A Tale of Two Commerce Protocols

In previous posts I discussed the advent of Agentic Commerce and how that is primed to become the new way to shop for products online.

In order to enable the AI platforms to be aware of your brand presence and product information there are a number of strategies and techniques, specifically GEO (Generic Engine Optimization) and AEO (Answer Engine Optimization), that can attract the AI bots to prefer your brand and recommend your products within the many conversations that customers are now having with AI applications.

GEO is a broad strategy that involves a number of techniques that involve changes in how you write product content and optimize your websites so that AI will pick you first as the authoritative source for the answers within the Agentic Commerce experience.

Very recently a couple of new developments have emerged that both sound like it’s attempting to answer a similar question. Namely OpenAI’s Agentic Commerce Protocol (or ACP) and Google’s Universal Commerce Protocol (or UCP).

OpenAI’s ACP is an open, cross-platform protocol designed to enable shopping and payments directly within AI assistants, independent of any single platform or user interface. It allows AI agents to discover products via merchant-provided feeds, surface accurate pricing and availability, and autonomously initiate checkouts on the user's behalf without redirecting them to an external website.

The checkout process uses secure, delegated payment tokens (which are single-use, time-bound, and amount-restricted), while ensuring that the merchant retains full control over settlement, refunds, chargebacks, and compliance. The first implementation of this protocol is the Instant Checkout experience within ChatGPT.

Google’s UCP is a new open standard designed to establish a common language that allows AI agents, businesses, and payment providers to work together across the entire shopping journey from product discovery to post-purchase support. They also have massive Industry Endorsement collaborating with the likes of Etsy, Shopify, Best Buy and Walmart (US) who are either implementing, or have gone live with AI Agents.

While it is designed to be compatible with other agentic protocols, UCP is initially rolling out exclusively on Google-owned surfaces, such as Search AI Mode, Google Shopping, and the Gemini App. It enables shoppers to buy from eligible retailers directly during product discovery without leaving Google, utilizing Google Pay for seamless transactions while the retailer remains the seller of record.

Why ACP/UCP are More Helpful Than AIO/GEO

While Artificial Intelligence Optimization (AIO) and Generative Engine Optimization (GEO) are critical strategies, they are fundamentally focused on top-of-funnel visibility. AIO and GEO ensure that an AI model correctly parses, embeds, and cites your brand as the "source material" when answering a user's question. However, simply getting found is only the first step of the commerce journey.

ACP and UCP are arguably more helpful because they bridge the gap between discovery and execution, transforming the entire commercial funnel:

• Moving from Recommendation to Action: AIO/GEO might prompt an AI to recommend your product, but the user still has to navigate to your site, browse, add to cart, and manually checkout. ACP and UCP grant the AI "agency" to act on the user's intent and execute the purchase directly within the conversational interface.

• Frictionless Shopping: Traditional e-commerce is linear and rigid (search → browse → filter → product page → cart → checkout). ACP and UCP collapse these steps into a natural dialogue, drastically reducing friction and lowering cart abandonment.

• Capturing Immediate Revenue: By allowing shoppers to move from intent to purchase without breaking context or leaving the app, these protocols turn high-intent discovery moments directly into revenue.

In short, AIO and GEO help AI talk about your product, but ACP and UCP allow AI to buy your product on the customer's behalf.

Which one to choose?

Both OpenAI's Agentic Commerce Protocol (ACP) and Google's Universal Commerce Protocol (UCP) share the same overarching goal: to reduce friction in the shopping journey by allowing AI agents to handle product discovery and checkout seamlessly, without redirecting the user to an external website.

However, they differ significantly in their execution environments, how they handle payments, and their initial scope.

OpenAI’s Agentic Commerce Protocol (ACP)

• Design & Environment: ACP is an open, cross-platform protocol built to enable shopping and payments directly within AI assistants. It is designed to be independent of any single platform, user interface, or distribution surface. Currently, its primary implementation is the "Instant Checkout" experience inside ChatGPT.

• Payment Mechanism: ACP initiates checkout on the user's behalf using delegated payment tokens. These tokens are highly secure because they are single-use, time-bound, and amount-restricted.

• Merchant Role: In this model, merchants maintain complete control over the transactional backend, retaining responsibility for settlement, refunds, chargebacks, and compliance.

Google’s Universal Commerce Protocol (UCP)

• Design & Environment: UCP is pitched as a new open standard designed to support the entire shopping lifecycle, from product discovery and buying to post-purchase support. However, unlike ACP's cross-platform focus, UCP is initially being rolled out exclusively across Google-owned surfaces, including Search AI Mode, Google Shopping, and the Gemini App.

