Jan 6, 2026
How Eleway Fixes Blind Spots in Agentic Commerce for Shopify
The promise of agentic commerce
A new era of online retail is emerging where AI-powered agents play a central role in how people discover and buy products. This shift—often called agentic commerce—describes shopping experiences where intelligent agents research, recommend, and even complete purchases on a shopper’s behalf [1].
Instead of manually browsing stores and search results, customers increasingly rely on AI to compare products, answer detailed questions, and complete purchases through conversation [1].
Analysts estimate that by 2030, up to $1 trillion in U.S. retail sales and $3–5 trillion globally could be influenced or executed by AI agents [1]. This isn’t a distant future—it’s already happening.
A growing number of shoppers now use tools like ChatGPT for product research, with surveys showing that around one in four users who try AI search prefer it over traditional search engines [1]. The shift is already visible in real metrics: platforms like TripAdvisor have reported significant traffic declines as planning moves into AI conversations, while retailers such as Home Depot and Wayfair report a growing share of referrals coming directly from language models [1].
What makes agentic commerce different?
Agentic commerce goes beyond chatbots.
A conversational agent doesn’t just answer questions—it acts.
Examples include:
recommending products based on intent, not keywords
checking real-time availability, variants, and pricing
generating discount codes
tracking orders or handling returns
completing a purchase within a single conversational flow
OpenAI has already introduced Instant Checkout in ChatGPT, allowing users to buy from connected Shopify and Etsy stores without leaving the chat interface [3]. At the same time, OpenAI open-sourced the Agentic Commerce Protocol (ACP) to enable secure transactions between AI agents, merchants, and shoppers [3].
This signals a fundamental shift: AI agents are becoming a new front door to e-commerce stores.
The problem: where AI agents fail today
Despite the excitement, many AI shopping experiences fail in practice. Early implementations often break not because the models are weak—but because the data they rely on is incomplete, outdated, or poorly structured [5].
Common failure points include:
outdated product or pricing data
missing inventory or variant information
hallucinated policies or shipping details
generic or off-brand responses
poor handling of edge-case questions
Studies of deployed e-commerce chatbots show that incomplete integration with product databases can lead agents to recommend discontinued or irrelevant items, quickly eroding customer trust [5].
There is also a brand risk. AI agents generate language autonomously, which means they may phrase things incorrectly or even invent policy details if not properly constrained [5]. Some large retailers have been hesitant to embrace third-party shopping agents for this reason, fearing loss of control over the customer relationship [2].
For smaller brands and Shopify merchants, however, AI-driven discovery represents a major opportunity—if agents can represent the store accurately.
Trust is critical. If a shopper asks, “Does this laptop support 240V power?” or “Do you have this dress in size M, color red?”, the answer must be correct. When agents are rushed to production without sufficient testing and data access, the result is broken flows and lost sales [5].
How AI agents understand your store
To answer questions correctly and take action, AI agents need structured access to store data. Today, this usually happens in three ways.
1. Direct integrations (best-case scenario)
The most reliable approach is real-time integration with a store’s backend via official APIs.
Shopify-native tools such as conversational sales agents can:
pull live product catalogs and inventory
check order status
generate discount codes on demand
Examples like Zipchat demonstrate how tightly integrated agents can function as a true 24/7 sales representative, rather than a simple FAQ bot [4].
Voice-based agents such as Callsy extend this idea further by connecting directly to Shopify data and calling customers who abandoned checkout with personalized follow-ups [6].
2. Crawling and scraping (fragile)
Some AI tools attempt to understand stores by crawling and scraping public webpages. While this can work for basic discovery, it is brittle and often outdated.
As discussed in industry forums, Shopify has increasingly discouraged unauthenticated scraping in favor of official APIs and structured data feeds [8].
A useful mental model:
Websites are built for humans. Agents don’t browse — they query [8].
3. Knowledge bases and fine-tuning (high maintenance)
Another approach is uploading FAQs or product data into an AI knowledge base or fine-tuning a model on store content. This can work for static information, but it requires constant manual updates as catalogs and pricing change [5].
Where the industry is heading
The emerging best practice is real-time, structured, machine-readable data.
This includes:
official APIs and feeds
Schema.org and JSON-LD
conventions like llms.txt to guide AI crawlers
Shopify itself has signaled this direction by encouraging structured access to store data rather than scraping [8]. Ensuring your store is AI-readable is quickly becoming as important as traditional SEO was in the Google era [1][2].
Real-world agentic commerce examples
Conversational sales agents like Zipchat actively guide shoppers, handle objections, and close sales, with merchants reporting higher conversion rates and improved customer satisfaction [4].
Voice agents like Callsy use AI-generated speech to recover abandoned carts through personalized phone calls, demonstrating how agentic commerce can extend beyond chat interfaces into telephony [6].
Together, these tools show that AI agents are increasingly handling discovery, decision-making, checkout, and post-purchase engagement across channels [3][4][6].
How Eleway prepares Shopify stores for AI discovery
Eleway is built to help Shopify merchants ensure that AI agents can accurately understand and represent their store.
It works by indexing store data directly inside Shopify, rather than relying on scraping or static uploads [7].
Real-time store indexing
Eleway indexes products, variants, collections, blogs, and informational pages in real time, ensuring AI systems always access up-to-date information [7].
llms.txt: a map for AI agents
Inspired by robots.txt, llms.txt helps AI crawlers understand what a store sells, which pages matter most, and how the catalog is structured [7].
Structured data for machines
Eleway injects structured metadata (JSON-LD) into the storefront, allowing AI systems and search engines to parse pricing, availability, and product details reliably [7].
As the Eleway team puts it:
LLMs don’t rank keywords — they cite structured content [7].
Final thoughts
Agentic commerce is no longer theoretical. AI agents are already influencing how shoppers discover, evaluate, and purchase products [1][3].
Merchants who invest early in structured data, real-time integrations, and AI-readable infrastructure can turn this shift into a powerful growth lever. Those who ignore it risk becoming invisible—not because their products lack quality, but because AI systems can’t understand them [1][2].
Sources
McKinsey & Company — The agentic commerce opportunity: How AI agents are ushering in a new era for consumers and merchants
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-agentic-commerce-opportunity-how-ai-agents-are-ushering-in-a-new-era-for-consumers-and-merchantsRetail TouchPoints — Agentic Commerce: The Inevitable, but Narrow, Future of AI Shopping
https://www.retailtouchpoints.com/features/executive-viewpoints/agentic-commerce-the-inevitable-but-narrow-future-of-ai-shoppingOpenAI — Buy it in ChatGPT: Instant Checkout and the Agentic Commerce Protocol
https://openai.com/index/buy-it-in-chatgpt/Zipchat AI — AI chatbot for eCommerce: Sales & Support (Shopify App)
https://www.zipchat.ai/Titan Tech — AI Chatbots in eCommerce: 5 Failures and How AI Testing Fixes Them
https://titancorpvn.com/insight/technology-insights/ai-chatbots-in-e-commerce-5-failures-and-how-ai-testing-fixes-themF6S — Callsy AI: Automated voice agent for cart recovery
https://www.f6s.com/companies/shopify-app/moEleway — Enable AI to discover your brand
https://www.eleway.app/Reddit (r/ShopifyeCommerce) — Shopify’s stance on agentic commerce, scraping, and APIs
https://www.reddit.com/r/ShopifyeCommerce/comments/1mj3i6l/