I’m excited to share that I can now effortlessly integrate Google Search Console data directly into any of my Profound Agents. This powerful combination, uniting Search Console insights with Profound’s answer engine data, is transforming how I handle reporting, content creation, monitoring, and optimization.
Staying on the Profound platform makes the entire process seamless, allowing me to focus on what truly matters—building and optimizing my digital strategies without the hassle of platform switching.
I’ve just learned that Perplexity AI’s Comet browser agent can no longer make purchases on Amazon. This decision comes after a federal judge ruled in Amazon’s favor, expressing concerns about AI shopping bots.
Why this matters to us. The ruling challenges AI’s ability to simplify tasks, such as online shopping, by acting on our behalf. If similar restrictions are enacted, AI agents might face significant hurdles when trying to access logged-in areas of popular platforms.
The situation as it unfolded. U.S. District Judge Maxine Chesney in San Francisco issued a preliminary injunction, favoring Amazon’s position.
Perplexity is now prohibited from using Comet to enter password-protected sections of Amazon, like those reserved for Prime members.
Judge Chesney noted Amazon’s “strong evidence” indicating Comet’s access was granted by users but not authorized by Amazon itself.
The court order also mandates that Perplexity must eliminate all Amazon data it has gathered.
Getting up to speed. Back in November, Amazon filed a lawsuit against Perplexity, accusing it of computer fraud and unauthorized platform access. Allegedly, Comet completed purchases on user accounts without properly identifying itself as a bot.
Next steps. There’s a one-week suspension on the order, giving Perplexity the chance to appeal.
What Amazon says. According to Lara Hendrickson, an Amazon spokesperson, this injunction is crucial for stopping Perplexity’s unauthorized Amazon access and is a vital move towards maintaining trust for customers.
I recently came across an intriguing study that shows AI tools are now responsible for generating 45 billion monthly sessions globally. This accounts for an impressive 56% of all search engine activity, according to Graphite.io CEO Ethan Smith.
The analysis combines web and mobile app usage across leading AI platforms and suggests that AI activity matches 56% of global search use and 34% in the U.S.
The surge is particularly evident in mobile applications like ChatGPT, Gemini, Perplexity, Grok, and Claude.
Why it matters: AI is broadening the horizons of discovery, rather than limiting the demand for search. Since 2023, combined usage across search engines and AI assistants has increased by 26% globally. It’s clear that having visibility in both LLMs and traditional rankings is crucial.
Key insights: The report dives into the performance of the top five LLM products—ChatGPT, Gemini, Perplexity, Grok, and Claude—and compares them to the biggest search engines. Here are some standout insights:
AI platforms generate 45 billion monthly sessions worldwide.
Within the U.S., AI accounts for roughly 5.4 billion monthly sessions.
An astounding 83% of global AI usage takes place within mobile apps (75% in the U.S.).
ChatGPT is leading the charge, representing 89% of AI sessions globally.
When looking at search-like prompts, AI usage constitutes 28% of the global search and 17% within the U.S.
The report leaves out prompts in the “doing” or “expressing” categories. According to OpenAI, around 52% of prompts focus on seeking information, akin to traditional search queries.
Reading between the lines: Most forecasts comparing AI and search focus only on website traffic, often just Google.com and ChatGPT site visits. This approach overlooks much of AI’s impact.
The research suggests these comparisons undervalue AI activity by a factor of 4-5 times because a significant chunk occurs on mobile apps.
The analysis takes into account various LLMs and search engines, rather than only comparing Google and ChatGPT.
What to keep an eye on: Google remains a dominant force in discovery, but the report estimates its share of search-related activity has declined from 89% in 2023 to 71% by the fourth quarter of 2025.
While global AI usage seems stabilized since July 2025, the U.S. usage is still on a rapid climb—up about 300% year over year by December 2025.
Recently, while exploring the latest developments in web technology, I stumbled upon something groundbreaking: WebMCP, introduced in Chrome 146. Being a tech enthusiast, I was intrigued to learn how this emerging protocol could reshape the way AI agents interact with websites.
Chrome 146 has rolled out an exciting early preview of WebMCP, hidden behind a flag. This protocol, known as Web Model Context Protocol (WebMCP), is designed as a web standard to lay out website tools in a structured manner, guiding AI agents in executing tasks seamlessly.
So, what does this mean for us? Historically, the internet has been developed with humans in mind. Buttons, forms, and dropdowns are all elements we understand. But there’s an emerging user—AI agents. Soon, they will be completing registrations, purchasing tickets, and achieving other goals autonomously on websites.
Currently, AI agents face a daunting task. They navigate websites by crawling and attempting to decipher their functionalities. Imagine an AI agent trying to book a flight. It has to identify input fields, guess data formats, and pray nothing goes awry. It’s far from ideal.
