Brands Are Losing Control of Their Product Catalogs — And L'Oréal, Unilever, Mars Just Fixed It

You spent years building your website. SEO. Product pages. Brand equity. The whole machine designed to put you in front of buyers.

AI agents don't care.

When someone asks ChatGPT or Perplexity what ski helmet to buy, the agent doesn't visit your site. It pulls product data from Reddit. From outdated affiliate blogs. From wherever it finds something that looks relevant.

Your brand gets represented by whatever the agent scrapes together. No control. No accuracy. No brand governance.

That just changed.

On March 12, Azoma launched the Agentic Merchant Protocol (AMP) — the first enterprise infrastructure that gives brands a single system to define, distribute, and govern how their product catalogs are understood by AI agents across every commerce surface.

Early adopters: L'Oréal, Unilever, Mars, Beiersdorf, and Reckitt.

When five of the world's largest CPG brands move this fast on the same protocol, the rest of the market is already behind.

The Problem: Your Product Pages Don't Exist in Agentic Commerce

Traditional e-commerce was built for humans browsing product pages.

Agentic commerce removes the page entirely.

When an AI shopping agent executes a purchase on behalf of a user — whether through ChatGPT, Amazon Rufus, Google Gemini, or Walmart Sparky — it doesn't load your product detail page. It reasons over product data pulled from the open web, synthesizes information from multiple sources, and makes a recommendation.

If your product data is incomplete, inconsistent, or buried in unstructured content, the agent moves on.

McKinsey projects that by 2030, up to $1 trillion in U.S. B2C retail revenue could be orchestrated through agentic commerce — shopping where AI completes tasks on behalf of consumers. Globally, the opportunity reaches $3 trillion to $5 trillion.

Morgan Stanley estimates that nearly half of online shoppers will use AI shopping agents by 2030, accounting for approximately 25% of their spending.

The shift is already happening. A 2026 study found that 73% of consumers are using AI in their shopping journey — 45% for product ideas, 37% for summarizing reviews, 32% for price comparison. While only 13% report completing a purchase after an AI referral, 70% are at least somewhat comfortable with an AI agent making purchases on their behalf.

Most brands aren't ready. Their product catalogs were structured for search engines and human browsers, not machine reasoning.

The Infrastructure Gap: ACP and UCP Aren't Enough

Over the past year, the AI and payments industry rolled out the plumbing for agentic commerce:

OpenAI's Agentic Commerce Protocol (ACP) — connects ChatGPT to merchant systems for agent-led checkout
Google's Universal Commerce Protocol (UCP) — allows agents to transact across platforms with a common language
Agent Payments Protocol (AP2) — enables secure payment authorization for autonomous agents
Model Context Protocol (MCP) — standardizes how agents retrieve context across systems

These are agent-side protocols. They solve how agents connect to platforms, execute payments, and access context.

They don't solve the merchant-side problem: How do brands ensure the product intelligence agents consume is accurate, compliant, and strategically governed?

ACP and UCP connect agents to product data. They don't control what that data says or where it comes from.

That's the gap AMP fills.

What AMP Actually Does

The Agentic Merchant Protocol is merchant-side infrastructure for agentic commerce.

It sits above ACP, UCP, and platform-specific integrations. Brands feed product intelligence into AMP once — catalog data, brand guidelines, compliance rules, regulatory requirements, competitive positioning, persona-level targeting — and AMP handles distribution everywhere.

Four core capabilities:

1. Canonical Product Intelligence

Brands create a machine-native source of truth for their product catalog, enriched with:

Brand messaging guidelines
Regulatory compliance guardrails (FDA, DSHEA, etc.)
Target personas and use-case prioritization
Competitive context and positioning
Availability rules and regional constraints

This becomes the single authoritative dataset that all AI agents reference.

2. Open Web Distribution

AMP programmatically syndicates structured product data across the open web — not just to AI platforms via ACP/UCP, but to the third-party sources AI agents actually cite when they reason.

Reddit threads. Quora answers. YouTube product reviews. Wikipedia entries. Affiliate comparison sites.

If an agent pulls product data from these surfaces, AMP ensures the information is accurate, up-to-date, and brand-approved.

3. Agent-Agnostic Integration

AMP is platform-neutral. It works across:

ChatGPT (via OpenAI's ACP)
Google Shopping and Gemini (via UCP)
Amazon Rufus
Walmart Sparky
Perplexity
Any future agent platform

Brands avoid lock-in. One protocol, every surface.

4. Enterprise Brand Governance

AMP includes RegGuard™ Compliance — an automated engine that audits all generated content against brand guidelines and regulatory standards before distribution.

This is critical for CPG, pharmaceuticals, supplements, financial services, and any regulated category where inaccurate agent-generated claims create legal exposure.

AMP also provides citation tracking — brands can see exactly which sources (Reddit, Quora, Wikipedia, YouTube) AI agents are citing when they recommend a product. This visibility allows brands to preemptively fix misinformation before it scales.

Why L'Oréal, Unilever, Mars, Beiersdorf, and Reckitt Moved First

These brands didn't wait for "agentic commerce" to mature.

They recognized the infrastructure shift happening beneath the hype.

