern that costs zero conversion under human traffic can cost the full booking under agent traffic. The failure modes cluster into a handful of categories.

Client-side rendering blocks the page. The agent reads the initial HTML response. If the booking form, the calendar widget, or the call-to-action button renders only after JavaScript hydration, the agent sees an empty shell. This is the same failure that hides content from AI search citation, applied to the transactional surface. Modern visual website builders that default to client-side rendering, including Figma Sites, Bubble, Wix Studio, Plasmic, and Lovable in its default React + Vite configuration, produce websites where the booking flow is invisible to the agent.

Cookie walls block content until interaction. If your website shows a cookie banner that obscures all content until the user clicks Accept, the agent has to click Accept first. Some agents handle this. Some do not. Some click Accept on a banner whose terms the user has not seen, which is a separate problem. Either way, the cookie wall introduces a step the agent might fail.

Forms without proper labels are unreadable. A without an associated element or an aria-label attribute is a field the agent cannot identify. It does not know whether to put the phone number, the email, or the name there. Multiply across a five-field booking form, and the failure rate compounds. Real label elements paired with each input are the fix, and they are the same fix accessibility audits have recommended for fifteen years.

Div-based buttons fail interaction. A

styled to look like a button is not a button to the agent. The agent reads HTML semantically. If the “Book now” element is not a real or element, the agent does not know it can be clicked. The fix is to ship real button elements.

Modal traps prevent flow completion. A modal that appears mid-flow with a close button hidden behind a CSS hover state, or a calendar widget that opens in a pop-up the agent cannot dismiss, breaks the booking. The agent gets stuck in a state with no recoverable path forward.

CAPTCHA stops the agent cold. A CAPTCHA on the booking form is a hard stop. The agent will not solve it. The user did not ask to be CAPTCHA-tested in the middle of a delegated task. The booking fails. CAPTCHAs are increasingly the friction layer of last resort against bot traffic, and they are about to start blocking legitimate user-delegated agent traffic, too.

Dynamic-load timing exceeds the agent’s patience window. A page that loads in eight seconds because of heavy JavaScript bundles is a page the agent might abandon. Mike King’s retrieval-eligibility work this April showed that page-load time has become a hard cutoff for AI retrieval, with 499 status codes (“client closed connection”) appearing where the agent gave up before the page finished. Auto-browse inherits the same constraint, sharpened by mobile latency.

Sign-in walls require credentials the agent might not have. Google Password Manager helps where the user has saved credentials. Without saved credentials, the agent stops. For local business bookings, sign-in walls are a common pre-action friction layer. They become a hard agent-blocker the moment the user has not previously signed up.

The Audit Has Not Changed In A Decade, Only The Visitor Class Has

I ran Google’s seven-rule audit on nohacks.co earlier this month. Six of seven passed. Rule five, cursor pointer on interactive elements, failed on every native button because of a Tailwind v4 default that ships with no warning. Three lines of CSS fixed it. The fix took longer to find than to implement.

That audit was framed around AI search citation eligibility. The same audit, under auto-browse stakes, is a transaction-eligibility audit. Open the booking flow on a phone in Chrome. Disable JavaScript in dev tools. Reload. Can you see the form, see the buttons, complete the booking with keyboard only?

If yes, the agent can do it too.

If no, the agent cannot, and the booking goes to the next salon on the list.

The audit is not new. Accessibility teams have been running variations of it since the WCAG 2.0 era. The retrieval-eligibility and transaction-eligibility frames are new visitor classes that benefit from the same fixes. The web.dev guidance Google published in April makes the convergence explicit. Every one of Google’s seven agent-readability rules maps to an existing WCAG recommendation.

The pre-existing audit, applied with intent, covers both classes.

The Bookings Go Elsewhere Silently, And The OS Picks The Winner

When a Pixel 10 user says “book a haircut Tuesday at six” in late June, Chrome auto-browse picks a salon’s website. The agent doing the picking is the OS itself, not a third-party app the user chose, and the user did not select the picker any more than they selected which search engine the address bar uses.

If the booking succeeds, the user gets the confirmation, the salon gets the booking, the website is the default destination for the next agent-delegated booking too. If the booking fails, the user does not see a failure. The agent retries. The agent picks another salon. The booking goes there.

The salon whose website failed the booking never sees the user. There is no analytics signal. There is no abandoned-cart event. There is no “the agent timed out on your booking form” notification in Google Search Console. The traffic that did not arrive is invisible. Three months later, the owner notices bookings are down and cannot identify the cause.

Late June is the deadline, and the work to be ready is small. An audit takes a few hours, the fixes run a day or two for most websites, and the alternative is months of silent loss before the cause is even visible.

