Trading has undergone several major changes in the last few decades. What started with localized physical stores evolved into borderless, internet-driven e-commerce experiences.
Now, it’s thought that with the advent of AI, commerce could be headed for another transformation: agent trading, where AI agents help consumers discover products, compare options, and even complete purchases on their behalf.
But despite the excitement, many questions remain unanswered. Will consumers trust AI agents to make purchasing decisions? Will companies get a sufficient return on investment to justify the cost? And does autonomous shopping solve a real problem or simply add another layer of complexity to the purchasing journey?
Nevertheless, technology is advancing rapidly. Imagine a shopping experience where consumers no longer have to switch between tabs, compare dozens of products across different sites, or manually research each purchase. Instead, AI agents understand intentions, evaluate options, compare prices, and act according to predefined rules to help users make purchasing decisions. What once sounded futuristic is already taking shape.
In this article, we examine what agent commerce is, how it works, what technological developments are driving it, and what challenges might shape its future adoption.
Key insights
- Agentic Commerce represents a shift in which AI agents help consumers discover, compare and purchase products
- AI agents perform tasks based on user intent, simplifying the shopping process and increasing efficiency
- Consumer interest is growing: over 60% expect to use AI in their shopping experiences by 2026
- Technological developments such as the Agentic Commerce Protocol (ACP) and the Universal Commerce Protocol (UCP) are critical to enabling agent commerce
- Despite its potential, agent trading faces challenges related to consumer trust, security and the need for business investment.
What is Agent Trading?
In simple terms, agentic commerce refers to a trading model in which AI agents act as decision makers on behalf of customers.
Instead of manually searching for products, comparing options, filtering results, and completing purchases, users can rely on AI agents to complete these tasks based on their intentions, preferences, constraints, and purchasing goals.
To paint a clearer and more practical picture, Alex Moss explains Agentic Commerce in the SEO Unplugged: Agentic Commerce with Alex Moss podcast:
So it’s all connected.
I could literally say into the phone to my agent, go buy me some new shoes to go with that jacket I bought last week, and that’s it.
And it would disappear.
It would do the research.
And of course you can have a say in approving part of the trip.
At the core is Agent Commerce works like a digital shopping proxy. Humans define the intent or goal while AI agents execute the process behind the scenes. While AI does the heavy lifting, users remain in control of the final decision-making process.
Also read: Ensuring continuous discoverability with agentic AI for SEO
Agentic Commerce is the next big thing in e-commerce
The concept of agent trading may still sound futuristic, but change has already begun. Consumer behavior, AI adoption and industry forecasts all point to a future where AI agents become an active part of the buying process.
Here are some numbers that illustrate why agent commerce is the next big thing in e-commerce.
Consumers are already using AI in their purchasing process
Consumers already rely on AI-powered tools to discover products and make purchasing decisions. According to a McKinsey & Company report, More than 70% of AI-powered search users ask questions about categories, brands, products or services at the top of the funnel.
The same report almost found that too 50% of consumers already use AI-powered search experiences. As AI becomes increasingly part of product discovery, there may be increasing disruption to traditional search-driven traffic. In fact, the study suggests so Over time, companies could see 20-50% of their traffic moving away from traditional search experiences.
This highlights an important shift: consumers are no longer just searching; They are increasingly asking AI systems to guide their decisions.
Buyers expect agent dealing
Consumer interest in AI-powered shopping is also growing rapidly. The 2025 report entitled “Agentic Commerce: From Brand Loyalty to Bot Logic“ examined what shoppers think about AI agents in retail.
This is what the report stated More than 60% of buyers expect to use artificial intelligence in 2026. The results also showed a significant behavioral shift: consumers increasingly value convenience, speed, pricing and trust over platform loyalty.
Instead of searching through individual retailer apps, shoppers can rely on AI agents who can compare products across multiple platforms, evaluate reviews, identify the best deals, and complete purchases more efficiently. This is changing the competitive landscape from retailer-versus-retailer competition to AI-driven discovery ecosystems.
Analysts predict explosive growth in agent trading
Industry analysts also believe that agent trading will develop into a huge economic opportunity in the next few years. Another McKinsey The report suggests that agent trading could fundamentally change the shopping experience.
Based on the increasing adoption of AI-powered detection tools and increasing merchant readiness, the report estimates this By 2030, the U.S. B2C retail market alone could unlock $900 to $1 trillion in orchestrated sales potential. Globally, this opportunity could be between $3 trillion and $5 trillion.
How does Agent Commerce work?
At its core, agentic commerce combines human intent with AI-driven execution. Instead of manually browsing websites, comparing products, and completing purchases, users can delegate much of the shopping journey to AI agents. These agents understand goals, evaluate options, make decisions within defined constraints, and even conduct transactions on behalf of users.
The difference to conventional AI assistants lies in their ability to act. While assistive AI tools primarily provide information or recommendations, agent AI can independently perform tasks throughout the shopping journey.
Also read: What is the user journey in SEO?
Here you can see step by step how Agent Commerce works:
Step 1: Capture the intent
Every agent commerce journey begins with an intention. Instead of typing short keywords into a search bar, users interact with AI agents in conversation.
