What is Agentic Commerce? The Future of AI-Powered Shopping

TL;DR

Agentic commerce is the next major evolution in digital shopping, where autonomous AI agents act on behalf of consumers to discover products, compare options, and complete purchases.

Why it matters: Early pilots show 30% higher conversion rates, faster time-to-purchase, and reduced cart abandonment.
Who’s leading: Amazon (“Buy for Me”), Shopify (agent infrastructure), Stripe (Agent Toolkit), and payment giants like Visa, Mastercard, and PayPal.
What it means for businesses: Success requires agent-friendly product data, API-first systems, and trusted payment integrations.
How to prepare: Start small — optimize your product catalogs, pilot agent-ready use cases, and partner with payment providers to gain a first-mover advantage.

Background

Have you ever wondered how some online businesses manage to create such engaging and personalized shopping experiences? What if there was a way to leverage AI to give your customers more control and satisfaction? Welcome to the world of agentic commerce, where the blend of technology and consumer autonomy can redefine online shopping. In this article, we’ll answer your burning questions about what agentic commerce is, its benefits, and how you can prepare your business for its rise. We’ll also highlight the key players making waves in this field, setting the stage for your own success. Get ready to discover strategies that can elevate your e-commerce game and reshape the way you connect with your customers.

Understanding Agentic Commerce

What is Agentic Commerce?

Agentic commerce represents a revolutionary shopping model where intelligent, autonomous AI agents act as personal shoppers for consumers or procurement agents for businesses. According to Mirakl’s analysis, these AI agents autonomously search for products, compare options, and even complete purchases on behalf of users. Unlike traditional e-commerce where users manually browse websites and compare products, an AI agent handles these tasks, finding products that meet specific requirements, making recommendations, and executing purchases within set parameters.

The term “agentic” derives from “agency,” meaning the ability to act independently. As Coveo explains, agentic AI systems don’t just respond to direct commands; they proactively make decisions to achieve a goal. In commerce, this means the AI doesn’t merely show search results – it can decide which product best fits the user’s intent and go through the checkout process autonomously but within user-defined boundaries.

A real-world example is Amazon’s new “Buy for Me” feature, which allows an AI agent to purchase products from third-party websites on a user’s behalf while remaining within the Amazon app. This signals that agentic commerce is moving from concept to reality, handling transactions end-to-end.

The Meaning of Agentic Commerce

Agentic commerce fundamentally transforms the relationship between consumers and shopping. According to Checkout.com’s research, at its heart, agentic commerce involves AI agents that understand complex requests, negotiate or verify details, and carry out multi-step transactions without constant human input.

For example, an agent could be tasked with “find me a durable laptop under $1,000 with at least 16GB RAM,” then proceed to research options, compare prices, check reviews, and present the best choice – or even buy it for the user once approved. This represents a shift from reactive to proactive commerce, where technology anticipates and fulfills needs.

Agentic Commerce Definition and Overview

Key features that distinguish agentic commerce from earlier e-commerce models include:

Autonomous Product Discovery and Comparison:

Agentic AI shoppers can instantly analyze specifications, prices, and reviews across vast catalogs from multiple merchants to find the best match for a user’s needs.

Goal-Driven Transactions

Unlike typical recommendation engines, agentic commerce systems are goal-oriented. The agent understands the user’s objective and actively works to fulfill it, including refining product criteria and making optimal purchase decisions.

End-to-End Purchasing Capability

According to PayPal’s newsroom, agentic commerce integrates discovery with payments and fulfillment. A well-equipped agent can add items to cart across different stores, select shipping options, and complete checkout using stored payment credentials – all in one seamless flow.

Personalization and Learning

These agents leverage machine learning and large language models to continuously learn from user interactions. Over time, a shopping agent learns a consumer’s style, preferred brands, and quality expectations, making increasingly personalized recommendations.

Agent-to-Agent Communication

As GridDynamics reports, a defining feature on the horizon is the ability for buyer agents and seller agents to communicate directly. In a fully realized agentic commerce ecosystem, your personal shopping agent might negotiate with a merchant’s AI agent – exchanging requirements, inventory info, or even price bargaining. This is enabled by emerging protocols like Google’s Agent-to-Agent (A2A) framework and Anthropic’s Model Context Protocol (MCP).