More Resources:

This post was originally published on No Hacks.

Featured Image: Yaaaaayy/Shutterstock

Ecommerce MGMT 0 Comments
Jun 8 2026
5 Content Marketing Ideas for July 2026

From America’s 250th birthday to back-to-school preparation, July 2026 offers plenty for content marketers seeking to connect the products they sell to how shoppers travel, celebrate, and prepare.

Content marketers produce articles, videos, and podcasts to attract, engage, and retain customers. Content underlies search engine optimization and social media marketing. But it comes with a challenge: an almost never-ending need for topics.

What follows are five ideas for your ecommerce content marketing.

America’s 250th Birthday

Photo of a preschool-aged girl running beside a beach holding a U.S. flag

For many Americans, the nation’s 250th anniversary is the most important event of the summer.

On July 4, 2026, the United States will mark its Semiquincentennial, setting the stage for a nationwide celebration that promises to capture the spirit of many Americans eager to participate.

For example, some 62% of Americans surveyed said the anniversary is important, while 88% view it as a time to celebrate the nation’s history, and 85% say it is an opportunity to recognize the country’s achievements and values, according to research from M Booth, a marketing agency.

The occasion offers merchants an opportunity to connect their goods with American history, craftsmanship, and national pride.

Content should not focus on politics. Rather, merchants can tell stories about the people, companies, and traditions behind the products they sell. Shoppers often appreciate knowing where those items come from, who makes them, and the values they represent.

Here are a few example topics for articles, videos, or podcasts.

  • Apparel retailer: “7 Clothing Brands Made in the U.S.”
  • Tool shop: “How American Toolmakers Helped Build a Nation”
  • Kitchen supply store: “5 American-Made Kitchen Products Built to Last”
  • Furniture store: “The Craftsmanship Behind American-Made Furniture”
  • Outfitter: “Celebrating the Great Outdoors with American-Made Gear”

FIFA World Cup

Photo of a soccer team in a huge area

The FIFA World Cup is the world’s most popular sporting event. Image: FIFA.

The 2026 FIFA World Cup concludes on July 19 with the final match in New Jersey.

In June and July, matches will occur across Canada, Mexico, and the United States. The tournament will likely dominate sports coverage and attract millions of fans to parties, restaurants, and stadiums.

The content marketing opportunity goes beyond the matches and extends to the surrounding activities. That includes party planning or even the inspiration to start exercising or playing soccer. It’s a shared cultural moment that nearly all of North America and the world can enjoy together.

Here are some ideas.

  • “International Dishes to Serve at a World Cup Party”
  • “How to Host a World Cup Watch Party.”
  • “5 Soccer Drills Inspired by the World’s Best Players”
  • “Cocktails from around the World for Match Day”
  • “What Fans Wear to the World’s Biggest Sporting Event”

Summer Road Trips

Male and female in a convertible auto driving alongside the ocean

Americans like summer road trips.

July is road trip season. Friends and families load the car for vacations and weekend getaways. Outdoor enthusiasts enjoy national parks, campgrounds, beaches, and mountain trails.

The opportunity is significant. As airfare costs remain a concern for many households, road trips offer a flexible and relatively affordable way to vacation.

Ecommerce content marketers can help travelers prepare by focusing on packing, organization, vehicle preparation, and gear selection — while also introducing products naturally.

Here are a few example headlines.

  • Travel gear store: “Ultimate Checklist for Summer Road Trips”
  • Automotive products retailer: “7 Vehicle Checks before a Road Trip”
  • Camping supplies shop: “Essential Gear for a Camping Road Trip”
  • Consumer electronics store: “Travel Gadgets to Ease Long Drives”

National Disco Day

Manu Dibango holding a microphone

Pictured in 2019, Manu Dibango is an important influence on disco.

July 2, 2026, is National Disco Day. The origins are unclear, but the occasion is a grassroots celebration of the music and the cultural movement that emerged from American dance clubs in the 1970s and spread worldwide.

The topic lends itself to articles, videos, or podcasts that focus on disco’s history and artists. For example, a retailer selling audio equipment might profile Manu Dibango, whose Afro-funk track “Soul Makossa” featured some of the first looping polyrhythms vital to disco.

Back-to-School Planning

Photo of students entering a school

For many families, planning for a new school year starts in July.

School in the Northern Hemisphere typically begins in August or September, but preparation starts in July. Parents make lists, teachers prepare lesson plans, and students consider schedules, activities, and supplies.

Marketers can help families, teachers, and students before the rush with content that reduces stress, saves time, or simplifies planning. Here are a few examples.