• Payment Mechanism: Instead of delegated tokens, UCP leverages Google Pay to complete transactions natively during product discovery, with PayPal support planned for the future.

• Additional Features: Alongside UCP, Google launched a feature called "Business Agent," which allows retailers to engage shoppers conversationally and enable direct purchases right within Google Search.

The Core Differences

• Where the Shopping Happens: ACP enables agent-led commerce primarily across the OpenAI ecosystem as a standalone destination, while UCP currently focuses on reducing checkout friction specifically within Google's massive search and discovery surfaces.

• Coexistence Over Competition: Google designed UCP to be compatible with other agent-to-agent standards and protocols. This means the two protocols are not necessarily meant to replace one another, but rather to coexist. UCP helps convert high-intent shoppers who are actively searching on Google, while ACP opens the door to new demand where AI chat assistants act as the shopping destination.

So it’s not like the old VHS/Beta video wars of the 80s. The question isn't which protocol "wins" it's whether your product data (and infrastructure) is ready to feed both. The reality is that you may need to support a multi-protocol ecosystem, just like supporting Apple Pay, Google Pay, and PayPal today. We are entering a multi-agent, multi-protocol world where structured product data is the "source code" of commerce.

Measuring Success in the Age of GEO

I am back after missing a week due to the day job! So, you devised your perfect GEO/AEO strategy and started writing your product content in conformance with the methodologies outlined in previous posts . Now comes the million-dollar question: Is it actually working?
Auditing your performance in the age of AI is tricky because the old scoreboard (Google Analytics) might be lying to you. Traffic might go down while your brand awareness goes up—simply because the AI answered the customer’s question without them ever needing to visit your site.
Here is a no-nonsense, friendly guide on how to audit your GEO and AEO efforts, the tools you can use, and how to fix the cracks in your strategy.

1. The "Ego Surf" Audit (Ask the AI)

The simplest way to audit your standing is to go directly to the source. You need to see if the "Generative Engines" (ChatGPT, Perplexity, Gemini, Claude) actually know who you are. Also, bare in mind that the AI models don’t reindex as often as the Google Search Index, so this is a long game.
The Action: Treat the AI like a potential customer.
Brand Audit: Ask, "What is {Your Company Name}?" or "What does {Your Company} sell?" If the AI hallucinates or says "I don't have enough information," you have an AIO (AI Optimization) problem. It means your digital footprint is too small or inconsistent.
Category Audit: Ask, "Who provides the best Service in {City}?" or "Compare {Your Product} vs {Competitor}".
The Goal: You aren't just looking for a mention; you are looking for sentiment and accuracy. Does the AI recommend you? Does it cite the right features? If it recommends a competitor, analyze why—is their pricing clearer? Do they have more reviews?

2. The Metric Shift: From Clicks to "Inclusion"

In traditional SEO, we obsess over Click-Through Rates (CTR). In AEO and GEO, we care about Source Inclusion and Visibility Scores.
Zero-Click Visibility: You need to track how often you appear in "Featured Snippets," "People Also Ask" boxes, or AI overviews. Tools like AIOSEO (for WordPress) or SEMrush can help track these specific SERP features.
Position-Adjusted Visibility: This is a fancy term for a simple concept: Did the AI mention you early in its answer? Research suggests that visibility is measured not just by if you were cited, but where and how much of your content was used. You want to be in the first paragraph of the AI’s script, not a footnote at the bottom.

3. The Toolkit: What to Use

You don't need to invent new technology to do this, but you do need to use existing tools differently.
AIOSEO (All In One SEO): If you are on WordPress, this plugin has a "Search Statistics" module. It helps you track keyword rankings specifically for content performance and identifies "content decay" (when your old posts stop ranking and need a refresh).
Using tools such as AIClicks and Profound, track AEO performance and monitor which products appear in AI citations, which content gets extracted most often, and what language patterns work best. Use these insights to refine your content templates, adjust attribute structures, and improve descriptions across similar products. Once you identify effective AEO patterns.
Question Research Tools: Use AnswerThePublic, SEMrush, or even your own customer support tickets. These tell you exactly what questions people are asking. If you aren't answering these specific questions on your site, you are invisible to the Answer Engine.
GPT-4 (as an Auditor): You can actually feed your content into ChatGPT and ask it to evaluate it against Google’s E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) standards. Ask it, "How would you rate this article’s authority compared to Competitor {URL}?".

