AI is the quiet engine reshaping retail innovation, and Agentic Commerce is rapidly becoming the headline. As online retailers shift focus from experimenting with assistive AI to the idea that autonomous agents will be the customer, new capabilities based on a Universal Commerce Protocol (UCP) will create a new standard of personalization based on data quality, and not ad spend.
There’s no doubt that Agentic Commerce is a provocative idea: autonomous software agents that can identify, compare, decide and purchase on behalf of consumers – shifting the retail paradigm from keyword matching to understanding a consumer’s true intent.
Automated Buying Meets "Garbage In, Garbage Out"
Unlike traditional search engines that return a list of links based on keywords, such as “floral dress,” AI agents analyze natural language to understand context. For example, an agent can process a request for "a dress to wear to a concert next weekend in Nashville". By filtering on contextual needs – such as weather suitability or event appropriateness – agents avoid recommending products that match the keyword but miss the actual intent.
But AI can’t reason. When a user asks for "organic pajamas under $100," the agent builds a candidate set based on specific attributes. If the attribute an agent is looking for is missing from the data, the product isn’t just ranked lower; it’s excluded from the agent's candidate set entirely. This limits the agent's ability to find the actual best match, potentially forcing it to select a suboptimal substitute that is more likely to create a systemic returns loop, eroding the very margins Agentic Commerce aims to protect.
Optimizing for Agentic Accuracy
It’s still early days for AI shopping agents, but the retailers who are already winning by preventing returns in the first place are those who possess the cleanest data and the deepest understanding of SKU-level product performance.
This is true because as we’ve seen AI acts on attributes, not intuition. For example, a "red" jersey that looks orange in photos isn't just a customer service issue anymore; it is a data failure that can mislead thousands of buyers – and buying agents – simultaneously. If a product description is vague, or if sizing data is inconsistent, the human shopper might hesitate and ask a question. An AI agent, programmed for efficiency, may simply buy – or worse, buy three sizes to bracket the purchase for its human master.
In this new reality, Returnalyze is optimized to process billions of data points to pinpoint flawed or incomplete product data. In one case our AI-powered analytics platform helped a major retailer determine that a "pull-on" pant description failed to mention a hidden zipper, causing mass confusion and returns. An AI agent, analyzing only the "pull-on" attribute, would have likely bought this for thousands of customers looking for zipper-free comfort.
By identifying discrepancies quickly and routing actionable intelligence appropriately, Returnalyze’s returns prevention platform can help ensure Product Detail Pages (PDPs) are optimized for agentic accuracy to get the purchase right the first time.
The Verdict
The National Retail Federation predicts that in 2026 growing AI optimization will level the playing field on brand visibility and other long-standing competitive advantages. Financial survival and healthy margins mandate that retailers invest in future-ready data strategies optimized for customer satisfaction and brand affinity – unencumbered by a massive influx of returns.
Returnalyze provides the AI-powered intelligence layer large retailers need to win.
Are you ready for AI Innovation? Schedule a demo or contact our team to learn how AI-powered returns prevention can transform your business.
Frequently Asked Questions (FAQs)
1. What is Agentic Commerce?
Agentic Commerce refers to autonomous AI agents that can identify products, compare options, and complete purchases on behalf of consumers based on intent, context, and structured product data.
2. Why does product data matter more than ad spend with AI agents?
AI agents rely on structured attributes, not marketing signals. If key product data is missing or inaccurate, the product may be excluded entirely from an agent’s consideration set.
3. What does “garbage in, garbage out” mean in agentic commerce?
If product attributes like sizing, fabric, fit, or features are incomplete or wrong, AI agents can make poor purchase decisions at scale, increasing dissatisfaction and returns.
4. How can poor data increase retail returns?
When agents select products based on flawed attributes, customers receive items that do not meet expectations, creating systemic return loops across thousands of automated purchases.
5. What is agentic accuracy?
Agentic accuracy means ensuring product data and PDP attributes are clean, consistent, and complete so AI agents can select the correct product the first time.
6. How does Returnalyze support AI-driven retail strategies?
Returnalyze analyzes SKU-level performance and returns behavior to identify flawed product data, enabling retailers to optimize PDPs and prevent errors before they reach customers or AI agents.
7. Will AI agents replace human shoppers?
AI agents will not replace consumers but will increasingly act on their behalf, especially for repeat purchases, apparel basics, and time-sensitive buying decisions.
.png)