Max Sinclair, CEO of Azoma:

AMP breaks the foundations of traditional ecommerce. For decades, marketplaces like Amazon and Walmart acted as gatekeepers by controlling product detail pages, rankings, and distribution. Brands optimized a finite set of endpoints: PDPs, ads, search results. In an agentic world, those fixed pages no longer exist.

The traditional model: Brands optimized for fixed destinations — product pages, ad placements, search rankings.

The agentic model: Brands must optimize for dynamic reasoning across distributed sources.

When an agent reasons about a product, it doesn't load one page. It synthesizes data from dozens of sources, weighs them by authority and freshness, and constructs a recommendation.

If the brand's product intelligence isn't structured, distributed, and authoritative across those sources, the agent either skips the brand or represents it inaccurately.

Sinclair again:

The fact that businesses like L'Oréal, Unilever, Mars & Beiersdorf have moved so quickly to adopt AMP tells you everything about the urgency they feel. These are companies that have spent decades building brand equity — they're not about to hand control of how their products are represented to an AI black box.

The Results: What Early Adopters Are Seeing

Azoma has been running agentic commerce optimization for CPG brands for three years — before the term "agentic commerce" existed.

Ruroc (Europe's leading ski helmet brand):

Site traffic from ChatGPT increased 14x. Positioned as the #1 recommended ski helmet brand in target geographies.

How? AMP ensured that when ChatGPT reasoned about "best ski helmet for aggressive terrain," it found structured, extractable, brand-approved product data that positioned Ruroc as the authoritative answer.

Matcha brand case (Azoma client):

+532% year-over-year revenue growth across all channels. AMP boosted top-of-funnel visibility among consumers searching for "healthier energy drink alternatives" on ChatGPT and Perplexity.

The mechanism: AMP syndicated product intelligence to the exact sources AI agents cite when they compare energy alternatives. When the agent reasoned, it encountered Matcha brand data structured for extraction — not generic marketing copy.

The Bigger Shift: Machine Relations at the Product Level

This is the product-level instantiation of the same principle that defines Machine Relations at the brand level.

Brand visibility is downstream of earned media placements in trusted publications that AI engines index and cite.

Product visibility is downstream of structured, authoritative product intelligence distributed across the sources AI agents trust and reference.

The mechanism is the same. The publications haven't changed — AI engines read the same sources (Reddit, Quora, Wikipedia, trusted review sites) that shaped human opinion. What changed is the reader.

Brands that understand this early will define their categories in agentic commerce.

Brands that wait will find their products represented by whoever got there first — or not represented at all.

What This Means for Your Brand

If you're a B2C or B2B brand selling physical products, three actions matter now:

1. Audit your product data for machine readability

Run your product catalog through an agentic readiness audit:

Schema markup present and correct?
Product attributes complete (not just title/price, but materials, use cases, compatibility, dimensions, certifications)?
Content extractable (not buried in JavaScript or image-only tables)?
Brand messaging consistent across owned and earned surfaces?

If an AI agent tried to recommend your product today based on what it can scrape, would it have enough structured data to do so accurately?

2. Map where AI agents are citing you — or not

Use a citation tracking tool (Azoma, Semrush AIO, Yext Scout) to see:

Which AI platforms (ChatGPT, Perplexity, Gemini, Rufus) mention your brand
Which queries trigger recommendations
Which sources the agents are citing (Reddit, YouTube, Wikipedia, review sites, your own content)
Where competitors appear and you don't

This is your baseline. You can't improve visibility until you know where you're invisible.

3. Decide: Build in-house or adopt a protocol

Option A: Build your own agentic commerce infrastructure. Integrate directly with ACP, UCP, and every future agent platform. Manually distribute product data to citation sources. Build compliance engines for every regulated market.

Option B: Adopt AMP or an equivalent merchant-side protocol. One integration, universal distribution, automated compliance.

Most brands will choose Option B. The exception: Platforms and marketplaces large enough to define their own standards (Amazon, Walmart, Shopify).

For everyone else, the choice is speed and interoperability via a shared protocol, or slow, fragmented in-house builds that lag the market.

Machine Relations Is the Architecture

PR practitioners are admitting that AI citations are the new success metric.

GEO researchers are proving that earned media is what AI engines actually cite.

Neither side has the architecture that connects them.

Machine Relations is that architecture.

At the brand level, it's earned media placements in trusted publications that AI engines index. That's the mechanism AuthorityTech pioneered eight years ago, formalized now as Machine Relations.

At the product level, it's structured product intelligence distributed across the sources AI agents trust — the mechanism AMP now automates for CPG, retail, and ecommerce brands.

Both solve the same problem: How do you ensure AI systems cite you as the authoritative source when someone asks about your category?

Brand visibility and product visibility are converging into one infrastructure question.

Brands that treat them separately will lose on both fronts.

The Bottom Line

You can't optimize for agentic commerce the way you optimized for Google.

AI agents don't rank pages. They synthesize distributed intelligence and construct answers.

If your product data isn't structured, authoritative, and distributed across the sources agents trust, you're invisible.

Azoma's Agentic Merchant Protocol gives brands the infrastructure to solve this.

L'Oréal, Unilever, Mars, Beiersdorf, and Reckitt already moved.

The question now is how long the rest of the market waits.

Want to see how your brand shows up in AI answers today?

Run a free AI Visibility Audit at app.authoritytech.io/visibility-audit — see where you're cited, where competitors appear, and which AI platforms are recommending you (or not).