  • “The Ultimate Back-to-School Checklist”
  • “How to Choose a Student Laptop in 2026”
  • “Creating a Productive Homework Space at Home”
  • “Back-to-School Wardrobe Planning Made Easy”
  • “Building a Family Command Center for the School Year”
Ecommerce MGMT 0 Comments
Jun 7 2026
What SEOs Should Read Before Labor Day, 5 Books For A Transformative Summer via @sejournal, @gregjarboe

Most summers, a reading list for SEO professionals is about thinking more broadly, stepping back from the day-to-day, and coming back in September with fresh perspective. This summer, it’s about keeping up. Because the gap between what you knew going into June and what you need to know by Labor Day is wider than it’s been in years.

Nobody in SEO still believes in set-it-and-forget-it. What practitioners need now is not philosophical preparation for change but concrete guidance on navigating a specific, unprecedented moment: the restructuring of search itself around generative AI. Google just completed the biggest overhaul of its search interface in 25 years at I/O 2026. The rules of content discovery, audience building, and visibility are being rewritten simultaneously.

That’s a lot to absorb. The books below won’t give you a checklist. But they’ll give you the frameworks, context, and competitive intelligence to make sense of what you’re already seeing in your traffic data, and what’s coming next.

Start Here: The Competitive Intelligence You’re Missing 

AI Valley: Microsoft, Google, and the Trillion-Dollar Race to Cash In on Artificial Intelligence by Gary Rivlin (Harper Business, 2025) is the backstory to everything currently reshaping search. Rivlin spent more than a year embedded with founders, investors, and engineers across Google, Microsoft, OpenAI, and the firms orbiting them. He followed the story from DeepMind’s early days through the ChatGPT moment and the scramble it triggered at every major tech company.

This is not a technical book. It reads like the best kind of corporate narrative journalism – specific people, real stakes, institutional chaos – and it gives you the context to understand why Google shipped its biggest search redesign in 25 years at I/O 2026 rather than taking its time. The competitive pressure Rivlin documents is why your search traffic looks the way it does right now. Understanding the pressure helps you anticipate what comes next.

For The Philosophical Foundation 

I Am Not a Robot by Joanna Stern, the Wall Street Journal’s tech journalist, not Gerd Gigerenzer, the German psychologist, is the book that I wrote about in “White-Collar Will Be Fully Automated In 18 Months – So What Makes You Different?” Stern spent a year using AI for as much of her life as possible and documented what transferred and what didn’t. For SEO professionals and content marketers who are trying to figure out which parts of their work to automate and which parts to protect, her year-long experiment is the most practical field test currently published.

John Kaag’s review in The Boston Sunday Globe identified the book’s deepest argument: the question “I am not a robot” has transformed from a CAPTCHA formality into a genuine philosophical claim about what makes human output worth producing. That question has direct implications for content strategy in an era when AI Overviews are serving a growing share of informational queries without a click.

For Understanding Audience Behavior 

The People’s Choice by Paul Lazarsfeld, Bernard Berelson, and Hazel Gaudet (1948) is the oldest book on this list and possibly the most relevant. Its central finding – that information flows from media to opinion leaders and then to followers, not directly from media to mass audiences – is the theoretical foundation of influencer marketing and the idea that reach and influence are not the same metric.

The finding is directly applicable to how brands need to think about AI search. When an AI Overview answers a query, the brand cited in that overview becomes an opinion leader in the old Lazarsfeld sense: an intermediary whose authority gives the information credibility before it reaches the end user. Lazarsfeld showed in 1948 that this is how influence has always worked. The platforms changed. The human behavior didn’t.

For The Tactical [Machine] Layer

If AI Valley explains the competitive forces that reshaped search, and The People’s Choice explains why audience behavior outlasts every platform change, The Machine Layer by Duane Forrester, is where the reading list gets specific.

His framework for what he calls machine comfort bias is worth the price of the book on its own. AI systems, he argues, naturally favor sources that prove reliable over time because verifying trust costs fewer computational resources than guessing. That’s not a ranking factor in the traditional sense. It’s a different game entirely, one where consistent, structured, citation-ready content compounds in ways that keyword-chasing never did.

This is the most practitioner-facing book on the list. It’s a working guide for teams who need to understand how discovery actually functions in a world where the intermediary between content and audience isn’t a user clicking a link.

For PPC Practitioners Who Want Leverage, Not Hype

The AI-Amplified Marketer: Digital Marketing in a GenAI World by Frederick Vallaeys is the most practically grounded book on this list for anyone managing paid search. Vallaeys was one of Google’s first 500 employees and its first AdWords Evangelist. He helped build Quality Score, conversion tracking, and the early automation capabilities that most PPC practitioners now take for granted. He has been watching AI transform paid search from the inside for two decades, which gives his skepticism and his enthusiasm equal credibility.

