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AI shopping agents are changing product discovery
Key Takeaways
- Agentic commerce is the shift from shoppers searching for products to AI agents discovering, recommending, and purchasing products on their behalf using natural language intent.
- AI shopping agents differ from chatbots by reading live product catalogs and making contextual recommendations, not just answering scripted queries.
- Product data quality determines whether AI agents can function effectively, poorly structured catalogs limit discovery.
Shoppers are already changing how they find products. Instead of typing “blue running shoes size 10,” they’re asking, “What’s the best lightweight running shoe for a half marathon?”, and expecting a clear answer, not a list of links.
This shift is what’s driving agentic commerce, a model where AI systems don’t just respond to queries but actively guide product discovery and purchase decisions. Solutions like Netcore Unbxd’s AI Shopping Agent are built around this shift, turning search into a conversation that leads to action.
In this blog, we’ll break down what agentic commerce actually means, how AI shopping assistants work, and what this change requires from ecommerce teams today.
What Is Agentic Commerce?
Agentic commerce is the model of ecommerce where AI agents discover, evaluate, recommend, and potentially purchase products on behalf of shoppers, acting with autonomy based on natural language instructions rather than keyword queries.
The shift in shopper behavior
Traditional ecommerce assumes shoppers know what they’re looking for. They type keywords, filter results, and manually compare products.
Agentic commerce flips that model. Shoppers express intent in plain language, and the system interprets it.
For example:
- Search: “running shoes.”
- Agentic: “I need lightweight running shoes for long-distance runs in hot weather.”
That difference is not cosmetic; it changes how discovery works entirely.
What makes commerce “agentic”
An “agent” doesn’t just respond, it acts.
In ecommerce, this means: Interpreting user intent
- Evaluating product options
- Recommending relevant items
- Guiding users toward purchase
The system becomes an active participant in the buying journey, not just a retrieval tool.
Agentic commerce vs. conversational commerce: what’s the difference?
Conversational commerce enables interaction through chat or voice interfaces. Agentic commerce goes further.
- Conversational commerce: Helps users navigate and ask questions
- Agentic commerce: Takes action based on intent and can drive decisions
The difference is autonomy.
What Is an AI Shopping Agent for Ecommerce?
An AI shopping assistant is the interface through which agentic commerce operates. It replaces the search bar with a conversation.
How AI shopping agents work: a 4-step process
- User expresses intent in natural language
Example: “Show me durable office chairs under $300”
- The system interprets intent using language models
It identifies constraints like budget, use-case, and preferences
- Relevant products are retrieved from the catalog in real time
This depends heavily on how well the catalog is structured
- The agent presents recommendations and refines them conversationally
It may ask follow-ups or narrow results dynamically
Netcore Unbxd’s AI Shopping Agent: built to sell, not just respond
Netcore Unbxd’s AI Shopping Agent is designed as a sales layer, not just an interface. It:
- Reads catalog data in real time
- Understands complex shopper intent
- Recommends products with context
- Guides users toward purchase
As Angela Maynard, Director of Ecommerce Operations at Restaurant Equippers, explains:
“The Shopping Agent has been a real game-changer for us. It helps bridge the gap between in-store and online experiences, guiding customers, anticipating their next question, and proactively surfacing the right products.”
AI shopping agents vs. chatbots: a critical distinction
This is where most confusion happens.
- Chatbots: Scripted responses, FAQ handling
- AI shopping agents: Real-time product understanding and recommendations
The difference isn’t the interface, it’s the intelligence behind it.
Best AI Shopping Agents for Ecommerce Product Discovery
The leading AI shopping agents for ecommerce product discovery are:
- Netcore Unbxd AI Shopping Agent
- Bloomreach Clarity
- Algolia AI Shopping Assistant
What differentiates Netcore Unbxd is its focus on on-site product discovery tied directly to catalog intelligence, rather than treating the agent as an overlay.
The hidden constraint: Why product data quality determines what AI agents can do
AI shopping agents are only as effective as the data they rely on.
If your catalog lacks structure, the agent cannot interpret intent correctly.
What the Agentic Commerce Protocol (ACP) requires
The Agentic Commerce Protocol (ACP), introduced by companies like OpenAI and Stripe, is emerging as a way to standardize how product data is structured for AI systems. The implication is simple: If product data is not structured properly, AI systems cannot use it effectively
The readiness gap: most catalogs are not AI-ready
Many ecommerce catalogs were built for search—not for reasoning.
They often:
- Lack consistent attributes
- Miss contextual descriptions
- Don’t encode use-cases
This limits what AI agents can do.
How enriched catalog data enables agent performance
When product data includes:
- Clear attributes
- Use-case descriptions
- Structured metadata
AI agents can:
- Match intent more accurately
- Recommend better products
- Reduce friction in discovery
As Rajesh Jain, Founder and Group MD, Netcore Cloud, puts it:
And Nishant Jain, Chief Strategy Officer, adds:
“Smart discovery starts with smarter data. You can’t build a smart agent on messy data.”
What this means for ecommerce brands right now
This shift is already happening. The question is whether your stack is ready.
3 things to do before deploying an AI shopping agent
- Audit your catalog for AI-readiness
Check attributes, taxonomy, and descriptions
- Evaluate your discovery stack
Does your current search solution support conversational discovery?
- Assess data structure and standardization
Ensure your product data can support intent-based queries
How to measure AI shopping agent performance
Focus on metrics tied to discovery and engagement:
- Add-to-cart rate
- Average order value (AOV)
- Cart abandonment
- Time to order
These reflect whether the agent is actually improving the buying journey, not just engagement.
Frequently Asked Questions
Yes. Most AI shopping agents are deployed alongside existing site search systems rather than replacing them immediately. They act as an additional discovery layer, handling complex, intent-driven queries, while traditional search continues to serve keyword-based navigation and browsing use cases.
Yes, and significantly. Unlike traditional search, which can function with minimal attributes, AI shopping agents rely on structured and descriptive product data to interpret intent. Incomplete or inconsistent data directly limits recommendation accuracy and overall performance.
No. While most current implementations are on-site, AI shopping agents are increasingly being integrated into external platforms such as chat interfaces and AI assistants. This expands product discovery beyond the ecommerce website into broader digital ecosystems.
The primary risk is poor user experience due to weak data foundations. If the catalog is not enriched or structured properly, the agent may return irrelevant or incomplete recommendations—reducing trust and adoption rather than improving discovery.
