Getting Ready for the World of Agentic Commerce: What Businesses Need to Know

We're moving rapidly into the era of agentic commerce — a shift in which AI-powered digital agents shop, compare, and purchase on behalf of consumers, making the transaction itself nearly invisible. Rather than manually browsing websites, filtering options, and navigating checkouts, customers increasingly delegate these tasks to smart assistants that handle the entire purchase journey. The agentic AI market in retail and e-commerce was valued at $46.7 billion in 2025 and is projected to reach $218.37 billion by 2031, reflecting a nearly 30 percent compound annual growth rate. For retailers and e-commerce brands, the implications reach far beyond technology infrastructure. They fundamentally reshape how customers are reached, how campaigns are measured, and how personalization is delivered at scale.

This is a fast-evolving space, and nobody yet knows exactly how adoption will unfold. Will AI search engines take the lead, enabling direct purchases through features like Perplexity’s “Buy With Pro”? Will marketplaces such as Amazon.com expand into full agentic buying with tools like “Buy for Me”? Will brand websites adapt to support agentic commerce directly, or will entirely new players emerge? One thing is certain: Traditional commerce models will face serious disruption. The industry is already moving in that direction, with both major platforms and niche innovators making strategic moves that signal what’s coming next. Here are some of the biggest trends shaping commerce today.

The Shift From Search Engines to AI Assistants

• Google AI Mode, built on top of Gemini and the Shopping Graph — which contains more than 50 billion product listings — enables shoppers to browse and search entirely through natural language conversations. By tapping into the broader Google data platform, it can surface personalized shopper insights and help users explore products for inspiration. This functionality integrates seamlessly with features like price tracking, virtual try-ons, and even making purchases on the shopper’s behalf.
• Perplexity’s Buy With Pro is another example, offering Pro subscribers the ability to purchase products directly within the Perplexity AI search engine. It supports one-click checkout for eligible products and includes free shipping on those purchases.

Increased Support for Agentic Payments

• Mastercard’s “Agent Pay” focuses on making AI-driven payments simple and secure. Using Agentic Tokens, verified AI agents can complete purchases safely, in line with consumer-defined rules and preferences. This technology can handle one-off payments, recurring expenses, subscriptions, and more, underpinned by strong verification and trust frameworks.
• Visa’s “Intelligent Commerce” combines a suite of integrated APIs with a commercial partner program to help developers connect with consumers through AI. Its tokenization technology, framed as “AI-ready cards,” includes spending limits and controls, delivering security and legitimacy for agent-initiated transactions.

Marketplaces and Commerce Platforms Increasingly Adopt Agentic Commerce

• Amazon has introduced its Buy for Me feature, which allows shoppers to purchase products from other brand sites while remaining within the Amazon experience, with the AI agent completing the transaction in the background.
• SAP’s CX AI Toolkit offers an AI shopping assistant that enhances e-commerce experiences through natural language conversations. Customers can inquire about product details, availability, pricing, and compatibility. They can also receive context-aware responses that streamline the shopping process.
• Salesforce’s “Agentforce” is an AI platform designed to boost productivity and enhance customer experiences. Its agents can autonomously guide shoppers through product selection, manage order inquiries, process returns, and deliver personalized recommendations based on behavior analysis, including alternative suggestions and tailored promotions.

While all these changes are happening, it's imperative for organizations to transform their own commerce platforms and customer interaction models. To be ready for agentic commerce, it's not enough to simply add an AI assistant to your site. This requires a fundamental shift in engagement models across the entire purchase journey, including how marketing campaigns are designed, how personalization is delivered, and how success is measured.

Here are four steps you can take now to get ready for the era of agentic commerce:

1. Build agentic storefronts and interfaces.

User journeys are shifting, and traditional engagement platforms — marketing, commerce, and service — will need parallel agentic pathways. As a growing share of transactions happen through AI assistants, businesses must design new journeys and front-end interfaces tailored to agentic commerce. These should enable voice and image searches, run price and product comparisons, and simplify checkout by guiding customers through each step or completing purchases on their behalf. Marketers must also consider how promotions, loyalty incentives, and personalized offers are surfaced within these interfaces because an agent that cannot read or act on a discount is an agent that will route the purchase elsewhere. Agentic interfaces must accommodate rapid iteration because AI capabilities and consumer expectations are evolving quickly. A modular, API-driven architecture makes it easier to adapt as standards for agent interactions continue to mature.

2. Introduce AI shopping assistants for product search and recommendations.

Advanced filtering by price, brand or rating is now standard, but too many options can overwhelm shoppers. The next step is AI shopping assistants that hold natural language conversations, ask the right questions to uncover customer needs, and combine that input with profile insights to instantly surface best-fit products for every shopper. These assistants should work intelligently to guide customers and simplify discovery for complex product sets. They also represent a powerful personalization channel: By drawing on purchase history, stated preferences, and real-time inventory, AI assistants can deliver recommendations that feel tailored rather than algorithmic. Retailers that invest in product data quality and customer data infrastructure will see outsized returns here, while those with fragmented catalogs or siloed customer profiles will find themselves increasingly invisible to the agents doing the shopping on their customers’ behalf.

3. Enable always-on agentic customer service.

When virtual assistants and AI agents handle purchases, they must also manage post-purchase support, from order tracking to returns. Brands will need to prepare their customer service teams to support agent-to-agent communication, where a customer’s AI assistant interfaces directly with a brand’s systems. For example, a voice assistant could connect with retail apps to act as a single touchpoint for the user’s inquiries across services. This kind of always-on service infrastructure reduces the burden on human support teams while improving resolution speed and customer satisfaction, both of which are increasingly important factors in building long-term retention in an agent-mediated world.

4. Optimize for machine-to-machine (M2M) decision-making.

As AI increasingly drives product discovery and research, content strategies must adapt to serve machines as effectively as they serve human browsers. What's relevant for human browsing often fails to meet the needs of bots. Product data must be structured, concise, and immediately clear, with deep technical detail where relevant. Marketing copy and promotional messaging should also be reviewed through a machine-to-machine lens, since offers and incentives must be structured so that AI agents can identify, evaluate and act on them. Organizations will need to make their existing websites more visible to generative AI search engines and shopping assistants by exposing product and service data via structured, machine-readable formats such as schema.org, as well as creating agent-friendly APIs for ordering, inventory and fulfillment. Campaign measurement frameworks will need to evolve alongside these changes, incorporating new signals, such as agent conversion rates and structured data quality scores, to accurately reflect how performance is being driven in an agent-first environment.

In short, agentic commerce demands a complete transformation of the e-commerce landscape — from retooling the tech stack to refining marketing strategy for an agent-driven world, enabling seamless machine-to-machine interactions, and rethinking how personalization and campaign performance are measured. The shift is already underway, with new tools and solutions emerging at a rapid pace. To keep pace, organizations should treat this as a dedicated strategic initiative, building focused teams around the four pillars of agentic storefronts: AI-powered product search and discovery, agentic customer service, and content optimization for M2M engagement.

Gaurav Mittal is the managing director and global practice leader for customer experience (CX) at Brillio, a full-service digital transformation services and consulting firm.