I heard him speak at a conference in Boston on Thursday, where he walked through how agents and MCPs are turning AI from a content generator into an actual PPC workflow layer. The book covers the same territory in depth: where AI genuinely amplifies what an experienced marketer can do, where it breaks down without human judgment to steer it, and how to close the gap between the tool demos and the messy reality of running real accounts. If you’ve spent the past year accumulating AI tools without feeling meaningfully more productive, this is the book that diagnoses why.

The Reading Order I’d Suggest

Start with AI Valley to understand the competitive forces that created the current landscape. Move to The People’s Choice to understand why audience behavior is more durable than any platform change. Use I Am Not a Robot to ground the abstract in a specific human experiment that maps directly onto content strategy decisions you’re making right now. And then read The Machine Layer and The AI-Amplified Marketer for the tactical layer.

Or reverse the order entirely. The point is to arrive at Labor Day understanding something you didn’t know in June. The web isn’t going to stop changing while you’re on vacation. You might as well be reading about it somewhere comfortable.

As an extra bonus, Rand Fishkin is currently pre-ordering for his new book, Zero Click Marketing, which will launch in the fall and will be essential reading for later in the year.

More Resources:

Featured Image: hmorena/Shutterstock

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Jun 7 2026
Google’s New Guidance Claims Authority Over SEO, Tools, And AEO/GEO via @sejournal, @martinibuster

Google has published new guidance that canonicalizes itself as the single source of objective truth for SEO practices, including for AI SEO. The new guidance, published on Google Search Central, is Google’s strongest assertion of itself as the official source of information SEO best practices and SEO tools.

The new guidance affects:

  • Third-party SEO resources.
  • Third-party SEO tools.
  • Third-party SEO services.
  • Third-party data providers.

The effect of the new guidance is to assert Google as the authoritative source of resources, tools, SEO information, and SEO data.

The four main points of the new documentation are:

  1. Google Says It Is The Authority On SEO Advice
  2. Google Claims Authority Over AI Search Optimization
  3. Google Distances Itself From Third Party SEO Tools
  4. Google’s Recommends Itself For SEO Tools

Google Says It Is The Authority On SEO Advice

Google’s new guidance is specifically about third-party SEO tools and third-party SEO advice. It expressly asserts its own guidelines as the canonical source of truth about SEO and for the nascent practice of AI optimization.

The new guidelines insist on Google as the objective truth about SEO:

“While some advice is helpful, others may misinterpret or make claims about what “Google says” or how Google ranking systems work. In general, good advice either qualifies their claims as opinion based on data or experience, or backs up their claims by citing official Google Search guidance.

We recommend carefully evaluating any advice you might be considering implementing against our official SEO guidance, including our guidance on optimizing for generative AI, and making your own informed decisions.”

Those statements assert Google’s own documentation as the reference point for evaluating whether SEO advice is credible and worth implementing. That’s always been a good practice. What’s unusual is how strongly the new guidance asserts Google’s primacy over all SEO information.

Google Claims Authoritativeness Over AI SEO

The guidance applies the same canonicalization of objective truth to AI search optimization advice, by asserting Google’s advice as authoritative for AEO and GEO, as well as SEO in general.

Google specifically references advice related to AI optimization, specifically mentioning AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization).

Google’s new guidance essentially divides SEO information into two categories:

  1. Third-party SEO opinion based on data or experience.
  2. Google’s own guidelines and recommendations.

After setting up the us versus them comparison, it follows by strongly recommending its own guidance as the source of truth by which any other advice should be weighed.

The new guidance explains:

“There’s plenty of third-party SEO advice on the internet related to SEO, search listings, and AI experiences (sometimes called AEO for “answer engine optimization” or GEO for “generative engine optimization”). While some advice is helpful, others may misinterpret or make claims about what “Google says” or how Google ranking systems work.

In general, good advice either qualifies their claims as opinion based on data or experience, or backs up their claims by citing official Google Search guidance.

We recommend carefully evaluating any advice you might be considering implementing against our official SEO guidance, including our guidance on optimizing for generative AI, and making your own informed decisions.”

Google Distances Itself From Third-Party SEO Tools

The strongest language in the document is directed at third-party SEO tools and services that imply some level of Google approval.

Google lists examples of third-party SEO services, including sitemap tools, indexing tools, content generation services, ranking advice services, and tools that promise improvements for AEO and GEO.

It then states:

“Some of these services may be helpful in your work, while others may make claims or imply that what they do is somehow ‘acceptable’ or ‘approved’ by Google Search.”

Google follows that statement with a warning:

“Google doesn’t evaluate third-party services, so be wary of such claims and those making them.”

