Brands vs. Bots: Azoma’s New Protocol Aims to Tame AI Commerce

LONDON & TORONTO – March 12, 2026 – A new front has opened in the battle for the future of online shopping. As artificial intelligence agents increasingly act as personal shoppers for consumers, brands are scrambling to avoid being misrepresented or lost in the digital noise. Today, Azoma, a firm specializing in this new landscape, launched its Agentic Merchant Protocol (AMP), a system designed to give power back to retailers and brands in the age of AI.

The move is backed by an impressive roster of consumer goods giants, including Mars, L'Oréal, Unilever, Beiersdorf, and Reckitt. Their rapid adoption signals a deep-seated anxiety within the industry about losing control over brand narratives that have been meticulously built over decades. The stakes are immense. This past Black Friday, AI-driven agents were estimated to have facilitated $14.2 billion in global sales, a figure that underscores a fundamental shift in consumer behavior. Shoppers are moving away from browsing static websites and are instead delegating their purchasing decisions to AI assistants, a trend that market researchers project could command a $3 to $5 trillion slice of global commerce by 2030.

The 'Black Box' Dilemma

The core of the problem for brands is the "black box" nature of current AI systems. While major tech players like OpenAI and Google have rolled out their own protocols—the Agentic Commerce Protocol (ACP) and Universal Commerce Protocol (UCP), respectively—these are primarily engineered for developer integration and streamlining checkout. They do not, however, guarantee how a brand is represented.

An AI agent, tasked with finding "the best sustainable skincare product," doesn't just consult a brand's official website. It synthesizes information from across the entire internet, including reviews, articles, and forum discussions, sources entirely outside a brand's control. This can lead to product information being presented out of context, or worse, based on outdated or inaccurate data.

"Brand representation cannot be left to chance," Azoma stated in its announcement. This sentiment is echoed by industry analysts and consumer data. A recent Gartner survey found that 53% of consumers lack trust in AI-enabled search results, fearing bias and a lack of transparency. For brands, this erosion of trust is a critical threat.

"For decades, marketplaces like Amazon and Walmart acted as gatekeepers by controlling product detail pages, rankings, and distribution," said Azoma CEO Max Sinclair in the announcement. "In an agentic world, those fixed pages no longer exist."

A New Layer of Control

Azoma's AMP is positioned not as a competitor to Google's and OpenAI's protocols, but as an essential orchestration layer sitting above them. The company, founded in 2022 by Sinclair, a former Amazon executive, takes a merchant-first approach, focusing on brand control rather than the consumer-facing experience.

The protocol allows a brand to create a canonical, machine-native product catalogue. This isn't just a list of SKUs and prices; it's a rich dataset enriched with brand guidelines, compliance guardrails for regulated industries, target personas, and competitive context. AMP then programmatically distributes this verified "brand intelligence" across the open web and various agent ecosystems. The goal is to ensure that when an AI agent is reasoning about a purchase, it encounters and prioritizes the brand's intended messaging and accurate product data.

"AMP radically improves the AI visibility of product information, guaranteeing agents will weigh it correctly," Sinclair explained. "For enterprise brands, this is an existential change. They can now retain control over channels, messaging, accuracy and compliance."

The system offers an agent-agnostic interface, designed to reduce a brand's dependence on any single AI platform and its specific roadmap or incentives. This is a crucial feature for enterprises wary of being locked into a single tech giant's ecosystem.

Heavy Hitters Signal a Market Shift

The immediate buy-in from companies like L'Oréal, Unilever, and Mars is perhaps the most telling aspect of the launch. These are not nimble startups but global behemoths with immense brand equity to protect. Their swift move to adopt AMP is a powerful indicator of the urgency they feel.

"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," Sinclair noted. "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."

Azoma, formerly known as Ecomtent, already has a track record with some of these giants. A case study with Mars Wrigley detailed how using Azoma's optimization tools led to an average 8% increase in search visibility for key brands, translating into tens of millions in additional revenue. With $7.97 million in funding from investors including Ignite Ventures and eBay Ventures, and having reached profitability in 2025, Azoma has established a credible foothold as a key player in this emerging field.

The Road Ahead

Despite the promise, the path to widespread adoption is not without its challenges. Implementing a system like AMP requires significant technical investment. Enterprises must ensure their product data is clean, consistent, and structured for machine consumption, a complex task that can take months and dedicated engineering resources. This raises questions about the accessibility of such solutions for the small and mid-market merchants who form the backbone of e-commerce.

Furthermore, the entire agentic commerce ecosystem is grappling with complex issues of governance, fraud, and liability. As AI agents gain the authority to transact on a user's behalf, new systems for verification, consent, and risk management must be developed. Traditional fraud detection, often based on human behavioral signals, becomes less effective in a world of automated purchases.

The launch of AMP is a clear sign that the industry is beginning to build the infrastructure needed to address these challenges. It represents a move to establish a new system of record for commerce, one where brands can define how they are understood by machines, ensuring consistency and compliance in a world that is becoming less about browsing and more about asking. The success of this protocol could determine whether brands remain the authors of their own stories or become mere subjects in a narrative written by algorithms.