4. Corrective Actions: How to Fix Your Strategy

So, you audited your site and the AI is ignoring you. Here is how to get its attention.

Fix #1: The "Answer First" Adjust (AEO)

If you aren't winning featured snippets or voice search results, your content is likely buried.
The Fix: Rewrite your headers as questions (e.g., "How long does a drill battery last?") and provide the answer immediately in a concise, 40–60 word paragraph directly underneath. No fluff, no backstory. Just the answer.
Technical Boost: Use Schema Markup (like FAQPage schema). This is code that screams to the robot, "Here is the answer!" Tools like AIOSEO can generate this for you without you needing to code.

Fix #2: The "Citation Magnet" Move (GEO)

If the AI summarizes the topic but doesn't mention you, your content lacks authority signals.
The Fix: Add hard data. Don't say "Our software is fast." Say, "Our software processes data 30% faster than the industry average," and cite a source or internal study. Adding citations and statistics can increase your visibility in AI answers by 30-40%.
Quote Experts: Include direct quotations from industry leaders or your own experts. AI loves to pull quotes to build its "script".

Fix #3: The "Consensus" Cleanup (Off-Page Audit)

This is the big one. AI doesn't just trust your website; it trusts what the rest of the internet says about you. If you have great content but terrible reviews on Yelp or G2, the AI might skip you.
The Fix: Audit your N.A.P. (Name, Address, Phone) across all directories. Inconsistency confuses the AI. Then, actively drive happy customers to leave reviews on third-party sites. The AI looks for "consensus" across the web to verify you are a legitimate recommendation.

Summary Checklist

Ask the AI: regularly prompt ChatGPT/Perplexity to see how it describes your brand.
Track Snippets: Monitor how often you appear in "People Also Ask" or AI Overviews.
Inject Facts: Audit your top pages—if they are full of fluff, replace them with stats, tables, and direct answers.
Check the Vibe: Ensure your off-site reviews and directory listings are squeaky clean.

If you do this, you stop chasing clicks and start building the "influence" that gets you cited as the expert, but remember that this is built over time. Be patient!

Mastering the Art of Answer Engine Optimization (AEO)

Think of Answer Engine Optimization (AEO) as a sub genre of GEO which I explored in my previous post while we walk through some of the details and have a closer look into what it’s all about…

The Cheat Sheet: AEO vs. GEO

Before we dive in, let’s clear up the alphabet soup. Both strategies want AI to notice you, but they play different positions on the field.

• AEO (Answer Engine Optimization): This is about being the direct answer. When someone asks a specific question (e.g., "How long does a drill battery last?"), you want the AI to read your specific sentence verbatim as the solution. It’s about winning the "Featured Snippet" or the voice answer on Alexa/Siri.

• GEO (Generative Engine Optimization): This is about being the recommendation. When someone asks a complex question (e.g., "Best drills for contractors"), you want the AI to synthesize your content with others and cite you as an authority in its custom-written essay. It’s about influence and reputation.

Think of it this way: AEO is writing the summary on the back of the book so the librarian can instantly answer a quick question. GEO is ensuring your book is cited in the librarian's research paper.

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AEO: The Art of the "Zero-Click" Win

We are moving toward a world where people don't want links; they want answers. If your customer asks, "Why is my power drill not working?" they don't want to read your company history. They want to know if they need to replace the battery.

AEO is the art of structuring your content so clearly that an AI (like ChatGPT, Google’s AI Overview, or Siri) looks at it and says, "This is the perfect answer," and serves it up on a silver platter, often without the user ever clicking your website.

Here is the playbook for getting your content chosen as the "Answer."

1. The "Answer First" Rule (Don't Bury the Lead)

To reiterate what I discussed in my previous post, LLMs (Large Language Models) are impatient. If you write a 2,000-word blog post where the actual answer is buried in paragraph twelve, you lose.

• The Move: Identify the specific question your customer is asking and answer it immediately in a clean, 40–60 word paragraph at the very top of your section.

• The Example: Let's say you run an HVAC company.

• Bad AEO: Starting with "Drilling into steel and concrete is one the most challenging mediums that stress your drill operability…..."

• Good AEO: Create an H2 header: "Why is my power drill not working?" Immediately follow it with: "The most common reason for a power drill not working is due to poor battery health after a long period of heavy usage."

• Why it works: You gave the AI a perfect, bite-sized snippet it can steal and read aloud to the user.