The guidance stops short of criticizing SEO tools in general. In fact, Google acknowledges that some may be useful. But it clearly distances itself from vendors and services that invoke Google’s name to imply endorsement, approval, or validation.

Google also reminds businesses that using a tool is not a shortcut to better rankings:

“Keep in mind that using a service or tool doesn’t guarantee ranking success.”

Google Says SEO Tool Data Is Not Google Data

Google also addresses what it describes as a common misunderstanding about SEO tool data.

According to the guidance:

“Some third-party services provide data that some users of those tools misinterpret as somehow being from Google.”

Google then explicitly states:

“Third-party tools don’t have access to our internal ranking data.”

The guidance continues:

“They can’t guarantee performance. Any predictions are their own and like predictions generally, may not happen.”

Google’s position is that SEO tool forecasts, scores, and performance predictions should not be confused with Google’s own ranking data or internal systems. This is the strongest distancing that Google has put between itself and third-party data providers.

Google Recommends Itself For SEO Tools

After warning businesses about third-party claims, third-party predictions, and third-party data sources, Google recommends using its own platform, Search Console.

Google states:

“Whether you use a third-party tool or not, we strongly encourage using our first-party tool, Google Search Console, which provides you with key information and data directly from Google Search itself.”

That recommendation ends the new guidance, which is expressly designed to assert Google as the ground truth about SEO, AEO, GEO, and SEO tools. The question to ask now is: Why is Google doing this?

  • Is there a new algorithm coming that will crack down harder on sites that practice SEO that diverges from Google’s own?
  • Or is Google just trying to assert its own information as the canonical source of SEO truth?

Google explicitly advises businesses to “think critically” about using third-party tools and third-party services (SEOs). The phrase “think critically” means to not take things at face value, to analyze and question the information.

Looked at another way, it’s hard to ignore that this is Google’s strongest assertion of authoritativeness for SEO information.

How do you feel about Google’s new guidance? Your opinion matters.

Featured Image by Shutterstock/rasskazov

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Jun 6 2026
Google Gives Sites AI Search Opt-Out, But Not The Data To Use It via @sejournal, @MattGSouthern

Some websites can now opt out of Google’s AI search features without losing their place in standard search results. The UK’s Competition and Markets Authority imposed a conduct requirement this week, and Google began testing its own Search Console toggle the same day.

The real question is whether there’s enough information to make a decision. Google’s new AI performance reports in Search Console show impressions but not clicks. The CMA’s interpretive notes, published alongside the conduct requirement, say Google should also provide click-throughs, click-through rates, and data separated from organic search. That data isn’t in the reports yet.

How We Got Here

The CMA designated Google as having strategic market status in the UK search in October. In January, it opened a consultation on conduct requirements. That same day, Google said it was “exploring updates” to let sites opt out of Search generative AI features. By March, Google’s response to the consultation had changed the language from “exploring” to “developing.”

Before this week, there wasn’t a simple way to keep website content out of AI Overviews. A tag called Google-Extended lets sites opt out of AI model training and grounding, but the content could still appear in AI Overviews or AI Mode. There’s also the nosnippet tag that affects AI Overviews and AI search at the same time. You couldn’t opt out of one without losing the other.

In May, Google introduced AI search changes at I/O. The CMA’s final decision says it will “actively monitor” those changes. In June, the conduct requirement was imposed, and Google was testing its own Search Console controls with a subset of UK website owners.

Google hasn’t stated whether the Search Console toggle is intended to satisfy the CMA requirement. The company says it’s engaging with regulators like the CMA and testing the feature first with UK websites. That makes the UK the first market where both a regulatory requirement and a voluntary platform control for AI search are live at the same time.

What Arrived This Week

Three separate changes arrived this week.

The CMA’s conduct requirement, a legal obligation, requires Google to let publishers withhold content from AI search features and from AI model training. Google must clearly attribute domains in AI responses with links that let people reach the source. Importantly, it requires Google not to penalize websites that opt out.

Google’s Search Console toggle, a voluntary product change, lets publishers exclude their sites from AI Overviews, AI Mode, and AI Overviews in Discover at the domain level. Google confirmed it won’t use the opt-out as a ranking signal for standard search. Page-level controls aren’t available yet. The CMA has given Google until March 2027 to implement them.

Google also started rolling out AI performance reports in Search Console which show how often your pages appeared in AI features, broken down by page and country. Google notes it will add more data over time but hasn’t named what comes next.

Where The Data Falls Short

The reports don’t yet include all the data the CMA says publishers should receive for informed opt-out decisions.

The CMA’s interpretive notes list three kinds of data Google should provide. The first is impressions, showing when a publisher’s content appears in AI features. Google’s reports cover that.

