Online Shopping Is About to Move From Browsing to Delegating

For most of the history of eCommerce, the consumer has performed the work.

A typical online shopping journey looks like this:

Need → Search → Browse → Compare → Read Reviews → Check Prices → Add to Cart → Checkout

Even after search engines, recommendation engines, and marketplaces made shopping easier, the final decision-making process largely remained human-driven.

Agentic commerce introduces a different model.

Instead of manually visiting multiple websites, a consumer could simply say:

“Find me the best noise-cancelling headphones under $300, prioritize comfort and battery life, and deliver them before Friday.”

An AI agent could then:

• Understand the requirement
• Search for relevant products
• Compare specifications
• Analyze prices
• Evaluate reviews
• Check inventory
• Compare delivery options
• Apply customer preferences
• Recommend the best option
• Potentially complete the transaction

This represents a fundamental shift:

From digital shopping to delegated shopping.

The economic consequences could be much larger than simply making checkout more convenient.

Agentic commerce could reshape who controls product discovery, how retailers acquire customers, how advertising works, and ultimately who captures value from a retail transaction.

What Is Agentic Commerce?

Agentic commerce is a commerce model in which AI agents can act on behalf of consumers or businesses to discover, evaluate, compare, recommend, and potentially purchase products or services.

The key difference between traditional AI assistance and an AI agent is agency.

A traditional chatbot might answer:

“Here are five laptops under $1,500.”

An AI shopping agent could potentially continue:

“Based on your previous preferences, coding requirements, budget, and preferred delivery date, I recommend Option B. It has the best performance-to-price ratio. Shall I purchase it?”

The evolution can be understood in four stages:

Stage 1: Traditional eCommerce

Human searches → Human compares → Human buys

Stage 2: AI-Assisted Shopping

Human asks → AI recommends → Human decides

Stage 3: Agentic Shopping

Human defines intent → AI researches and evaluates → AI assists transaction

Stage 4: Autonomous Commerce

Human establishes preferences and rules → AI monitors, decides, and transacts within authorized boundaries

The industry is currently moving rapidly between stages two and three, while autonomous commerce remains an emerging frontier. Deloitte describes agentic shopping as the current transformation phase and autonomous shopping as the next stage, where agents proactively search, decide, and transact within preapproved parameters.

The Core Difference: Search vs Intent

The existing internet economy is heavily based on search.

Consumers type keywords such as:

“Best laptop under $1500”

The search engine returns links.

The consumer performs the evaluation.

Agentic commerce changes this.

The consumer can communicate intent:

“I am a software developer. I need a laptop that can run multiple virtual machines, has at least 32GB RAM, weighs less than 4 pounds, and costs less than $1,500.”

This is a fundamentally richer commercial signal.

The AI agent does not simply match keywords.

It attempts to understand the desired outcome.

This creates a shift from:

Attention Economics

Retailers compete to capture clicks.

Toward:

Intention Economics

Retailers compete to become the best answer to a customer's specific requirement.

That distinction could redefine digital retail.

Which Industries Adopted Agentic AI First?

It is important to distinguish between agentic AI adoption in general and agentic commerce adoption specifically.

Agentic AI did not begin in retail.

Industries with structured digital workflows and high volumes of repetitive knowledge work were among the earliest adopters.

1. Technology and Software

Technology companies have been among the strongest early adopters of AI agents.

Software engineering and IT are particularly suitable for agentic workflows because tasks often involve:

• Defined objectives
• Digital environments
• APIs
• Structured workflows
• Measurable outcomes

McKinsey's 2025 survey found scaled AI-agent adoption particularly advanced in technology, especially in software engineering and IT.

Examples include agents that can:

• Write code
• Debug applications
• Monitor infrastructure
• Respond to incidents
• Manage cloud resources

2. Financial Services and Insurance

Financial services quickly became a major candidate for agentic systems.

Potential applications include:

• Financial research
• Fraud detection
• Customer support
• Investment analysis
• Insurance claims
• Personal finance management

The attraction is obvious.

Financial institutions operate with large amounts of structured information and rule-based processes.

However, financial services also face significant constraints around:

• Regulation
• Liability
• Data privacy
• Security

Agentic AI therefore has enormous potential but requires greater governance than many retail applications.

3. Customer Service

Customer service became one of the most practical entry points for AI agents across industries.

Unlike traditional chatbots, agents can increasingly perform actions.

For example:

“My package hasn't arrived.”

A traditional chatbot might provide tracking information.

An agent could potentially:

• Check the order
• Identify the shipping problem
• Contact the carrier
• Issue a replacement
• Process a refund

McKinsey notes that customer care has emerged as a major entry point for agentic AI adoption across industries.

