How Do You Compete In Agentic Commerce?

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Agentic commerce transforms organic search from a source of cheap traffic into the mandatory gatekeeper of AI verification. Marketing arbitrage dies; product truth wins.

This week, we’re covering:

• Why agentic commerce filters out marketing-first brands and rewards granular product data.
• How ChatGPT, Copilot, and Google’s protocols reshape merchant economics and customer relationships.
• Which feeds to optimize, which protocols to prioritize, and the implementation sequence that matters.

Agentic commerce acts as a “great filter,” so to speak, for marketing arbitrage, transforming organic search from a source of cheap traffic into the mandatory gatekeeper of AI verification.

The signal is already visible in the noise. During the 2025 holiday season, AI agents powered 20% of retail sales. Even allowing for loose definitions, the era of agentic commerce has arrived.

All major LLMs now offer direct checkout and new commerce protocols:

1. ChatGPT has Instant Checkout with Shopify and Etsy, and ACP (Agentic Commerce Protocol).
2. Microsoft Copilot uses ACP and offers Copilot Checkout with PayPal, Shopify, and Stripe.
3. Google has embedded checkout in AI Mode and Gemini via its Universal Commerce Protocol (UCP).

The infrastructure question is settled, but the strategic question remains: How do you compete when users don’t need to click through to websites to buy?

1. Agentic Commerce Has A Hole In The Middle

The phrasing “agentic commerce” sets the wrong expectation. Autonomous purchasing, where you give an agent a credit card and monthly allowance to buy on your behalf, is not becoming a reality in the near future.

• High-priced purchases like plane tickets or cars are too risky to delegate. You have idiosyncratic preferences (airline seat rules, car features) that no agent can reliably model.
• Low-priced purchases like toilet paper or laundry detergent already have automation via subscription services (Instacart recurring orders, Subscribe & Save). An agent adds no incremental value.
• The middle ground is smaller than the hype suggests. If high-priced resists delegation and low-priced is already “automated,” where does autonomous purchasing actually generate value?

“Conversational commerce” is a better frame. Instead of 100% automating the act of buying, LLMs compress the funnel by offering far superior research to classic search engines and showing products in the user interface.

• Models read expert reviews, product specs, ingredient lists, and actual user feedback rather than ranking by keyword bids and conversion history.
• The value lies in collapsing 14 clicks (Amazon’s disclosed average before purchase) into one or two.

2. Protocols Make Ecommerce “Headless”

The new commerce protocols allow AI agents to directly plug into the backend of your business, instead of crawling your site to show them in a list of search results. Protocols make commerce “headless” and decouple the front from the back-end:

• Websites become less important as destinations and more important as databases.
• The game shifts from optimizing landing page design for human eyes to optimizing data feeds for machine ingestion.
• If your shipping speed, inventory status, or return policy isn’t accessible via API, you are invisible to the agent.

The shift from crawling to protocols collapses the legacy 14-click funnel (search, browse, click, checkout) into just two interactions: (1) the model parses intent by matching expert reviews against real-time inventory, and (2) the user executes a single click to buy using stored credentials.

While both protocols, ACP and UCP, enable the same user experience, they offer vastly different terms for the merchant.

OpenAI’s ACP (Agentic Commerce Protocol)

• The Vision: The “Walled Garden.” OpenAI aims to handle the entire transaction within the chat interface, treating merchants effectively as suppliers.
• The Trade-off: Efficiency vs. LTV. You gain access to 700 million weekly users, but you lose the direct customer relationship. Because OpenAI currently restricts passing customer emails for marketing, you lose the ability to remarket – effectively killing the 15-20% of Lifetime Value (LTV) that typically comes from post-purchase email flows.

Google’s UCP (Universal Commerce Protocol)

• The Vision: The “Distributed Layer.” Google extends its Shopping Graph into a transactional layer that sits on top of Search, Lens, and Gemini.
• The Trade-off: Ownership vs. Competition. Unlike ACP, Google allows merchants to retain the full customer lifecycle, including email rights and loyalty data. The cost is significantly higher competition intensity: Instead of fighting for 10 blue links, you are fighting for one of three “slots” in an AI Overview, making the margin for error in your product data effectively zero.

3. Conversational Commerce Disrupts The Whole Ecosystem

The shift from search to conversation creates a distinct set of winners, losers, and strategic dilemmas.

Buyers get a dramatically better user experience.

• Discovery: High-consideration purchases (e.g., specific running shoes) shift from clicking through six potentially irrelevant product listing ads to receiving top-tier recommendations based on expert reviews.
• Cognitive Load: The model handles the research, collapsing the average 14-click journey into one to two interactions.