The second is engagement data “including data on click-throughs to the publisher’s website from links in search generative AI features and a means by which publishers can easily identify those clicks, and therefore assess their ‘quality.’”

The third is click-through rate, defined as “the percentage of users who click on a link to that publisher within a Google search generative AI feature.”

The interpretive notes also say this data should be separated from organic search results and delivered “through a commonly accessible platform, such as Google Search Console.”

Google’s reports currently cover impressions. Click-throughs and CTR aren’t there yet. Whether Google adds click and CTR reporting before the imposed deadline is an open question.

SEO consultant Aleyda Solís noted on LinkedIn that the reports don’t “seem to include prompts / topics information or clicks data but … it’s a start.” Joy Hawkins, owner of Sterling Sky, was more direct on X: “I can only imagine why they wouldn’t include clicks.”

Glenn Gabe, president of G-Squared Interactive, captured the reaction: “AI reporting coming to GSC! Awesome! No click data. NOT Awesome.”

This isn’t a new complaint. SEJ has tracked Google adding more links to its AI results without releasing click data. Google VP of Search Liz Reid has described AI Overviews as removing “bounce clicks” rather than useful traffic. Without click data for AI features, publishers can’t test that claim. The difference now is that the missing data sits inside a regulatory process, not just an industry feedback loop.

Why This Matters

Freelance SEO consultant Natalie Arney connected both announcements on LinkedIn: “One gives publishers the exit door. The other shows what it would cost to walk through it.”

That’s the decision publishers face now. The opt-out exists, but the data to evaluate it is incomplete. A publisher that opts out before looking at AI visibility data may be giving up traffic it can’t yet measure. A publisher that stays in has more to learn from the new reports, but it’s working from impressions alone.

For anyone advising clients, the AI performance reports give the first dedicated view of how a site shows up in AI search responses. That baseline didn’t exist a week ago. Once click data arrives, the picture changes. Agencies may be asked to help clients evaluate AI search participation by market, content type, and what the reports show.

The CMA’s goal goes beyond the opt-out itself. Its final decision describes the requirement as intended to put publishers “in a stronger position to negotiate content deals with Google.” A publisher with visibility data and a working exit option has more leverage than one locked in with no alternative.

The CMA’s requirements apply to results shown in the UK. Google is also testing the Search Console controls with UK sites first. But Google has said it plans to roll both out globally. The EU’s Digital Markets Act covers some of the same territory, and the DOJ’s proposed remedy in the US antitrust case included a publisher opt-out provision. How the UK rollout works will inform those conversations.

Looking Ahead

The conduct rule takes effect immediately, while other obligations start in December. The nine-month implementation for page controls points to early 2027. The CMA will announce further action on Google’s search business in the coming weeks.

Google’s reports currently cover impressions, but the CMA expects click-throughs and CTR. Whether the reporting catches up in time for publishers to make informed decisions, which will determine how helpful the tool is.

More Resources: 

Featured Image: Marijus Auruskevicius/Shutterstock

Ecommerce MGMT 0 Comments
Jun 6 2026
Your Next AI Visitor Will Know Who Sent It via @sejournal, @slobodanmanic

The agent visiting your website knows the person who sent it.

That is the shift underneath Google’s Gemini Deep Research Max, launched on April 21, 2026, as a public preview on the paid Gemini API tier. Deep Research Max itself is a narrow rollout. The pattern it ships is a preview of what the agentic web becomes when the other major vendors follow, which they typically do within a quarter or two on capabilities like this. When a blended-retrieval agent runs, it arrives with private context: the user’s financial data, their file stores, their connected professional data streams, all fused into the query before the agent reaches any page.

For web professionals, this is the next chapter of the agentic web story. The claim that agents are a new primary visitor class has held for months. The claim has since evolved. Agents are a new primary visitor class with private context. The reasoning that decides whether your page answers a query runs on a larger input set than your page. The weight the agent gives your content depends on whether it adds anything the private sources did not already provide. This is the blended-retrieval moment in the agentic web story, and it lands on the supply side of how agents fetch, not on the user-facing product layer.

The old AI-search optimization posture (write content that matches the keyword query) was weakening before this. It weakens further now. The new posture is structural predictability: clean entity relationships, canonical identity, live data, rendering independence. Structure matters to the agent functionally. When the agent arrives with context, the content it picks is the content its model can fuse cleanly with everything else it already has.

Blended Retrieval Previews The Agentic Web’s Next Layer

Google’s Gemini Deep Research Max, in public preview on the paid API tier from April 21, can pull from four input classes in a single reasoning loop: the public web, file uploads, connected file stores, and arbitrary remote MCP servers. From Google’s own announcement, the agent “searches the web, arbitrary remote MCPs, file uploads and connected file stores, or any subset of them.”