User Control and Defined Parameters

Visa’s Navigate platform emphasizes that users remain in ultimate control. Consumers can set spending limits, brand preferences, and require confirmation for big decisions, ensuring the agent operates within user-defined guardrails.

The Future of Agentic Commerce

Why Agentic Commerce is the Next Big Thing

Industry experts are calling this the third major wave in digital commerce – the “Agentic Commerce Era” – following the eCommerce era (web shopping) and mCommerce era (mobile shopping). According to Bain Capital Ventures, intelligent agents operating on each consumer’s behalf will fundamentally alter how sellers compete and win.

The numbers support this trajectory. KPMG’s Q1 2025 AI Quarterly Pulse Survey found that 99% of executives surveyed said their organizations plan to deploy AI agents, with 67% expecting to buy pre-built solutions and another 27% opting for a build-and-buy approach. Additionally, 65% of organizations were already piloting AI agents in Q1 2025, up from 37% the previous quarter.

PayPal projects that within five years, 20-30% of its customers will start their shopping through AI agents or tools rather than through search engines or retailer websites directly. This represents a fundamental shift in how commerce operates.

The Impact of Agentic Commerce on eCommerce

The impact on e-commerce is multifaceted. GridDynamics research suggests that AI agents might increase cart conversion rates by approximately 30% while reducing time-to-purchase significantly. When the “decision” phase is optimized by AI, customers are more likely to complete sales with less cart abandonment.

For retailers, this means a potential shift in customer journey from browsing-driven discovery to agent-driven delegation. Less direct traffic as consumers rely on AI assistants to shop means retailers must ensure their products are attractive to AI agents through data quality, integration, and value propositions beyond just price.

However, retail experiences won’t become 100% hands-off in all categories. In experiential or style-driven verticals like fashion and home décor, browsing and inspiration remain important to customers. As Coveo notes, “this isn’t ‘decide for me’ in categories where the joy is in the journey.”

How Agentic Commerce is Shaping Consumer Behavior

Consumer behavior is evolving rapidly with agentic commerce. According to Salesforce’s Connected Shoppers Report, 65% of shoppers are interested in using AI to buy items once they hit a target price. This demonstrates a growing comfort with delegating purchasing decisions to AI agents for specific scenarios.

The rise of agentic AI also blurs the line between conversational commerce and transactional commerce. With partnerships like PayPal and Perplexity bringing shopping directly into AI chat interfaces, consumers can move from asking a question to buying a product in one seamless conversation. Commerce becomes embedded in everyday digital interactions, powered by agents that connect intent to action in real time.

The Role of AI in Enhancing Agentic Commerce

AI technologies are the backbone of agentic commerce. Large language models (LLMs) enable agents to understand natural language requests, while machine learning algorithms optimize product matching and price comparisons. According to Checkout.com, these AI systems make shopping “feel effortless” by finding exactly what customers want and facilitating payment in the same flow.

The sophistication of these AI agents continues to grow. They can now interpret complex, multi-faceted requests, understand context and nuance, and make judgment calls about product suitability based on learned preferences. This level of AI capability transforms shopping from a task into a service.

Key Players in Agentic Commerce

Major Companies in the Agentic Commerce Landscape

The agentic commerce landscape is rapidly evolving with major technology and payment companies leading the charge. According to PYMNTS.com, three of the world’s largest payment companies – Visa, Mastercard, and PayPal – are racing into this frontier, with all three recently announcing deployments of agentic commerce capabilities.

Major AI companies are also key players. Visa is working with Anthropic, Microsoft, Mistral, OpenAI, and Perplexity to integrate payment capabilities into AI chatbots. Mastercard is collaborating with Microsoft on new use cases to scale agentic commerce, with other leading AI platforms to follow.

E-commerce platforms are adapting quickly. Amazon’s “Buy for Me” feature represents one of the first major implementations, while Shopify and other platforms are developing agent-friendly infrastructure to support this new commerce model.

Amazon’s Revolutionary “Buy for Me” Feature

Amazon launched its groundbreaking “Buy for Me” feature in April 2025, marking a significant milestone in agentic commerce. According to Digital Commerce 360, this AI shopping agent allows customers to purchase products from third-party websites directly within the Amazon Shopping app.