The introduction of WebMCP is set to change this. By exposing the structure behind web tools, AI agents will be equipped to understand and execute tasks with ease.
Let’s dive a bit deeper to understand WebMCP. Picture yourself needing to book a flight.
Without WebMCP: An AI agent scrambles to find a relevant button like “Book a Flight” or “Search Flights.” It then interprets the on-screen information, hoping it inputs correctly.
With WebMCP: Forget searching for buttons. Instead, the agent calls a function, like bookFlight(), using well-defined parameters (such as date, origin/destination, and passengers), receiving a structured result in return. Much like developers interacting via APIs, AI agents will seamlessly call functions.
WebMCP empowers websites with JavaScript APIs and HTML form annotations, guiding AI agents on interacting with web tools in three steps:
Discovery: What tools does the page support? Examples include Checkout, BookFlight, or searchProducts.
JSON Schemas: They precisely define expected inputs and the kind of output that will be returned.
State: Tool availability alters based on the page’s state, allowing agents to only see actions relevant to the current context.
My website, for instance, could offer a list of actions each detailing its functionality, accepted inputs, returned outputs, and required permissions.
But why does this matter? AI agents are infiltrating our daily workflows rapidly. Soon, AI will handle our flight bookings, fill out forms, and publish content. But, as of now, AI agents struggle to interact seamlessly with websites due to two current approaches:
Automation (fragile): An AI acts by clicking buttons and inputting data like we do, but since websites frequently update, this can lead to failures.
APIs (limited): While APIs offer a structured approach for interaction, they’re not universally available or comprehensive.
WebMCP offers a middle ground, allowing websites to make tools accessible without the drawbacks of UI automation or needing separate APIs.
Like the early 2000s SEO race, WebMCP symbolizes a shift towards optimization for AI agents. Those who adopt this early could enjoy significant advantages as AI-centric search and commerce grow.
This opportunity is not merely about SEO anymore. It’s about realizing broader growth potential through WebMCP, which signifies not just being discoverable, but actionable by AI agents who’ll soon connect with future customers.
Practical applications of WebMCP in B2B and B2C scenarios are vast, from simplifying quote requests to expediting inventory checks, offering a seamless experience for business and everyday consumers alike.
To start experimenting with WebMCP, Chrome 146 lets you access it behind a feature flag. It’s still in its nascent stage but provides a valuable chance for developers and innovative teams to play around with the conceptual framework.
While getting acquainted with WebMCP, you’ll need Chrome version 146.0.7672.0 or later and a basic understanding of Chrome flags. Follow these steps to set up:
Navigate to chrome://flags/#enable-webmcp-testing in Chrome.
Set the “WebMCP for testing” flag to “Enabled”.
Relaunch Chrome.
Start experimenting with WebMCP today and perhaps even install the Model Context Tool Inspector Extension to witness WebMCP in action. It’s an exciting era we’re stepping into, enabling websites to be as understandable to AI as they are to us.
For years, I relied on a straightforward ecommerce model: Google attracted visitors to my site, where transactions were completed. Success was measured through rankings, clicks, and conversion rates. That scenario has drastically changed.
With Google’s Universal Commerce Protocol (UCP) combined with AI Mode, it’s possible for Google to uncover, evaluate, and finalize purchases within its AI framework. The dynamic is shifting from merely directing traffic to facilitating transactions. Now, the visibility of my products hinges on whether Google’s AI includes my data in its algorithm.
When AI can recommend and close sales, the optimization challenge moves even farther upstream. The vital question now isn’t just about my ranking; it’s about whether my products get chosen by AI.
So, let’s explore these changes and what strategies those involved in SEO and AI optimization should adopt next.
On January 11, Google introduced the Universal Commerce Protocol, or UCP. This innovative open standard empowers AI agents to explore, assess, recommend, and purchase products seamlessly across the web within Google’s own AI settings.
What caught my attention was not just UCP itself but the entire ecosystem Google devised around it. UCP was created in collaboration with platforms like Shopify, Etsy, Wayfair, Target, and Walmart, with pre-existing payment networks incorporated. This level of planning signifies a long-term vision, rather than a fleeting experiment.
Simultaneously, Google introduced three platform-level features that make this transformation tangible in everyday shopping experiences:
Business Agent: Brands now have an AI-powered ambassador in Search and the Gemini app. Shoppers can inquire about products, compare choices, and receive brand-specific advice without the necessity to visit a separate site.
Direct Offers: This feature allows merchants to incorporate exclusive discounts directly into Google’s AI Mode, embedding promotions within the recommendation engine itself.
Checkout in AI Mode: Google now facilitates purchases directly within its interface, transitioning from a traffic broker to an integral transaction facilitator.