The two new classes (file stores and remote MCPs) share one property. They are private by default. The agent reads them only through user consent. Once connected, a financial data provider or an enterprise CRM exposes its data to Gemini through the Model Context Protocol, Anthropic’s open standard with over 97 million installs as of March 2026. Google’s agent retrieves from those private sources with the same reliability it reads the open web, inside the same reasoning pass.

This is the structural move everyone watching the agentic web has been waiting for a major vendor to ship: public web and private context, fused by the agent, inside a single query. Gemini is the first.

The pattern is also not here for most operators yet. Deep Research Max is a public preview behind a paid API, not a feature in the consumer Gemini app. Most websites will not be read by a blended-retrieval agent this quarter. What Google announced on April 21 is the direction, not the arrival. Treat it as a leading indicator: If this architecture scales, and major vendors generally copy each other within a quarter or two on capabilities like this, the operator work gets real before the traffic does.

Signal Share Collapses When The Agent Has Better Alternatives

In a blended-retrieval query, every connected source competes for signal share: the open web, the user’s file stores, and any private MCP servers. The weight any single source gets is proportional to how cleanly the agent can extract and fuse its signal with everything else the agent is holding.

For public websites, this shifts the competitive terrain in two ways.

First, machine-first websites win more citation share. A page with clean structured data, unambiguous entity relationships, and rendering that does not hide content behind JavaScript is easy for the agent to merge with the user’s private context. The fused answer references the machine-first page because that page contributed usable, mergeable material.

Second, poorly structured websites lose signal share they used to get for free. In a web-only era, even a messy page could surface in a citation because there was no better public-web alternative. In the blended-retrieval era, the alternative may be the user’s uploaded documents or a connected MCP with cleaner data. The messy content page loses the citation share it used to split with clean sources.

This is a different competition from classical SEO. Classical SEO ranked pages against each other. Blended retrieval ranks pages against the user’s own context. You cannot see the competing sources. You can only make sure that when the agent reaches your public page, the page contributes something extractable and unambiguous.

Structured Product and Offer schema gets cited more often than unstructured descriptions when the user’s private context touches anything related. Canonical identity, clean entity relationships, and rendering independence all become higher-leverage when the agent is fusing signal across sources. The Adobe Q1 2026 AI traffic inversion was the demand-side proof that structured commerce wins in AI search; blended retrieval is the supply-side mechanism driving the same effect into the rest of the web.

The Honest Counter-Read: Some Queries Route Around Your Website Entirely

Not every blended-retrieval query will end up citing a public website. Some queries will be answerable entirely from the user’s connected sources. A financial analyst running Deep Research Max over an internal MCP server, plus uploaded quarterly reports, may never need the public web for that answer. That query’s traffic does not flow through anywhere; the answer is satisfied inside the private-context boundary.

This is a real subset. Most queries still blend public and private sources, because most analytical questions touch both.

Blended retrieval does not mean every website gets less traffic. It means the agent is choosier about what it uses. The bar rises for the sources the agent picks. Deep Research Max is a preview of what the agentic web is about to demand. Machine-first websites will pick up share when that scale arrives. Unstructured content will continue to lose it. Google showed us the pattern on April 21, but the scale that follows is where the real work for web professionals starts, and there is time to do that work before the traffic catches up.

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This post was originally published on No Hacks.

Featured Image: RobinRmD/Shutterstock

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Jun 6 2026
Google’s Updated Guidance Urges FTC Complaints Against Shady SEOs via @sejournal, @martinibuster

Google updated its guidance for businesses interested in SEO to make it concise and easier to read. There is also a new mention of AEO/GEO services, caution about the use of third-party SEO tools, and, for the first time ever, Google is encouraging businesses to contact the United States government Federal Trade Commission if they have a complaint about fraudulent SEO services.

There are about seven changes to Google’s “Do you need an SEO?” web page. The purpose of the page is to provide guidance on deciding whether to hire an SEO, factors to consider during the hiring process, and advice on avoiding unethical or risky practices.

The web page also encourages businesses to question whether they need to hire an SEO and offers links to resources for learning about SEO in order to better understand whether or not it’s necessary.

The new web page goes further than it has ever gone before. It now cautions businesses about the use of third-party SEO tools and encourages them to report shady SEOs to the FTC.

AI Optimization Added To List Of Useful Is Mentioned In New Guidance

Google added AEO/GEO services to their list of useful and typical services offered by SEOs.

