For almost twenty years, online shopping has worked the same way. A person searches, scrolls, filters, compares, reads reviews, checks prices, hesitates, maybe tabs out, and eventually buys.

Every improvement in retail, cleaner PDPs, better filters, smarter recommendations, has basically been an attempt to reduce friction in that same loop.

But something genuinely different is happening now. AI is no longer helping the shopper shop. It’s starting to shop for them.

What Google, Amazon, and the major payment networks are rolling out is a structural change in how decisions get made. 

McKinsey’s numbers make it clearer: agentic commerce could influence $3–$5 trillion in global retail spend by 2030, with up to $1 trillion in the U.S. alone. When shifts reach that scale, they stop being “emerging trends” and start becoming economic gravity.

What’s Agentic Shopping and What’s Really Changing?

On the surface, agentic shopping sounds simple: you tell an AI what you want, and it goes out and gets it.

But the simplicity is misleading. What’s changing goes beyond the interface; it’s the decision-maker. The work that used to belong to the user (searching, comparing, checking stock, monitoring prices) is shifting to the agent.

And the shift is already visible inside the largest commerce ecosystems.

Google’s new AI shopping experiences are a good example. You can type something as vague as “gifts under $50 for a dad who cycles” and, instead of a traditional results page, AI Mode interprets the intent, pulls structured insights from the Shopping Graph – a system with 50 billion product listings, refreshed continuously – and generates a curated, deeply contextual output: prices, reviews, availability, comparisons. It feels less like search and more like delegation.

“Let Google Call,” which uses Duplex + Gemini, takes this even further. Instead of the user calling stores to ask, “Do you have this in stock?”, the agent does it. It phones multiple retailers, checks inventory, compares prices, asks clarifying questions, and sends the user a summarized answer. It’s mundane, but it quietly replaces an entire interaction pattern.

Agentic checkout is another step. A user can set a price threshold for a specific SKU, and Google will monitor price movements, wait for the right moment, and complete the purchase automatically, using user-approved credentials and merchant-integrated flows.

Amazon is moving just as aggressively. Rufus is already being used by more than 250 million customers this year, with interactions growing 210% YoY. And the impact isn’t soft: customers who use Rufus during their shopping journey are 60% more likely to convert. 

The impact is already showing up in real numbers. Sensor Tower found that on Black Friday, Amazon sessions that included Rufus outperformed everything else. Sessions involving the AI assistant were up 35% day-over-day, compared with 20% for overall Amazon traffic. 

Even more interesting, the sessions that both used Rufus and resulted in a purchase surged 75% day-over-day, while purchases without Rufus grew only 35%. Across the trailing 30 days, Black Friday purchases doubled overall, but Rufus-assisted sessions were responsible for the bulk of that spike.

Adobe’s broader retail data shows the same trend. AI-referred traffic to U.S. retail sites was up 805% year-over-year on Black Friday, and shoppers who arrived from an AI service were 38% more likely to buy.

Payments are being rebuilt as well. Mastercard’s Agent Pay gives AI agents a verifiable way to transact on behalf of users using cryptographically signed mandates. 

The pattern is consistent across platforms:

• Google is rebuilding discovery and execution around agents.
  
• Amazon is rebuilding evaluation, recommendation, and buying around agents.
  
• Payment networks are rebuilding trust, authorization, and settlement around agents.
  

Put simply: the world’s largest commerce and payments systems have already shifted to an agent-first architecture. The user is still in the loop, but increasingly, they’re not the one driving the transaction.

Why 2026 Is the Breakout Year for Agentic Shopping

Every major shift in retail has the same pattern: the technology matures, consumer behavior moves ahead of it, and platforms quietly rewire the infrastructure underneath. When all three align, the curve bends.

Heading into 2026, all three are aligning in a way we haven’t seen in more than a decade.

1. Consumers are already changing how they search

McKinsey found that 44% of users who try AI-powered search prefer it over traditional search.

That’s a remarkable number. If nearly half of users are more comfortable describing what they want in natural language than navigating a results page, then the entry point to shopping is already reorganizing itself.

2. Models finally have enough reasoning capacity to replace user effort

For years, AI could imitate language but not reliably execute tasks with multiple steps. That’s different now. 

According to METR, the task duration that top models can complete with at least 50% reliability has been doubling every seven months. Claude 4.5 now sustains workflows representing 30+ hours of human effort.

That level of reasoning is what lets an agent:

• Read and interpret reviews
  
• Compare hundreds of options
  
• Understand constraints
  
• Check stock
  
• And weigh tradeoffs in a way that feels surprisingly close to how a person thinks
  

The gap between “suggest” and “decide” is narrowing fast.

3. The underlying protocols for agentic commerce exist

Up until recently, there was no shared infrastructure for agents to exchange context, talk to each other, or execute purchases with accountability. That’s changed.

• MCP creates a way for agents and tools to share persistent context.
  
• A2A allows agents across platforms to coordinate tasks.
  
• AP2 gives agents a verifiable, standardized way to pay on behalf of users through cryptographically signed mandates.
  

These standards aren’t flashy, but they solve the practical problems that make real-world agentic commerce possible.

4. The major platforms have already committed

The clearest sign that 2026 will be the acceleration point is how quickly the big platforms have reorganized around this model.