What’s even more remarkable is how Google transforms routine conversations into commerce. Instead of waiting for users to type product-related queries, Gemini can respond to natural language prompts like “help me plan a camping trip” or “what will get wine out of my couch” by sourcing up-to-date inventory, pricing, and availability from retailers, completing the transaction in the same interaction.
In the era where AI navigates the purchasing journey, brands must compete within the AI’s recommendation system, not just in search results.
Throughout my career, ecommerce consistently functioned on a model where search engines, ads, and marketplaces aimed to divert users to my site, so it could handle the sales. UCP reshapes that perception entirely.
Now, AI takes charge of the complete journey. It understands the customer’s needs, assesses different options, and can even finalize the purchase. Under this model, the quality of my website’s homepage or category page matters less if AI doesn’t prioritize my product at the outset.
AI agents, shared signals, and fragmented identities are reshaping marketing intelligence, making it tough for most brands to identify real actors.
Somewhere in my CRM, lies a customer who doesn’t truly exist. They open emails at odd hours and redeem promotions with uncanny precision. They browse product pages across several devices within minutes. While they seem highly engaged on paper, they are likely a mixture of behaviors created by AI assistants, shared accounts, recycled addresses, autofill tools, and automated workflows.
This is what I call the Data Doppelgänger Problem—one of the biggest hidden challenges in contemporary marketing.
For years, we’ve treated identity resolution as merely a data hygiene issue. While cleaning data and removing duplicates are still important, the landscape has shifted. The major risk now comes from data that appears correct but isn’t.
Consumers are now using AI agents to perform tasks like summarizing emails, comparing products, tracking prices, filling forms, and even completing purchases. Shared credentials remain common, and privacy changes in browsers have pushed attribution models toward probabilistic methods. The rise in subscription commerce, loyalty programs, and cross-device behavior reveal a pattern of one individual generating multiple digital identities, while multiple actors generate activity appearing as one person.
The dashboard data no longer consistently reflects genuine intentions, but rather distorted, overlapping digital signals.
When High Engagement Misleads
In our marketing systems, engagement metrics like opens, clicks, and transactions are often proxies for value. But what if some of this engagement is synthetic?
Email clients prefetch content, AI tools summarize messages, and shopping agents track prices automatically, making these actions look like genuine high-intent behaviors in analytics.
When we consider recycled or shared email addresses, oddities surface. Dormant accounts might be reassigned, corporate aliases could forward emails to multiple users, and consumers might use alternate emails to exploit new user discounts. These all compromise identity credibility.
Optimizing campaigns based on inaccurate engagement data might detract from loyal customers, and active, valuable inputs might appear inactive due to fragmented identities. This misalignment could feed machine learning models wrong signals, further escalating problems.
This is where professional frustration kicks in. While dashboards seem intact and segments clear, conversion rates plateau, and fraud sneaks through legitimate-looking channels. Acquisition costs rise inexplicably because our problem is not effort—it’s identity confidence.
Doppelgängers and Operational Risks
The Data Doppelgänger Problem extends beyond marketing inefficiency into risk, compliance, and revenue protection. Much of what we think of as promotional abuse could actually stem from poor identity resolution, allowing a single person to appear as multiple new customers or vice versa.
As AI agents advance, the risk grows harder to detect. Automated assistants that act for customers might not be fraudulent, but they blur the behavioral signals distinguishing genuine intent from misuse.
While traditional systems check for anomalies, future risk might seem normal. Without distinguishing between stable and composite identities, controls become ineffective, either adding too much friction, deterring real customers, or not enough, encouraging exploitation.
To counteract this, we must move to continuous identity validation—understanding not just whether an email is deliverable, but how it behaves over time and integrates within a broader activity network.
Reevaluating the Golden Record
Many still aim for a unified data source, a ‘golden record’ that aligns identities into one profile. While tempting, this is increasingly impractical in a world of AI and shared signals. Identity isn’t a static snapshot but a moving target.
The key isn’t consolidating data into a single profile but assessing our confidence that the associated behaviors truly reflect one coherent person.
This sounds subtle but is crucial. Viewing identity as binary—either matched or unmatched—misses nuances. Treating identity as confidence-based allows us to prioritize higher-confidence interactions and manage ambiguity better.
Effectively, data becomes a strategic asset, not just a reporting tool.
Shifting Focus From Volume to Validation
Marketing tech has long idolized scale, emphasizing bigger lists and more signals. However, scale without validation creates misleading precision.
The Data Doppelgänger Problem prompts a crucial question: Is it better to have ten million records with unknown stability or eight million deeply understood records?
The frontrunners will not necessarily amass the most data but will hold the most reliable data, exemplifying continual validation, real-activity patterns and coherent cross-organizational integration.