The current list:

  • Review of your site content or structure
  • Technical advice on website development: for example, hosting, redirects, error pages, use of JavaScript
  • Content development
  • Management of online business development campaigns
  • Keyword research
  • SEO training
  • Expertise in specific markets and geographies
  • Optimizing for generative AI
  • Generative AI optimization is new to the list this year. There is no further guidance about this kind of optimization or a description of what this kind of optimization includes.

Content Rewritten For Clarity

Google’s encouragements to read their SEO guides were updated for clarity. . Some of the guide is extensively rewritten while some is only rewritten to be more concise. The rewritten guidance is essentially the same but clearer and easier to understand

Key Change: Google Discourages SEO Tools

One of the key changes to the guidance is an extensive section about third-party SEO tools. This isn’t something that Googlers have been talking about much but Google has actively been taking measures to discourage third-party tools from scraping Google search results.

Google doesn’t mention specific third-party tools but they do mention audits performed by the tools and advises businesses to compare tool recommendations against Google’s published guidance.

The new guidance and recommendations:

“If your SEO uses a third-party tool, keep in mind that Google doesn’t evaluate or endorse third-party SEO tools, and these tools don’t have access to Google’s internal ranking data. Be wary of tools that claim to be “acceptable” or “approved” by Google Search.

Evaluate your SEO’s recommendations and tools they use. Before making significant changes to your site based on a third-party tool’s audit, be sure to check their recommendations against official guidance from Google Search, think critically about any claims or recommendations you hear, and make your own informed decisions.
Do they cite official Google documentation as supporting evidence for their recommendations?”

Cautions On AEO/GEO Services

Google added AEO/GEO services to the list of the kinds of helpful services SEOs offer but they also published a warning about AI optimization services, advising businesses to make sure that SEO recommendations to step over the line between optimization and spam.

The new guidance:

“If they have advice on optimizing for AI experiences (also known as “AEO” “GEO” services), is their advice aligned with Google Search’s official guidance on optimizing for generative AI features?

Do they use tools that are aligned with Google’s guidance?”

Claims and Guarantees

Google rewrote the section about ranking guarantees. It’s substantially the same but more direct, concise and easier to understand.

“No one can guarantee a #1 ranking on Google. Beware of SEOs that claim to guarantee rankings, allege a “special relationship” with Google, or advertise a “priority submit” to Google.”

Warns About SEOs Who Violate Google’s Spam Policies

Google also rewrote the section about shady SEOs, cautioning that some SEOs are unethical, which Google defines as using “overly aggressive marketing” that violate the spam guidelines.

The updated guidance now says:

“Important: While SEOs can provide clients with valuable services, some unethical SEOs have given the industry a black eye by using overly aggressive marketing efforts or using techniques that violate our spam policies, which may result in a negative adjustment of your site’s presence in Google, or even the removal of your site from our index.”

Google Encourages Reporting SEOs To The FTC

Many SEOs see their practices in the light of whether or not they violate Google’s guidelines. But in fact it has always been the case that there are laws in the United States about advertising practices that may make some link building techniques (paid links) possibly illegal due to FTC guidelines that require “native advertising” content to be clearly labeled.

So, being cavalier about whether or not Google “likes” or “hates” how they promote a site has always been the least important thing for SEOs to worry about. Google’s new encouragement that businesses should report SEOs who use deceitful practices should give some SEOs a reason to reconsider their practices.

The new guidance says:

“Reporting issues
If you feel that you were deceived by an SEO in some way, you may want to report it.

In the United States, the Federal Trade Commission (FTC) handles complaints about deceptive or unfair business practices. To file a complaint, visit the FTC website to file a complaint online or call 1-877-FTC-HELP.

If your complaint is against a company in a country other than the United States, file it at https://www.econsumer.gov/.”

Takeaways

Google and SEOs have always had an adversarial relationship. The so-called white hat SEOs, despite representing themselves as ethical, have consistently been the ones testing the boundaries of Google’s algorithms to identify loopholes. For example, when Google introduced the nofollow link, originally created for disavowing links posted in comments, the white hat crowd started using it for “Page Rank sculpting,” a way to stop Google from counting “useless” pages like About Us pages in the calculation of how PageRank is distributed within a site. Google updated how nofollow links are treated by including them in the calculations of how PageRank is distributed.

Google’s relationship with the SEO industry appears to be updated now. Google is acknowledging AI optimization as a legitimate service while simultaneously warning businesses about AI optimization claims, third-party tools, and unethical SEO practices. The FTC reference is especially notable because it moves the discussion beyond Google’s guidelines and into legal territory.

Circling back to the traditional adversarial relationship between Google and SEOs, this update to Google’s “Do you need an SEO?” dials up the heat on SEOs who offer shady services.

Featured Image by Shutterstock/Blueastro

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