2. Product Titles & Descriptions That Actually Talk

Generic product pages are AEO killers. If you just list "Model X Drill" and a price, the AI has nothing to say. You need to anticipate the follow-up questions.

• The Move: Rewrite descriptions to proactively answer questions about specs, usage, and problems.

• The Example:

• Bad AEO: "Cordless Power Drill. High quality."

• Good AEO: "This Cordless Power Drill features a 20-hour battery life on a single charge and is water-resistant, delivering a massive 1,400 in-lbs of torque"

• Why it works: You just answered "How long is the battery?", "Is it water-proof?" and “How much torque does it have?” in one sentence. The AI can now match your product to those specific queries.

3. The Q&A Format (FAQ Pages on Steroids)

AI models love the "Q&A" format because it mimics how they are trained. You can force your way into the conversation by structuring data exactly how the AI wants to see it.

• The Move: Create "Question/Answer" pairs. Don't just rely on paragraphs; use an FAQ list where the question is an H3 header and the answer is body text.

• The Example:

• Q: "Is the Milwaukee FPD3 a hammer drill?"

• A: "Yes, the Milwaukee M18 FPD3 is a percussion/hammer drill designed for drilling into brick, concrete, and masonry."

• Why it works: You are literally feeding the robot the script. This creates "prime fodder" for AI overviews and voice search results.

4. Speak the Robot’s Language (Schema Markup)

This is the technical bit, but it’s crucial. You need to use code to tell the search engine exactly what it is looking at. This is called "Schema." and we will visit this in future posts, it’s something at the top of my list to understand further.

• The Move: Use "FAQPage" schema or "Product" schema. This puts invisible labels on your content that shout, "Hey Google, this text here is a price," or "This text here is an answer to a common question."

• The Result: It makes it incredibly easy for the engine to index your content as a verified answer, drastically increasing your chances of showing up in rich results and AI summaries.

The Bottom Line

AEO is about utility. It’s about accepting that your website might not be the destination anymore—it’s the database the AI uses to do its job. Be concise, be factual, and answer the question before the user has a chance to scroll.

What is Agentic Commerce?

What Agentic Commerce Means for the Regular Consumer

My day job involves working for a software company that develops a PIM (Product Information Management) solution. Maybe I will reveal what company that is in future posts, but I'd like to keep this fairly generic and sales free, and that way I may learn a thing or two about any other competitive systems too. The point is that this blog will possibly start off with a bit of a skew toward product content, selling products online and how AI is helping in that process. But I’m just going to meander and see what I uncover and hopefully it might be interesting to someone…

The first big topic that everyone is talking about (in my world) is Agentic Commerce and for the average shopper this represents a shift from "Do It Yourself" to "Do It For Me."

In the current world of e-commerce, the burden of work falls on you. You have to think of keywords, type them into a search engine, open ten different tabs to compare prices, read through reviews to check for quality, manually filter for your size, and finally click through a checkout process.

Agentic Commerce changes this dynamic by introducing AI Agents. Unlike standard chatbots that just answer questions, these Agents have "agency"—the ability to reason, plan, and execute tasks to achieve a goal you set for them.

Here is what this looks like in practice for a consumer:

1. The "Personal Shopper" Experience Imagine you need a gift. Instead of browsing endless categories, you simply tell your AI Agent: "Find a birthday gift for my brother, under $50, something tech-related".

• Reasoning: The Agent understands the constraints (budget, interest, occasion). You may already have told it your shoe size and waist measurement to streamline the next step.
• Planning: It scans catalogs, checks reviews for reliability, and compares prices across different sellers instantly.
• Action: It presents you with the best options—or, if you trust it enough, simply buys the best one for you.

2. Complex Tasks Handled Instantly Agents shine when the request is complicated. If you say, "Book me a flight to Barcelona for Friday morning," the Agent doesn't just show you a list of links. It autonomously compares airlines, finds the best deal that includes baggage, books the ticket, and emails you the confirmation. It acts like a knowledgeable consultant rather than a search bar. It may even handle the rest of the experience by organizing your boarding pass, alerting you to any delays and later managing any insurance claims should they unfortunately arise.

3. Running Errands on Autopilot For repeat purchases, Agentic Commerce offers "set it and forget it" convenience. An Agent can notice when you usually buy cleaning supplies or cosmetics and automatically reorder them so you never run out, handling the transaction in the background.