How Amazon’s Buy for Me Works:

The feature uses sophisticated agentic AI powered by Amazon’s Nova and Anthropic’s Claude models. As TechCrunch reports, when customers search for branded items in the Amazon Shopping app, they see results from Amazon’s marketplace plus additional products from external brand sites in a section labeled “Shop brand sites directly.”

Behind the scenes, Amazon’s AI agent:

Visits external websites autonomously
Selects products matching user requirements
Fills out customer information including name, shipping address, and payment details
Completes the purchase using encrypted customer data
Provides order tracking within the Amazon app

Strategic Impact:

Cognizant’s analysis highlights that Amazon’s move is “massively significant” as the company already commands 40.9% of US retail e-commerce sales in 2024, with 56% of consumers starting their product searches on Amazon. This positions Amazon to extend its influence beyond its own marketplace, fundamentally changing the relationship between brands and platforms.

Security and Control:

Amazon emphasizes customer control and privacy. According to About Amazon, the company cannot see unrelated or past orders made on brand sites, and all fulfillment, delivery, returns, and customer service remain handled by the brand store, not Amazon itself. Customers receive confirmation emails directly from brands and can track orders within the Buy for Me Orders tab.

Shopify’s Agent-Friendly Infrastructure

Shopify has emerged as a leader in building infrastructure specifically designed for agentic commerce. According to Shopify’s developer documentation, the platform has launched comprehensive AI agent capabilities that represent “native shopping integration into AI conversations.”

Core Infrastructure Components:

Shopify’s blog outlines several key infrastructure elements:

Shopify Catalog MCP Server: Enables AI agents to search hundreds of millions of Shopify products across the entire merchant ecosystem. Agents can receive search results with product web components and render them directly in chat for rich, inline previews.
Universal Cart System: As PYMNTS reports, Shopify’s Universal Cart allows AI agents to manage shopping carts across multiple merchants simultaneously. The MCP server provides cart details, checkout URLs, and cart web components displaying items and subtotals.
Compliance and Checkout Integration: Shopify maintains merchant customizations and agent branding while meeting compliance requirements including GDPR, CCPA, and PCI DSS v4, without requiring platforms to handle their own payments.

Strategic Positioning:

Shopify’s president stated the company “has been building infrastructure to power agentic commerce” and was “ahead of the curve” with social commerce integrations. According to Digital Commerce 360, Shopify sees agentic commerce as the next key e-commerce trend, with integrations spanning Instagram, YouTube, Spotify, Roblox, and Perplexity AI.

In late 2024, Shopify integrated with Perplexity’s Buy with Pro AI shopper, enabling consumers to browse and transact directly via Shop Pay within Perplexity’s interface. This positions Shopify as the core infrastructure for “always-on, channel-agnostic shopping.”

Enterprise Expansion:

CB Insights Research notes that Shopify has built the foundation to compete directly with enterprise commerce platforms like Salesforce Commerce Cloud and Adobe Commerce. Since 2022, Shopify has expanded its network of system integrators, forming partnerships with Deloitte, EY, and KPMG, and in April 2024 deepened this strategy with a joint partnership alongside Google Cloud and Cognizant.

Implementation and Pricing:

According to Fluid.ai, entry-level tools like Shopify Magic start at around $20 to $50 per month, while comprehensive platforms can run $200 to $500 monthly. The capabilities are currently in early access, requiring an invitation from Shopify, with PYMNTS reporting that Shopify has introduced specific rules governing merchants’ use of agentic AI on its platform.

Stripe’s Pioneering Role in Agentic Commerce

Stripe has emerged as a major player in agentic commerce with groundbreaking announcements at Stripe Sessions 2025. According to Stripe’s blog, the company launched several key innovations including the Order Intents API, which allows businesses to create commerce agents in seconds, and the Stripe Agent Toolkit that enables AI agents to earn, store, and spend funds autonomously.

Stripe Agent Toolkit and Infrastructure:

In November 2024, Stripe launched its Agent Toolkit, an SDK that enables AI agents to engage in transactions with human users and pay third parties. As reported by The Letter Two, the toolkit supports OpenAI’s Agents SDK, Vercel’s AI SDK, LangChain, and CrewAI, working with any LLM provider that supports function calling.

