Agentic Commerce: How AI Agents Are Revolutionizing Online Shopping

Traditional e-commerce is user-driven. Shoppers search, compare, and complete each step manually.

Agentic commerce works differently. AI agents handle most of the process, scanning multiple platforms, filtering by preferences, and suggesting the best options in seconds. They act like personal shoppers who understand intent and context. In the agentic era, you could ask “Find me the best running shoes under $100” and your AI will not only show a tailored shortlist of products but can also place the order within the chat interface once you decide.

The biggest change is convenience. As people grow more comfortable with AI, these agents also take care of checkout. They can apply discounts, confirm delivery, and complete the purchase automatically. Shopping becomes a simple, conversational experience. ChatGPT’s Instant Checkout for Etsy and Shopify is an early example. It lets users go from chat to purchase in just a few clicks. 

As McKinsey observes, it delivers a “fast, frictionless outcome” through highly personalized journeys guided by AI

In short, agentic commerce is not an evolution of e-commerce but a full rethinking. AI becomes the link between consumers and products. It makes buying faster, easier, and more personal. Instead of people adapting to websites, AI adapts to people and does the work for them.

Product discovery has changed significantly with agentic AI. In the past, finding something new meant typing keywords or browsing catalogs. Today, AI assistants understand natural queries and return curated results from across the web. Tools like Google’s AI Search Experience, Bing Chat, and ChatGPT can handle complex prompts such as “best 3-seater couch that stays cool in summer” and instantly suggest matching products. Instead of long lists of links, shoppers receive a few relevant answers.

AI also helps improve conversion rates by reducing friction. With traditional e-commerce, every click or form could lead to cart abandonment. Agentic commerce simplifies the process. Once the user shows clear intent, the AI can complete the purchase immediately. Perplexity’s “Buy with Pro” and ChatGPT’s Stripe integration already allow one-click purchases directly in chat. This makes shopping simpler and faster: browse, decide, and buy in one smooth interaction. Research shows that AI-referred shoppers are about 10% more engaged and closer to a purchase than average visitors, since the AI has already filtered options that fit their needs.

Visibility now depends on whether an AI includes your product in its response. Some items are recommended to users who have never visited your site, while others remain invisible. Marketers call this answer share (of voice), which represents the percentage of AI answers featuring your brand. In an environment where one single result can dominate attention, earning that spot is critical. Optimizing for AI-driven results and “answer engines” has become as important as SEO.

The rise of agentic AI brings deep strategic consequences. One of the main challenges for brands is the risk of disintermediation, as AI agents may replace direct relationships with customers. If a shopper relies entirely on an AI assistant, they may never visit a retailer’s website or app. The agent becomes the gatekeeper, while retailers are reduced to simple fulfillment services. This threatens years of investment in branding and loyalty, since AI often prioritizes price, reviews, and delivery speed over emotional connection.

As Boston Consulting Group noted, “a retailer’s most valuable customer might not be a human.” It could be an AI agent purchasing on behalf of one.

Competition for visibility on AI platforms is now the new marketing frontier. Brands must ensure their products appear favorably within systems like ChatGPT, Google Gemini, or Amazon Rufus.This involves optimizing product data for AI, a practice known as Generative Engine Optimization, and exploring new channels such as sponsored placements inside AI assistants. Companies will need to dedicate resources to both earned and paid visibility in these emerging ecosystems, just as they once did for search and social media.

Another key shift concerns the customer experience. If most buyers come through AI agents, retailers must learn how to retain them. Some companies are developing their own AI assistants to maintain a direct link with shoppers, while others integrate with third-party platforms but focus on exclusive benefits and loyalty programs. A global survey shows that 96 percent of major retailers are exploring AI agents, and 68 percent believe these systems will handle most customer interactions within five years. Nearly two-thirds say that companies delaying adoption for more than two years risk falling behind.

From an operational view, trust and data accuracy become critical. Retailers must keep product feeds, pricing, and inventory information perfectly up to date to avoid errors that could make AI recommend unavailable products. In the worst case, AI may not reference or display products at all if it detects missing or out-of-stock inventory. They also need to convey brand quality in a world where AI reads facts but cannot sense emotion. Strong product content and genuine reviews help fill this gap, since AI models rely on such signals to justify recommendations (hypotenuse.ai).

In short, brands that invest now in reliable data, structured content, and visibility across AI platforms will be best positioned to thrive in the agentic commerce era.

How can brands practically prepare their product catalogs and feeds for AI-driven discovery? It starts with rethinking your product data as something that feeds AI models (sometimes literally, via APIs or merchant feeds). Here are actionable steps to get AI-ready product data:

1. Enrich and structure your data: AI agents rely on structured attributes, clear details like name, material, size, price, and specs. Standardize and complete every field, using simple, descriptive language (“Material: 100% cotton”). Clean, consistent data helps AI identify and recommend your products more confidently.
2. Add schema and machine-readable formats: use schema.org markup (like Product, Offer, Review, and AggregateRating) so AIs and search engines can easily read your product pages. Keep feeds (Google Shopping, Amazon, etc.) updated, and provide direct data access to AI platforms where possible. Think of this as creating an LLM feed, a structured product data stream designed for large language models.
3. Write for real customer intent: AI matches content to human questions, not just keywords. Mirror real use cases: say “ideal for small apartments” or “breathable cotton for summer.” Align product descriptions with natural queries to increase your odds of being the AI’s chosen answer.
4. Leverage reviews and mentions: encourage customers to leave detailed, authentic reviews; these help both people and AI understand how your products perform. Mentions across forums, blogs, and social channels also strengthen your answer authority, signaling credibility to generative systems.
5. Keep your data fresh and consistent: AI favors accuracy and up-to-date information. Synchronize prices, stock, and specs across every channel. Inconsistent data erodes trust, both for shoppers and algorithms. A centralized product information setup ensures your feeds are always aligned and reliable.

By taking these steps, brands can significantly improve their chances of being featured in AI-driven discovery. In essence, you want to speak the AI’s language, which is structured data, factual precision, and user-centric context. The reward is not just staying visible, but potentially owning the recommendation in your category. And as agentic commerce grows, being the AI’s top recommendation could make a bigger difference to your sales than a front-page Google ranking ever did.

Navigating this transformation can feel complex, but solutions already exist to make it easier. Lengow helps brands manage, enrich, and distribute their product data across every channel, including emerging AI-driven platforms.

Agentic commerce is moving quickly from concept to reality. AI-driven product discovery and shopping will only expand as we enter 2025 and beyond. The brands that adapt early by refining their product data, content, and visibility strategies will gain the largest share of this new, answer-driven market.

Success now depends on being present where decisions happen. Whether through a search engine AI, a voice assistant, or an autonomous shopping agent, brands must ensure their products are discoverable, relevant, and structured so they appear in these AI-generated results.

Those who prepare today will shape tomorrow’s customer experience. The frontier of commerce is no longer about search rankings but about being the trusted answer in a conversation led by AI.