For example, a buyer might say:
- “Get me a pair of durable running shoes under $150.”
- “Load up on groceries for a vegetarian dinner party.”
- “Buy a formal shirt that matches the pants I bought last month.”
In this phase, the AI agent focuses on understanding the user’s goals, preferences, budget, delivery expectations, and constraints. If the request seems too broad, the agent may ask additional questions to clarify the intent before proceeding.
Step 2: Autonomous command execution and brand recognition
Once the intent becomes clear, the AI agent begins executing the task autonomously. Instead of scanning a single website, it scans multiple e-commerce platforms, marketplaces, product catalogs, reviews, pricing databases, and inventory systems simultaneously.
This is where Agent Commerce begins to transform traditional product discovery. Instead of displaying endless product pages, the agent narrows down the most relevant options based on the buyer’s needs.
At the same time, brands with better structured product data, accurate inventory information, transparent pricing and machine-readable content are more likely to be discovered by AI agents.
Read: Taxonomy SEO: How to optimize your categories and tags
Step 3: Assessment and decision making
After gathering options, the AI agent begins evaluating products and comparing trade-offs. It can analyze factors such as:
- Price and discounts
- Product specifications
- Customer reviews and ratings
- Shipping schedules
- Return Policy
- Brand trust and reputation
Rather than simply listing products, the agent argues the options and explains why certain products meet the buyer’s needs better than others.
Users can also further refine the decision-making process by adding conditions such as:
- “Show only products with free returns.”
- “Prioritize faster delivery.”
- “Exclude obsolete products.”
This creates a feedback loop in which the AI agent continually improves its recommendations based on user preferences.
Step 4: Buy
Once the buyer approves a product or sets predefined rules, the AI agent can proceed with the transaction. Using APIs, commerce protocols and secure payment systems, the agent can add items to shopping carts, apply discounts, authenticate payments and complete purchases.
In some cases the purchase can be made immediately. In other cases, the AI agent may wait for certain conditions, such as: B. a price drop, stock availability or faster delivery options before completing the transaction.
Even when AI takes over execution, users still remain in control through permissions, approval settings and spending limits.
Step 5: Support after purchase
The role of AI agents does not end after payment. Agent trading also extends to post-purchase experiences.
AI agents can continue to assist users by:
- Track shipments
- Manage returns or exchanges
- Monitoring refunds
- Sending delivery updates
- Reorder recurring products
- Recommend complementary products or accessories
This makes shopping an ongoing and intelligent experience rather than a one-time transaction.
Technological developments
Agent trading is not based exclusively on AI models. Behind the scenes, it depends on a growing ecosystem of protocols, frameworks, APIs and payment systems that help AI agents interact securely and efficiently with digital commerce platforms.
An important concept for designing agent AI is the Model Context Protocol (MCP). In agent AI, MCP enables AI models to connect to external systems, tools, databases and applications via a standardized communication layer.
Instead of creating separate integrations for each AI model and software platform, MCP creates a common framework that allows AI agents to access information and perform actions more consistently. Think of it as creating a common operating language between AI systems and digital tools so they can work together without requiring completely individual connections each time.
As agent trading continues to evolve as a use case of agent AI, similar commerce-focused protocols specifically for purchasing ecosystems are now emerging. These protocols help AI agents understand product information, communicate with merchants, compare inventory, and securely complete transactions on behalf of users.
Here are some key developments supporting agent trading:
Agentic Commerce Protocol (ACP)
One of the most important developments in this area is the Agentic Commerce Protocol (ACP)an open standard introduced by Stripe in collaboration with OpenAI.
ACP is designed to help AI agents interact more naturally with e-commerce systems by creating a standardized framework for product discovery, checkout and payment execution. Put simply, it provides the infrastructure that allows AI agents to go beyond simply recommending products and actually securely complete purchases on behalf of users.
The protocol is still at an early stage, but the first practical implementations are already emerging. For example, ChatGPT users in the US can already purchase products from Etsy merchants directly in chat using Stripe-powered checkout. Shopify integrations are also expected to follow.
This is important because it signals a shift from AI-powered recognition to AI-powered transactions that take place within the conversational interfaces themselves. Instead of redirecting users across multiple websites and checkout flows, ACP aims to make the entire shopping journey smoother and agent-friendly.
Another important aspect of ACP is its open standards approach. Rather than creating a closed ecosystem tied to a single platform, Stripe and OpenAI are positioning ACP as a framework that developers, merchants, and e-commerce platforms can adopt more broadly as agent commerce evolves.
Looking forward, protocols like ACP could become the foundational infrastructure for AI-driven shopping experiences, especially as more companies begin optimizing their product catalogs, payment systems and checkout experiences for AI agents and not just human users.
Also read: Increase the UX of your checkout page: Important tips for online shops
Universal Commerce Protocol (UCP)
As more and more AI agents enter the shopping process, a new challenge arises: How can these agents communicate with thousands of retailers, marketplaces, payment providers and service platforms without requiring individual integration for each?
That’s the problem that the Universal Commerce Protocol (UCP) aims to solve.