Enhancing identity confidence improves targeting, which strengthens engagement quality. Stabilized attribution then fortifies reliable forecasts, leading to performance-driven budget allocation.
Although this positive feedback loop is effective, it’s fragile; unstable identities compromise the entire system.
Key Questions for Professionals
Leaders in marketing, analytics, or risk need to pivot from data access to critically assessing data integrity at scale.
How many active profiles truly represent coherent individuals?
How frequently are identities validated against new activities?
Can we detect identity fragmentation or convergence?
Are fraud controls geared to actual behavior or outdated behavioral assumptions?
These queries don’t signal panic but a necessary evolution, recognizing a matured digital landscape where tasks are more software-driven, devices are proliferating, and privacy demands have complicated identifiers.
Brands that will succeed will treat identity as an evolving construct, using advanced activity networks to anchor identity in its current reality.
They’ll cut acquisition costs waste, safeguard margins without alienating customers, and trust analytics—an understanding of the confidence behind metrics paving the way.
Critically, seasoned professionals need to identify these ‘customers’ within CRMs that don’t exist before budgets suffer the consequences.
Stepping into the world of automation has always intrigued me. It brings a level of efficiency that every SEO team craves. Today, AI agents like n8n are revolutionizing how we automate SEO workflows, from data scraping to structured delivery—plus, they have their set of challenges.
What makes n8n particularly captivating is its flexibility and control. Let me walk you through how this platform functions and how it can be harnessed in modern SEO operations.
Understanding How n8n AI Agents are Deployed
Think of modern AI agent platforms as a more intelligent version of Zapier. Platforms like n8n don’t just shuffle data between steps—they interpret, modify, and decide on the next move.
Starting with n8n involves choosing your deployment method: cloud-hosted or self-hosted. While letting n8n host your environment could sound appealing, it has its downsides:
The environment can feel limited.
Customization, like modifying server interactions, becomes difficult.
No community nodes can be installed or utilized.
Costs are usually higher.
But there’s a silver lining:
Less management is required—n8n takes care of updates and patches.
It’s user-friendly with little technical expertise required.
Maintenance stress is reduced significantly.
n8n offers various license packages. The self-hosted option is free, though it poses challenges for larger teams due to limitations in version control and change tracking.
How n8n Workflows Run in Practice
API credentials from providers like Google and OpenAI are necessary to leverage AI models and LLMs. Once installed, n8n’s interface is reminiscent of Zapier—a simple canvas for process design.
You can add nodes and pull data from external sources. Workflows can be triggered via webhooks, schedule, or another system interaction.
The executed workflows transmit outputs to places like Gmail, Microsoft Teams, or HTTP request nodes, triggering further n8n workflows or interacting with external APIs.
Take, for instance, a workflow that scrapes RSS feeds, generating a summarized update. It’s not a full-scale article, but it trims down recap times substantially.
Building AI Agent Workflows in n8n
Within a webhook trigger node, you can generate a webhook URL that Microsoft Teams calls, activating the n8n workflow. It streamlines requests for search news updates directly in a Teams channel.
Once the workflow runs, AI agent nodes communicate with LLMs like those from OpenAI and Google. This opens up numerous possibilities.
Variables from the scraping node, including content from multiple RSS feeds, get transferred to the prompt for summarization. Both user and system prompts guide the AI in processing and formatting this data.
While a single AI node handles summarization, a second node converts this summary into HTML, proving effective for specific tasks where dual AI nodes function best.
The summarized news is delivered through Teams and Gmail, offering a look at efficient workflow execution.
n8n SEO Automations and Other Applications
While I’ve shared a rather straightforward project, n8n’s capabilities extend much further in SEO and digital applications, such as:
Creating full-length, in-depth content.
Crafting meta and Open Graph data snippets.
Analyzing content from a UX perspective.
Developing simple SEO scanners.
And much more!
Inspired by a colleague’s comment, “If I can think it, I can build it,” I ventured into complex systems using n8n to meet the changing needs of SEO.
Drawbacks of n8n
Despite its potential, n8n isn’t without limitations:
Platform immaturity can lead to transaction hiccups during updates.
Resistance might stem from fears about job redundancy or ethics.
The focus should be on supplementing roles, not replacing them.
Its utility is limited in extensive technical audits or large-scale data analysis.
Beginning with repetitive or tedious tasks and automating them might be the key to reducing friction within your team.
SEO’s Shift Toward Automation and Orchestration
AI agents don’t replace human expertise, but they enhance it. They free us from mundane tasks, allowing us to focus on strategic areas, showing the positive shift in SEO toward automation rather than the discipline’s demise.
The evolution of tools may continue, yet the trend toward automation and orchestration is undeniable. Building proficiency in these systems is on the horizon as a vital skill for SEOs.