In the past year alone:

• Google shipped agentic shopping, agentic checkout, and agent-led calling.
  
• Amazon expanded Rufus and rolled out “Buy for Me.”
  
• Shopify released agentic infrastructure for cross-merchant cart building.
  
• Visa, Mastercard, and Stripe introduced new agent-capable payment frameworks.
  

When the companies that control discovery, evaluation, and transaction flows all move in the same direction, the trajectory becomes obvious.

What the Shopping Journey Looks Like in an Agentic World

Instead of starting with a search bar, shopping starts with intent.

A user might say:

• “I need sports gear for a ski trip in January.”
  
• “Buy this moisturizer whenever it drops below $40.”
  
• “Find me a TV that fits this space and is good for gaming.”
  
• “Replace my dog’s food when I’m running low.”
  

The agent handles the rest:

• It compares products across retailers.
  
• It checks real-time inventory.
  
• It analyzes review sentiment.
  
• It evaluates price history, deals, promos, and loyalty points.
  
• It flags items likely to sell out.
  
• It buys autonomously within constraints.
  
• It keeps the user informed.

The user becomes the approver, not the operator.

ChatGPT compares all the products for you

How Brands Should Really Prepare for Agentic Shopping

Most brands think they’re preparing for agentic shopping by “adding structured data” or “testing AI journeys.”

That work has value, but it doesn’t address the shift that’s coming.

If 2025 was the year AI learned to describe products, 2026 will be the year agents start deciding what people buy. And once agents begin making choices, the entire retail stack starts to look different.

McKinsey estimates that agentic commerce could redirect $3–$5 trillion in global retail spend by 2030. Nearly $1 trillion of that would come from the U.S. alone. Those numbers only make sense when the infrastructure beneath retail changes, not just the interface on top.

This is the work that actually matters over the next 12–24 months.

1. Your product catalog needs to speak “agent” fluently

Agents don’t infer meaning the way humans do. They don’t “get the idea.” They parse data. If information is unclear, buried in PDFs, inconsistently structured, or spread across multiple systems, the agent won’t stitch it together.

Brands will need to treat product data the way they treat media: something that directly affects performance.

This means:

• Attributes expressed cleanly
  
• Variant logic made explicit
  
• Use cases and constraints written as structured metadata
  
• Materials, sizing, compatibility, and warranties formalized
  
• Noise removed from feeds so models don’t have to guess
  

The clearer the product graph, the more often agents will surface it in comparisons and recommendations.

2. Inventory accuracy becomes a ranking signal

One of the least discussed realities of agentic commerce is how sensitive agents are to uncertainty. Humans might tolerate “Low stock” or “Ships in 3–5 days.” Agents tend not to.

Google’s Shopping Graph, which refreshes 2 billion updates per hour, already uses inventory as a reasoning input. So does Amazon’s Rufus. If your availability data is slow, inconsistent, or missing location granularity, your products will quietly fall out of the agent’s decision path.

Inventory systems that were once operational are now a key part of your strategy and a determinant of whether your product is even considered.

Availability is one of the core ranking factors

3. Reviews become structured evidence, not just social proof

Humans read reviews for reassurance.

Agents read them for patterns. They want to know durability issues, recurring complaints, sentiment changes over time, standout strengths, and edge cases.

Amazon is already converting millions of unstructured reviews into structured insights (“runs small,” “battery lasts 8 hours,” “good for winter travel”), and Google is moving in the same direction.

If brands don’t build their own review-enrichment pipelines, the models will build their own interpretation, and that interpretation won’t always match the narrative the brand wants.

Understanding what reviews mean becomes mandatory. 

4. Checkout must become agent-compatible

Almost every retail checkout flow today is designed around a human completing the final step. Agentic commerce breaks that pattern.

Agents need a clear, verifiable way to authenticate, authorize, and complete transactions. That’s why protocols like AP2, tokenized credentials, and agent-to-merchant verification flows are now emerging. 

Google, Stripe, Mastercard, Visa are all aligning around the same idea: agents must be first-class transaction actors.

If a checkout flow can’t accept an authenticated agent, the agent will move the transaction somewhere else. It won’t debate it. It won’t try again. It will simply pick a merchant it can complete the cycle with.

This becomes one of the most immediate competitive advantages in 2026.

5. SEO evolves into “agentic SEO” 

Agentic shopping doesn’t eliminate SEO, but it changes the mechanics behind visibility.

Traditional SEO is built around ranking on a page. Agentic SEO is built around being selected in a reasoning process.

Models evaluate:

• Data completeness
  
• Clarity of attributes
  
• Structured comparisons
  
• Review-derived sentiment
  
• Historical performance
  
• Price movement and promotion pattern
  

Agentic shopping is becoming a competitive divider much faster than most brands expect. Over the next 12–18 months, the strongest players will be the ones whose product data is structured, whose inventory signals are accurate, whose reviews are enriched, and whose checkout flows can accept agent-led transactions.

Competitors are already moving. They’re tightening their catalogs, upgrading their feeds, improving stock visibility, and turning reviews into structured evidence. 

As agents take on more of the evaluation and decision work, these signals start to determine which products surface and which get ignored.

The brands that outperform in 2026 won’t do it with better design or more noise. They’ll do it because their data, systems, and truth signals align with how agents reason and buy.