4. Healthcare

Healthcare has explored agentic AI for:

• Knowledge management
• Administrative workflows
• Clinical documentation
• Scheduling
• Research support

However, fully autonomous decision-making remains more constrained because the consequences of mistakes can be severe.

Why Retail Is a Natural Industry for Agentic Commerce

Retail may not have been the first industry to adopt AI agents generally.

But it could become one of the industries most transformed by them.

Why?

Because shopping contains a large number of repetitive decisions.

Consumers repeatedly need to:

• Search for products
• Compare prices
• Evaluate alternatives
• Check availability
• Find discounts
• Track shipments
• Reorder essentials

These are precisely the types of information-heavy workflows AI agents can potentially automate.

Retail also has another advantage:

The transaction is already digital.

Modern eCommerce already has APIs and infrastructure for:

• Product catalogs
• Inventory
• Pricing
• Checkout
• Payments
• Logistics

The AI agent does not need to invent commerce infrastructure.

It needs to connect and orchestrate it.

That is why protocols and standards are now emerging specifically for agentic commerce.

Google's Universal Commerce Protocol (UCP), developed with ecosystem partners, is designed as a common standard for commerce interactions between AI agents, businesses, and payment providers.

Which Industries Need to Adopt Agentic Commerce First?

Not every industry will benefit equally or immediately.

The strongest candidates are industries with:

1. High transaction frequency
2. Large product selection
3. Complex comparison requirements
4. Repetitive purchases
5. Digital transaction infrastructure

1. Grocery and Consumer Packaged Goods

Grocery could be one of the most suitable industries.

Consumers repeatedly purchase:

• Milk
• Household supplies
• Personal care products
• Cleaning products
• Food staples

Imagine telling an agent:

“Maintain my household grocery inventory and reorder products when we are running low. Keep the monthly budget below $700.”

This is where autonomous commerce becomes especially powerful.

The agent can manage recurring demand rather than individual shopping sessions.

2. Electronics

Consumer electronics involve complex comparison.

A customer might compare:

• Processor
• RAM
• Battery
• Display
• Price
• Warranty
• Compatibility

AI agents can significantly reduce research time.

This makes electronics a strong early candidate for agent-driven product discovery.

3. Travel

Travel is already highly digital and comparison-heavy.

An agent could coordinate:

• Flights
• Hotels
• Transportation
• Activities
• Insurance

For example:

“Plan a seven-day trip to Japan under $4,000 and prioritize convenient flights and highly rated hotels.”

Travel may become one of the largest agentic transaction categories because consumers currently spend considerable time researching fragmented options.

4. Fashion and Apparel

Fashion is more complicated.

AI can assist with:

• Product discovery
• Style recommendations
• Size recommendations
• Price comparison

However, fashion also depends heavily on subjective preferences.

The opportunity is enormous, but agents will need strong personalization models.

Reducing returns could be particularly valuable.

5. B2B Procurement

B2B commerce may be one of the most economically valuable applications.

Businesses repeatedly purchase:

• Industrial supplies
• Software licenses
• Components
• Office equipment
• Raw materials

An agent could monitor:

• Inventory
• Supplier prices
• Delivery schedules
• Contract terms

Then automatically recommend or execute purchases.

Unlike consumer shopping, B2B transactions often follow established policies and approval workflows.

That makes agentic procurement particularly attractive.

How Expensive Is Agentic Commerce?

There is no single answer.

The cost depends heavily on what a company is trying to build.

A small merchant enabling products to be discoverable through AI channels has a very different cost structure from a multinational retailer building proprietary autonomous shopping agents.

We can think about the economics across three levels.

Level 1: AI-Ready Commerce Infrastructure

A retailer prepares its existing infrastructure for AI discovery.

Requirements may include:

• Clean product data
• Structured catalogs
• Accurate inventory
• APIs
• Modern checkout

For many retailers, this may primarily be an integration and data-quality investment.

The biggest hidden cost is often not AI.

It is fixing fragmented commerce infrastructure.

Level 2: AI Shopping Assistant

The retailer builds an AI-powered assistant capable of:

• Answering product questions
• Recommending products
• Comparing products
• Managing carts

Costs include:

• LLM usage
• Software development
• APIs
• Product data infrastructure
• Security
• Monitoring

This is substantially more expensive than simply deploying a chatbot.

Level 3: Fully Autonomous Commerce Agent

This is the most complex model.