Launched by Google, UCP is an open standard designed to create a common language for agent trading. Instead of building separate connections between each AI agent and each commerce platform, UCP provides a common framework that allows them to communicate more efficiently throughout the purchasing journey.
Think of it this way: If agent commerce becomes mainstream, millions of AI agents could research products, check inventory, compare prices, place orders, and manage returns every day. Without a standardized framework, retailers and AI platforms would have to create and maintain countless one-to-one integrations. UCP aims to eliminate this complexity by providing a common set of rules for all participants to exchange commercial information.
What makes UCP particularly interesting is its broad scope of application. Unlike protocols that focus primarily on purchasing, UCP is designed to support the entire trading lifecycle, including:
- Product discovery
- Product comparison
- Shopping and checkout
- Order tracking
- Returns and post-purchase support
Google also designed UCP to work alongside other new AI standards, including Agent2Agent (A2A), Agent payment protocol (AP2), And Model context log (MCP). This allows companies to adopt agent commerce without having to completely replace their existing systems.
The initiative already enjoys significant support from industry. Google developed UCP together with major commercial companies, including Shopify, Etsy, Wayfair, Target, And Walmart. It also received support from companies like Mastercard, Visa, Stripe, And American Express.
While agent trading is still in its early stages, UCP represents an important step towards a future where AI agents, merchants and payment providers can operate within a single ecosystem rather than across siled platforms. In many ways, it provides the foundational infrastructure needed to make agent trading scalable across the digital economy.
Mastercard Agent Pay
While protocols like ACP and UCP focus on communication and interoperability, Mastercard Agent Pay focuses on one of the most critical challenges in agent trading: trust and secure payment processing.
Because AI agents are able to discover products, compare options, and make purchasing decisions, they also need a secure way to complete transactions on behalf of users. Mastercard Agent Pay was introduced to provide the infrastructure to do just that.
The platform is designed to allow AI agents to execute payments while adhering to custom permissions, authentication requirements and spending controls. Instead of giving AI systems unrestricted access to payment data, Agent Pay focuses on creating verified, traceable and authorized payment flows for agent-driven commerce.
One of the most significant developments was the collaboration with PayPal, integrating Mastercard Agent Pay into PayPal’s wallet infrastructure. It enables AI agents to securely complete transactions on behalf of PayPal users while maintaining the security and trust mechanisms that consumers already expect from digital payments.
This partnership is particularly important as it brings agent trading closer to practical implementation. Rather than existing only in experimental AI environments, agent-driven payments can potentially operate across a much larger ecosystem of merchants, consumers and payment networks.
Together, ACP, UCP and Agent Pay help lay the foundation for agent commerce. While ACP focuses on enabling AI agents to interact with merchants and complete purchases, UCP creates a common language that enables agents, retailers, and platforms to collaborate at scale. Agent Pay adds the layer of trust by enabling secure, authorized payments, bringing AI-driven shopping one step closer to reality.
FAQs: What is agent trading?
Agentic Commerce benefits both businesses and consumers by making shopping more efficient and personalized.
For users
AI agents can reduce research time, provide tailored recommendations, monitor prices, and automate routine purchases.
For companies
Agentic Commerce can streamline operations, improve personalization, automate repetitive workflows, support faster decision making, and help products reach customers faster. Together, these benefits provide a more convenient shopping experience while improving operational efficiency.
No, they are not the same. Agentic AI is the underlying technology that enables AI systems to understand goals, make decisions, and complete tasks autonomously. Agentic Commerce is a specific application of agent AI in purchasing and trading. In other words, agentic AI is the foundation, while agent trading is one of its real-world use cases.
In traditional retail, the buyer remains the primary decision maker and executor throughout the purchasing process. Even when AI exists, its role is largely limited to recommending products or improving the search experience. In agentic commerce, AI agents actively participate in the purchasing process by researching products, comparing options, and executing tasks on behalf of users, guided by predefined goals and preferences.
Several companies are already experimenting with agent commerce. For example, Amazon has its “Buy for me” feature that allows AI agents to purchase products from third-party websites when items are unavailable on Amazon.
Similar, Google is testing AI-powered shopping experiences This can monitor prices and automatically purchase products when they meet custom conditions. Beyond consumer purchasing, companies are also using AI agents to monitor inventory levels and automatically reorder supplies when inventory runs low.
Agent trading still faces important questions
While the technology behind agent commerce is rapidly evolving, widespread adoption is far from guaranteed. Many consumers may not feel comfortable giving AI agents the authority to make purchasing decisions or access payment methods on their behalf. Others may wonder whether autonomous shopping solves a real problem or simply makes it easier to buy more things, more often.
Companies face their own uncertainties. Supporting agent trading may require investments in new protocols, structured data, integrations, and AI-enabled trading experiences. Whether these investments will deliver measurable returns remains unclear, especially given that consumer adoption is still in its infancy.
There are also broader challenges to solve, including security, fraud prevention, AI bias, platform dependency and the potential loss of direct relationships between brands and customers. Agentic commerce may represent an exciting new direction for digital shopping, but its long-term success will depend on whether it can create value for consumers, merchants and the entire e-commerce ecosystem, not just the AI platforms that power it.
