Agentic commerce, where AI agents autonomously discover, compare, and purchase products on behalf of consumers, is emerging as one of the defining commerce trends of 2026. Amazon, Google, OpenAI, and Meta have all launched AI shopping tools in the past year, while retailers like Shopify and Walmart navigate how much access to grant external agents. Google's Universal Commerce Protocol, backed by more than 20 global partners, signals an industry-wide push toward standardized AI shopping infrastructure. This FAQ covers what agentic commerce is, who the key players are, and what brands and advertisers should do about it.

What is agentic commerce?

Agentic commerce is online shopping in which AI agents autonomously discover, compare, and purchase products on behalf of consumers. Unlike chatbots that respond to individual queries, agentic systems combine memory (recalling user preferences), tool access (connecting to retailer databases and payments), and multi-step reasoning to handle transactions end-to-end.

The concept spans a range of capabilities, including Amazon's Rufus, OpenAI’s ChatGPT, and Perplexity’s agentic shopping tool.

Which companies and platforms are leading agentic commerce?

Five major players are shaping the agentic commerce landscape:

• Amazon. Rufus includes "Auto Buy" capabilities that authorize purchases when items hit target prices.
• OpenAI. ChatGPT enables consumers to complete purchases via third-party apps that plug into the chatbot,
• Google. AI Mode uses the Google Shopping Graph's 50 billion-plus product listings to enable conversational product discovery.
• Perplexity. Its agentic shopping tool spots purchase intent and personalizes recommendations using search history.
• Meta. Business AI delivers personalized recommendations and direct purchasing across Facebook, Instagram, and Shopify-powered sites.

How large is the agentic commerce market in 2026?

AI platforms will account for 1.5% of total US retail ecommerce sales in 2026, or $20.57 billion in spending, nearly quadruple 2025 figures, according to a December 2025 EMARKETER forecast. McKinsey projects the global agentic commerce opportunity at $3 trillion to $5 trillion by 2030, with up to $1 trillion in US B2C retail alone.

The gap between current market share and long-term projections reflects early-stage but accelerating adoption. Retailers that deployed AI capabilities saw 14.2% sales growth between 2023 and 2024, compared to 6.9% for those without, according to Capital One Shopping research. This performance gap is driving investment across the sector.

How are consumers using AI agents to shop?

Consumer adoption of AI shopping tools accelerated in 2025. During Cyber Week, 20% of global orders were influenced by AI and agents, according to Salesforce. AI chatbot traffic to US retail sites increased 670% year-over-year during the holiday season, per Adobe data.

Many shoppers use retailers' embedded AI features (Amazon's Rufus, Walmart's Sparky) rather than standalone AI platforms like ChatGPT or Perplexity, making it challenging to measure how effectively these tools drive sales. Nearly half of consumers (48%) planned to or had used AI for holiday shopping, per Adobe. Among Gen Z and millennials, 58% say they trust an AI agent to compare prices and recommend options, according to SAP.

What is generative engine optimization and how does it relate to agentic commerce?

The Universal Commerce Protocol (UCP) is an open standard developed by Google that enables AI agents to complete shopping transactions across retailers and payment providers. UCP establishes a common framework for product discovery, purchasing, and post-purchase support, allowing AI shopping agents to interact with merchant systems without custom integrations.

Google co-developed UCP with Shopify, Etsy, Wayfair, and Target, and more than 20 additional companies across retail and payments have endorsed it, per Search Engine Land. UCP integrates with the Agent Payments Protocol (AP2) for secure transactions and is compatible with Agent2Agent (A2A) and Model Context Protocol (MCP) standards. Without shared protocols, agentic commerce remains fragmented, with each AI platform requiring separate retailer integrations.

Why are some retailers resisting external AI shopping agents?

External AI agents that control product discovery and purchasing workflows threaten retailer ownership of the customer relationship and advertising revenue. Brands relying on sponsored placements, display ads, and keyword-based ads lose visibility and attribution data when AI agents bypass traditional search interfaces.

Several major retailers have taken defensive action:

• Shopify and Amazon restrict external AI agent activity within their ecosystems.
• Walmart added guidelines preventing agents from taking users to checkout pages or placing orders, per The Information.

This tension creates a paradox: retailers benefit from AI-driven demand but risk losing control of the transaction and the data that powers their retail media businesses.

How does agentic commerce affect retail media and advertising?

Agentic commerce poses a direct challenge to the retail media market. US advertisers will spend $71.98 billion on retail media in 2026, up 18.7% from 2025, according to a March 2026 EMARKETER forecast. AI shopping agents that bypass traditional search and browse behavior reduce the value of sponsored product placements, display ads, and keyword advertising.

The opportunity runs both ways. Shoppers directed to retail sites from AI platforms are 30 times more likely to make a purchase, per Adobe data cited by EMARKETER. Retailers are already testing AI ad formats: Walmart has explored ads in its Sparky chatbot, Amazon offers sponsored prompts in Rufus, and Google is testing ads in AI Mode.

Generative engine optimization (GEO) is the practice of structuring product data and brand content so AI agents can discover, understand, and recommend it. As agentic commerce shifts product discovery from search engines to AI platforms, GEO has become a strategic priority.

Key GEO tactics include:

• Structured product metadata. Clear specs, pricing, and availability data that AI agents can parse and compare.
• Indexable review sections. Customer reviews in formats AI systems can extract and cite.
• Full-sentence product descriptions. Complete descriptions that AI chatbots can include in outputs.

Some brands have scaled content production from 3 to 4 pieces per month to over 100, Brian Stempeck, CEO of GEO optimization platform Evertune.ai, told Reuters. Brooklinen pays influencers to promote products on social channels, knowing that text and audio transcripts will be scraped by AI crawlers, according to company COO Rachel Levy.

How should brands and advertisers prepare for agentic commerce in 2026?

Four priorities position brands for the agentic commerce shift:

• Invest in GEO. Ensure product data is structured for AI extraction: clear metadata, full-sentence descriptions, and indexable reviews. This is the foundation of visibility in AI-driven discovery.
• Experiment with AI ad formats. Test sponsored prompts and AI search ads on Amazon, Walmart, and Google to understand performance and consumer response.
• Monitor protocol adoption. Google's Universal Commerce Protocol and related standards (A2A, AP2) will determine how AI agents access merchant catalogs. Early participation gives brands infrastructure advantages.
• Strengthen first-party data. As external AI agents reduce reliance on traditional tracking, first-party purchase and behavioral data becomes more valuable for targeting and attribution.

Only 11% of retailers say they are ready to scale AI across their businesses, according to Amperity's 2025 State of AI in Retail study. Brands that build agentic-ready infrastructure now gain a competitive advantage as adoption accelerates.

We prepared this article with the assistance of generative AI tools and stand behind its accuracy, quality, and originality.

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