4. The End of "Tab Fatigue" The most immediate benefit for a consumer is the removal of friction. Because the Agent can read product specifications, check inventory, and understand shipping policies instantly, you no longer need to hunt for this information. The Agent digests it all and provides a direct solution, making the actual act of shopping nearly invisible but highly effective.

It's happening now!

Well, kind of. While we are not yet seeing this agentic concept fully realised, I can assure you it is starting to emerge, at least in the US, where the waves are lapping at the shore.

As far as infrastructure is concerned, OpenAI and Google have produced specifications to enabled sellers to upload their product information. Stripe, Klarna and Mastercard are at the forefront of supporting the transactional side of things to allow machine-to-machine payments, effectively making the shopping cart part of the agentic experience.

Several retail giants (customer-facing agents) have integrated capabilities directly into their apps to move beyond basic search:

• Amazon (Rufus & "Buy For Me") : As of 2026, Rufus has evolved to include a "Buy For Me" feature. It can monitor prices and autonomously execute a purchase when a specific price threshold is met. It can also digitize a handwritten grocery list and populate a cart instantly.
• Walmart : Using the OpenAI protocol, Walmart allows users to build complex carts (multi-item transactions) through conversational agents that understand "standing intents" which are pre-defined, persistent user goals or purposes that an AI system is trained to recognize and respond to, for example "Always keep my pantry stocked with organic milk")
• Shopify (Sidekick & Agentic Storefronts) : In early 2026, Shopify launched "The Renaissance" update. Sidekick is now a proactive agent that manages merchant operations (inventory and promo generation) and distributes product data to third-party agents like ChatGPT or Perplexity for "headless" agentic checkout.

Specialized AI platforms or "agent-first" companies are where many consumers are now starting their shopping journeys:

• Perplexity (Buy with Pro) : Launched in late 2024 and expanded in 2025/2026, this tool allows Pro users to research a product and click a single button to have Perplexity's agent handle the checkout process on a merchant's site.
• Microsoft (Copilot Checkout) : Integrated into the Microsoft 365 and Edge ecosystem, this allows agents to "negotiate" with brand agents and secure delivery slots that fit the user's Outlook calendar as well as complete the shopping cart within the customer conversation.

And finally, many software vendors are implementing solutions to help B2B/B2C enterprises manage their own agentic selling experiences and deliver product information to it:

• Salesforce (Agentforce) : Actively used by B2B and B2C enterprises to automate order management, returns and exchanges. Salesforce agents can now autonomously process refunds and update ERP systems based on a customer's conversation.
• UiPath ("Autopilot" and "Maestro") : These platforms allow enterprises to build custom agents that handle procurement, invoice matching and automated restocking without human intervention.
• Agility PIM : Using its built-in AI Content Generation to upscale creation of answer-optimized product information that is now required to tackle new content strategies such as GEO and AEO.

What does this mean for online sellers?

So this leads to a final point about what it means for you to sell products online, you can no longer just rely on attracting customers to your websites, or even other affiliated or distributor sites. Traditionally this process was done by optimising your content through SEO (Search Engine Optimisation) that sought to rank your product pages as high up the Google search results as possible. But in an Agentic Commerce world your customers may never even set eyes on your product’s pages. So you must seek to be part of the AI conversation rather than a link to your product.

So my journey into this world will cover a bunch of topics that will try to unravel big topics that sellers will need to be aware of as they make this shift (I nearly said "paradigm shift" but I really need to avoid such cliches, they are not funny).

• Generative Engine Optimization (GEO & AEO) : Sellers can no longer just optimize for Google keywords (SEO). They must learn AEO (Answer Engine Optimization) and GEO, strategies focused on becoming the "source material" or "cited answer" for AI models like ChatGPT and Perplexity,. This involves structuring content so an AI can easily read, summarize, and recommend it.
• The New "Product Feed" Standard: To sell to an AI Agent, your data must be flawless. OpenAI and Google have released their own specific product feed specifications that require high-frequency updates and new data fields like "Mood" or specific use cases, ensuring the AI understands context, not just specs.
• Agentic AI PIM (Product Information Management): Preparing data manually for AI Agents is impossible at scale. Sellers should explore Agentic AI PIM, where AI tools autonomously clean, enrich, and structure product data to ensure it is trusted by shopping bots.
• Trust and Transparency Signals: In an era where a machine buys on behalf of a human, trust is currency. Sellers will need to explore how to present "Seller Policies" and transparency data (like return rates and privacy policies) directly to AI Agents to ensure they are selected as a "safe" purchase option.