The agent can:

• Search
• Negotiate
• Compare
• Make decisions
• Execute transactions

Now the business must solve additional problems:

• Identity
• Authorization
• Payment delegation
• Fraud prevention
• Liability
• Spending limits
• Audit trails

At this level, the largest cost may not be the AI model.

It may be governance and transaction infrastructure.

The most important economic lesson is:

The cost of agentic commerce is often less about generating AI responses and more about integrating AI into reliable business processes.

Companies frequently underestimate the integration challenge.

An agent is only as useful as the systems it can safely interact with.

The Hidden Cost of Agentic Commerce

Retailers should think beyond software licensing.

The real cost stack may include:

The biggest mistake would be to ask:

“How much does an AI agent cost?”

The better question is:

“What business process can the agent improve, and does the economic value exceed the cost of implementation?”

The Pros of Agentic Commerce

1. Better Product Discovery

Consumers can describe what they actually need rather than searching with imperfect keywords.

This could improve product matching.

2. Lower Customer Acquisition Costs

Retailers currently spend heavily acquiring traffic.

Agentic commerce could connect retailers with customers who already have purchase intent.

This may reduce wasted advertising spend.

3. Increased Convenience

Consumers could save significant time.

Routine purchasing decisions could be delegated.

4. Better Personalization

An agent could remember:

• Brand preferences
• Budget
• Sizes
• Dietary preferences
• Previous purchases

Shopping becomes increasingly contextual.

5. Greater Price Transparency

Agents can compare:

• Product prices
• Shipping
• Taxes
• Discounts
• Delivery times

Consumers can make more informed decisions.

6. Improved Operational Efficiency

Retail agents could also work internally.

Examples include:

• Inventory monitoring
• Replenishment
• Supplier selection
• Demand forecasting

This expands agentic commerce beyond the consumer shopping interface.

The Cons and Risks of Agentic Commerce

1. Retailers Could Lose Control of Customer Relationships

If customers begin shopping through third-party agents, retailers may no longer own the discovery experience.

The AI interface becomes the new front door.

This creates platform dependency.

2. Margin Compression

Perfect comparison can intensify competition.

If agents always find the cheapest equivalent product, retailers may face increasing pressure on margins.

This is particularly dangerous for commodity products.

3. New Digital Gatekeepers

Agentic commerce could reduce dependence on traditional search engines while creating new dependencies.

Retailers might move from:

Paying Google for traffic

To:

Paying AI platforms for recommendation access.

The gatekeeper changes.

The economic dependency may remain.

4. Recommendation Bias

A critical question will be:

Who decides what the AI recommends?

Potential conflicts could emerge if recommendations are influenced by:

• Advertising
• Affiliate commissions
• Platform partnerships
• Commercial agreements

Transparency will become essential.

5. Security and Fraud

Giving AI agents transaction authority introduces risk.

An agent could potentially:

• Make unauthorized purchases
• Misinterpret instructions
• Interact with fraudulent sellers

Agent identity and payment authorization will become major infrastructure challenges.

6. Reduced Brand Discovery

If an AI agent only presents three recommendations, hundreds of other brands become invisible.

Traditional browsing creates opportunities for accidental discovery.

Agentic filtering could reduce that discovery.

How Agentic Commerce Could Change Advertising

This may be one of the most important economic consequences.

Today's digital advertising model is based heavily on:

Impressions → Clicks → Website Visits → Conversion

Agentic commerce could compress this funnel.

The consumer might simply say:

“Find me the best product.”

The agent may provide three recommendations.

The consumer never sees:

• Ten advertisements
• Twenty product pages
• Multiple sponsored listings

This reduces the available attention inventory.

But advertising will probably not disappear.

It will evolve.

The future may shift toward:

Recommendation Advertising

Instead of paying for clicks, brands could compete to become eligible for AI recommendations.

This creates a new competitive discipline.

Retailers may need to optimize:

• Product data
• Customer reviews
• Price
• Availability
• Delivery performance

The future equivalent of SEO may be:

Optimizing products for AI agents.

Agentic Commerce and Marketplace Economics

Marketplaces have historically controlled product discovery.

Consumers visit the marketplace.

Sellers compete inside the marketplace.

Agentic commerce could potentially reverse this model.

The AI agent could search across:

• Marketplaces
• Brand websites
• Specialty retailers
• Local stores

However, there is an interesting counterargument.

AI agents need reliable:

• Product data
• Reviews
• Inventory
• Payment infrastructure
• Fulfillment

Large marketplaces already possess these capabilities.

Recent analysis suggests AI-referred retail traffic is increasingly concentrating on large marketplaces because their structured catalogs, reviews, trust signals, and fulfillment capabilities make them easier for AI systems to evaluate.