Merchants face a tradeoff between distribution and control.

• On ChatGPT: You gain access to early adopters, but lose the direct customer relationship and email marketing rights. You have no leverage over commission rates or recommendation logic.
• On Google/Copilot: You retain merchant-of-record status, but as the funnel compresses, on-site ad inventory loses value. While conversion rates may rise, total ad revenue falls.

Affiliates die when LLMs disintermediate the click.

• The Trap: If ChatGPT synthesizes reviews without sending traffic, affiliates stop writing. This creates an “ouroboros” where models train on their own AI-generated output.
• The Pivot: Publishers must paywall premium content or charge merchants directly for reviews.

Amazon dominates on price and speed, but faces a business model conflict.

• The Conflict: Retail margins are thin (~1%); profitability comes from the $60 billion advertising business.
• The Risk: Amazon’s ad machine relies on a 14-click funnel. If conversational commerce compresses this to one click, sponsored product inventory evaporates.
• The Choice: They must either block crawlers to protect ad revenue (current strategy) or participate and cannibalize it. Walmart joining ChatGPT forces their hand.

Google is best positioned to weather the shift.

• Parity: They are already monetizing AI Overviews at parity with legacy search.
• Economics: Higher relevance leads to exploding conversion rates. Advertisers will pay more per click to offset the lower click volume, balancing the ecosystem.

4. SEO Shifts From Optimizing Clicks To Optimizing Ingestion

We are moving from a world of infinite shelf space (10 blue links, endless pagination) to a world of constrained shelf space (three recommendation slots in an AI response).

In this environment, SEO shifts from optimizing for clicks to optimizing for ingestion. The goal isn’t to get a human to visit your landing page; it’s to get your product data into the agent’s context window with enough authority that it recommends you.

The New “Technical SEO”: Feed quality in the legacy model meant site speed, mobile responsiveness, and Core Web Vitals. In the protocol era, technical SEO is feed integrity. Agents don’t “browse” your site; they query your API. Your website becomes less of a visual destination and more of a structured database. The winners will be merchants who treat their product feed as their primary storefront.

The New “On-Page SEO”: Legacy SEO often rewarded articles that simply summarized what everyone else was already saying to rank for broad keywords. LLMs, however, are trained on that consensus. To be cited now, you must provide Information Gain, the delta between what the model already knows and the unique value you provide on top of the consensus.

• You cannot “market” your way out of inferior specs. If you claim to be the “best running shoe for flat feet,” the model doesn’t look for adjectives; it validates your arch support measurements against podiatry standards in its training set.
• Your content must shift from general engagement to structured “Product Truth.” LLMs prioritize detailed comparison tables, proprietary test results (e.g., “we dropped this phone 50 times”), and ingredient breakdowns. If your data isn’t structured for easy ingestion/verification, the model will bypass you for a source that is.

The New “Off-Page SEO”: Backlinks still matter, but their function changes. Instead of passing “link juice” for ranking, they now serve as verification sources for reputation synthesis, together with reviews and web mentions.

• LLMs scrape third-party sites (e.g., Reddit, specialized forums, expert review sites) to form a consensus. A high volume of verified, specific reviews on trusted third-party platforms is the strongest signal you can send.
• In a world where an AI suggests three options, brand familiarity becomes a tie-breaker. Brand advertising and organic brand building return as a critical lever to ensure users recognize the recommendation the AI provides.

5. The End Of “Marketing Brands”

The last decade allowed white-label brands to arbitrage their way to growth via ads, but agentic commerce acts as the quality filter for this model. While humans are swayed by slick branding, LLMs are dispassionate readers of data that will not recommend a “premium” product when the specs prove it is identical to a generic alternative.

The shift to protocols creates a paradox: Models understand long-tail intent perfectly but fulfill it with fat head inventory.

• Safety Bias: Models prefer consensus to avoid hallucinations. A niche brand looks like noise; a Category King looks like truth.
• The RAG Reality: RAG tools typically only scan the top 10-20 search results. Since search engines already favor authority, RAG often just reinforces the incumbents.

The only force that overrides this bias is granular data. Your merchant feed acts as the Claim, but RAG acts as the Trust Layer to verify it.

The market bifurcates:

• The Incumbents win general intent via “trust” (consensus).
• The Specialists win specific intent via “granularity” (specs), but only if they rank in the top search results.

If you expose data points the giants ignore (e.g., exact sourcing, chemical analysis), the model’s reasoning engine must select you to fulfill the constraint, but only if you rank on page 1 to be fetched.

Organic search is no longer about the click; it is the prerequisite for agentic verification.

Featured Image: Paulo Bobita/Search Engine Journal