AI Shopping Agents: The New Front Door to Commerce

For years, online shopping has followed the same playbook: consumers type keywords, scroll through product lists, compare specifications, and manually make decisions. Mobile apps, personalization, and recommendation engines made improvements along the way, but the underlying model remained the same.

That model is now breaking. AI shopping agents are emerging as the new entry point to commerce. Instead of browsing, consumers can delegate the task to intelligent agents that interpret intent, compare products, and in some cases even complete transactions. Platforms like Perplexity and ChatGPT are already piloting these capabilities, and adoption is accelerating faster than many retailers expected.

Why AI Shopping Agents Matter

The rise of AI shopping agents is not an incremental update. It marks the beginning of the third wave of AI in commerce.

1. Predictive AI was the first wave, using historical data to forecast demand and optimize timing.
   
2. Generative AI followed, creating new content such as product copy, chat interfaces, and imagery.
   
3. Agentic AI is now here, with autonomous systems that not only reason but also act on behalf of consumers.

The numbers underscore the scale of this shift:

• Traffic to retail websites from AI platforms grew by 4,700% year over year by July 2025.
• 39% of shoppers have already used AI for shopping tasks, and more than half of Gen Z expect to rely on it this year.
  
• 63% of Gen Z say they are comfortable letting agents complete purchases directly.
  
• Global retail media spend is projected to surpass $200B in 2025, but agents prioritize structured product data over creative ads.

This is not a future scenario. It is already happening, and it is reshaping how products are discovered, compared, and purchased.

How AI Shopping Agents Work

AI shopping agents may look simple to users — a prompt in, a product recommendation out — but under the hood, they are powered by several key technologies.

• Understanding intent: Large language models (LLMs) break down requests like “Find running shoes under $100 with cushioning for long-distance training” into attributes such as product type, budget, and performance requirements.
  
• Retrieving information: On their own, LLMs do not know inventory or pricing. Retrieval-augmented generation (RAG) and vector databases allow agents to query live catalogs, surfacing results based on meaning rather than keywords.
  
• Reasoning across options: Agents can compare price, delivery, sustainability, or fit, sometimes orchestrating multiple agents to evaluate different dimensions.
  
• Taking action: Early pilots allow agents to add items to carts or complete checkout. Perplexity’s assistant, for example, has demonstrated end-to-end transactions, though some fail when inventory data is outdated.
• Guardrails and oversight: Because agents act autonomously, monitoring systems are critical. Guardrails prevent errors such as wrong orders, and in most pilots human oversight remains a backstop.

This workflow highlights why AI shopping agents are not just “better chatbots.” They represent a new layer of commerce infrastructure that requires reliable data, standardized taxonomies, and robust governance.

The Disruption Ahead

AI shopping agents introduce both opportunities and risks for retailers.

• Condensed journeys: Pilots show that browsing time can shrink from more than an hour to under five minutes [Salesforce, 2025]. For consumers, this is efficiency. For retailers, it reduces touchpoints for upselling or storytelling.
  
• Agent-to-agent transactions: Research from Meta and DeepMind shows AI agents can negotiate and cooperate at near-human levels. In retail, this could mean consumer-facing agents requesting items while retailer-side agents respond with price, stock, or promotions.
  
• Retail media under pressure: Sponsored placements may lose effectiveness if agents rely on structured attributes rather than human persuasion. Visibility will be determined by machine-readable data, not ad creative.
  
• Rising risks: Tests with Perplexity’s assistant revealed delays and failed purchases due to outdated inventory.Retailers without APIs and agent-ready catalogs risk being invisible in this new environment.
  

What Retailers Must Do

Early adopters are already showing how to adapt. Saks Fifth Avenue has used Salesforce’s Agentforce to create a digital stylist that interprets outfit photos and automates exchanges. SharkNinja deployed agents across customer support and sales, while Walmart’s Intelligent Retail Lab uses vision-based agents to monitor inventory. Carrefour applies agentic AI in supply chain and loyalty campaigns.

To compete in an agent-first landscape, retailers must:

• Enrich catalogs with structured, machine-readable product attributes.
  
• Expose live data via APIs for inventory, pricing, and logistics.
  
• Adopt standards like the Model Context Protocol (MCP) to ensure interoperability.
  
• Run simulation tests by prompting agents to find products, identifying gaps in attributes or taxonomy.

Those who act early will capture new pathways to customers, while those who delay risk being disintermediated by platforms that already control the consumer interface.

Get in Touch: Build What’s Next With Marvik

AI shopping agents are not a distant trend, they are already reshaping commerce. By changing how products are discovered, compared, and purchased, they are also redefining what it takes to compete in retail. Success will depend on whether retailers can build systems that agents can understand, trust, and act on.

This is where Marvik comes in. With over 200 AI projects delivered into production and partnerships with NVIDIA and Oracle, we help companies translate emerging technologies into real impact. From structuring product data to deploying multi-agent systems, we guide companies beyond pilots and into production-ready solutions that scale.

If your company is looking to unlock the potential of AI, Marvik is ready to help you turn ideas into impact.