Therefore, agentic commerce may not necessarily destroy marketplaces.

It could actually strengthen the largest ones.

The winners may be platforms with the best:

Data + Catalog + Logistics + Trust + Checkout Infrastructure

Two Well-Known Agentic Commerce Solutions in the Market

1. Shopify Agentic Commerce

Shopify Agentic Commerce

Shopify has positioned itself as one of the major infrastructure providers for agentic commerce.

Its ecosystem includes:

• Shopify Catalog
• Agentic Storefronts
• AI-channel integrations
• Checkout infrastructure
• Universal Commerce Protocol support

Shopify's approach is particularly significant because it focuses on enabling merchants to sell across multiple AI interfaces rather than building only one shopping agent.

According to Shopify, its infrastructure supports selling through AI channels including ChatGPT, Microsoft Copilot, Google AI Mode, and Gemini. Shopify also reports rapid growth in AI-driven traffic and orders to its merchant ecosystem.

Strategic Position

Shopify is effectively attempting to become:

The commerce infrastructure behind AI-driven shopping.

2. Google's Universal Commerce Protocol (UCP)

Universal Commerce Protocol

Google's Universal Commerce Protocol is an open standard designed to enable AI agents, merchants, and payment providers to interact across the commerce lifecycle.

Rather than being only a consumer-facing shopping assistant, UCP addresses the infrastructure layer required for agentic transactions.

It covers the ability to connect commerce systems around:

• Product discovery
• Cart creation
• Checkout
• Payments
• Post-purchase interactions

Google developed UCP with broad industry participation, including major retailers, commerce platforms, and payment companies.

Strategic Position

UCP represents an important direction for the industry:

Agentic commerce may depend less on individual AI products and more on interoperability standards.

Without common standards, every AI agent would need separate integrations with every retailer.

That does not scale.

Protocols such as UCP attempt to solve this infrastructure problem.

The Future of Retail With Agentic Commerce

The long-term evolution may happen in three phases.

Phase 1: Conversational Commerce

Consumers ask AI for recommendations.

Example:

“What are the best running shoes for beginners?”

The consumer still makes the final decision.

Phase 2: Agentic Shopping

Consumers define objectives.

Example:

“Find the best running shoes under $150 and prioritize comfort.”

The agent researches and recommends.

Phase 3: Autonomous Commerce

Consumers establish rules.

Example:

“Replace my running shoes when my preferred model drops below $120.”

The agent monitors the market and acts within predefined authorization.

This is where retail could fundamentally change.

Shopping becomes less of an activity.

It becomes an automated background process.

The Most Important Economic Shift: From Attention to Trust

For the past two decades, online retailers have fought for attention.

They invested in:

• SEO
• Google Ads
• Social media
• Influencer marketing
• Marketplace advertising

Agentic commerce could introduce a different competitive asset:

Trust.

Why?

Because consumers may allow AI agents to make decisions on their behalf.

The winning agent will need consumer trust.

And the winning retailer will need agent trust.

A retailer's competitive position may increasingly depend on:

• Accurate product data
• Reliable inventory
• Transparent pricing
• Consistent quality
• Good customer reviews
• Strong fulfillment performance

The question may shift from:

“How do we get customers to visit our website?”

To:

“How do we become the most trusted recommendation for an AI agent?”

Conclusion: Agentic Commerce Is Not Just a New Shopping Feature

Agentic commerce represents a potential restructuring of the digital retail economy.

It could change:

• How consumers discover products
• How brands acquire customers
• How advertising is sold
• How marketplaces compete
• How prices are compared
• How retailers protect margins

The greatest opportunity is convenience.

The greatest risk is commoditization.

If AI agents become highly effective at comparing products, retailers selling undifferentiated products may face intense price competition.

At the same time, retailers with superior products, accurate data, efficient fulfillment, and strong customer trust may gain access to high-intent customers without spending as much on traditional advertising.

The future of retail may therefore become a competition between two strategies.

Strategy One: Compete for Human Attention

Advertising, branding, social media, and website traffic.

Strategy Two: Compete for Algorithmic Recommendation

Data quality, product performance, pricing, availability, and customer satisfaction.

The most successful retailers will probably need to do both.

Agentic commerce will not eliminate traditional retail experiences.

Consumers will still browse, discover, and shop emotionally.

But for routine, research-heavy, and price-sensitive purchases, AI agents could become a powerful new layer between consumers and retailers.

The biggest question is no longer whether AI will participate in commerce.

It already is.

The real question is:

When AI becomes the consumer's shopping representative, who will control the relationship—and who will capture the economic value?

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