How to make your e-commerce product visible to AI agents? Use this new system trusted by L’Oréal, Unilever, Mars & Beiersdorf

2026-03-09 · Source: VentureBeat · Field: Retail & Consumer Goods — Retail Technology & Operations, Consumer Products & Manufacturing, Retail Analytics & Intelligence · Depth: Intermediate, medium

Summary

Azoma, a four-year-old agentic AI e-commerce startup, has launched the Agentic Merchant Protocol (AMP) to help high-volume retailers make their products visible to AI agents. This framework centralizes product intelligence, including SKUs, materials, legal guardrails, and brand books, into a machine-native format for distribution across various online marketplaces and AI-optimized pages. AMP aims to counter the "black box" effect of current AI integration, where agents synthesize data from unverified sources, by ensuring brand-consistent messaging. The protocol is designed for physical goods manufacturers, particularly in CPG and FMCG sectors, and has already been adopted by major brands like L'Oréal, Unilever, Mars, Beiersdorf, and Reckitt. Morgan Stanley projects agentic commerce could reach $190 billion to $385 billion in the U.S. by 2030.

Key takeaway

For e-commerce executives and product managers navigating the shift to agentic commerce, Azoma's AMP offers a critical solution to maintain brand integrity and visibility. Your teams should evaluate AMP to centralize product data, ensure consistent messaging across AI agents, and gain performance visibility, thereby securing market share in an AI-first economy and avoiding the loss of brand control to unverified data sources.

Key insights

The Agentic Merchant Protocol (AMP) centralizes product data for AI agents, ensuring brand consistency across diverse online surfaces.

Principles

• AI agents will drive significant e-commerce spend.
• Brand control is critical in agentic commerce.
• Fixed product pages are becoming obsolete.

Method

Centralize product intelligence into a machine-native format, distribute it programmatically across the open web, and monitor agent performance and compliance to optimize AI-driven product recommendations.

In practice

• Implement canonical machine-native product catalogs.
• Utilize RegGuard™ Compliance for automated content audits.
• Track citation sources for AI agent recommendations.

Topics

• Agentic Commerce
• E-commerce AI
• Product Information Management
• Brand Governance
• AI Agents

Best for: Executive, Product Manager, Investor, AI Product Manager, CTO, Marketing Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by VentureBeat.