With Stripe’s Issuing technology, agents can create single-use virtual credit cards that can be employed to pay for products with a simple LLM function call. Over 700 agent startups launched on Stripe in 2024, with the toolkit being downloaded thousands of times weekly.

AI Foundation Model for Payments:

Stripe unveiled what it claims is the world’s first AI foundation model for payments. The AI models built into their Optimized Checkout Suite use more than 100 signals to personalize checkout in real-time, including which payment methods are shown and which payment fields are displayed.

Market Impact:

Stripe’s newsroom reports that Stripe’s total payment volume reached $1.4 trillion in 2024, up 38% year-over-year, equivalent to around 1.3% of global GDP. The company is now used by half of the Fortune 100, 80% of the Forbes Cloud 100, and 78% of the Forbes AI 50.

Google’s Agent2Agent Protocol and Commerce Infrastructure

Google has positioned itself as a key infrastructure provider for agentic commerce through its Agent2Agent (A2A) protocol, launched in April 2025. According to Google’s Developer Blog, A2A is an open standard supported by over 150 organizations including PayPal, Salesforce, SAP, ServiceNow, and Workday.

A2A Protocol Features:

As Towards Data Science explains, A2A provides a universal translator for AI agents, allowing them to communicate and collaborate seamlessly regardless of vendor or underlying technology. Key features include:

Support for long-running tasks extending over days, weeks, or months
Multimodal collaboration for sharing text, audio, and video
Agent Cards in JSON format for advertising capabilities and security permissions
Enterprise-grade focus for complex business workflows

Google’s Agent Development Ecosystem:

Google Cloud Blog reports that Google has released the Agent Development Kit (ADK), which simplifies AI agent building with modular components. Google Agentspace provides a no-code interface for non-engineers to create and manage AI agents.

Real-World Implementation:

Tyson Foods and Gordon Food Service are pioneering collaborative A2A systems to drive sales and reduce supply chain friction, creating real-time channels for their agents to share product data and enhance the food supply chain.

The Involvement of PayPal in Agentic Commerce

PayPal has taken a distinctive approach to agentic commerce with its Agent Toolkit, which enables developers to integrate payment processes into the agentic AI workflow. According to Digital Transactions, PayPal offers a developer toolkit and access tokens that let AI agents interact directly with PayPal’s platform through APIs.

At a recent event for PayPal developers, the company demonstrated the potential of these tools when used in collaboration with technology like Google’s Gemini, AI by Amazon Web Services, and Microsoft’s Azure AI. PayPal’s partnership with Perplexity is particularly notable, as reported in PayPal’s newsroom, enabling seamless purchase experiences within AI-powered answer engines where the entire process, including payment, can be completed with a single user query or click.

PayPal also supports Google Cloud’s A2A protocol, representing a new way for developers and merchants to create next-generation commerce experiences powered by agentic AI.

Visa and Mastercard’s Role in Agentic Commerce

Visa Intelligent Commerce

According to Payments Dive, Visa launched its Intelligent Commerce program on April 30, 2025. The platform gives developers access to APIs and tools that integrate payment functions – such as identity verification and spending controls – into AI agents. Visa CEO Ryan McInerney stated that agentic commerce will be rolling out in the next few quarters.

Key to Visa’s system is tokenization technology. As TechCrunch reports, Visa creates 16-digit tokens that act essentially like a credit card for AI agents, linked to the consumer’s original card. It’s like giving the AI agent its own credit card but with strict, parental-style controls. Consumers decide when to activate it, what the agent can buy, and how much it can spend.

Mastercard Agent Pay

Mastercard’s newsroom announced the launch of its Agentic Payments Program, Mastercard Agent Pay, which integrates with agentic AI to revolutionize commerce. The solution promises to deliver smarter, more secure, and more personal payment experiences to consumers, merchants, and issuers.

Mastercard is working with acquirers and checkout players like Braintree and Checkout.com to enhance the tokenization capabilities they’re already using with merchants to deliver safe, transparent agentic payments. This collaboration ensures that the infrastructure for agentic commerce is robust and secure.

Benefits of Agentic Commerce

Advantages for Retailers and Brands

Agentic commerce offers numerous advantages for retailers and brands. According to Mirakl’s analysis, key benefits include:

Increased Conversion Rates: When AI agents find exactly what customers want and can purchase instantly, there’s less opportunity for cart abandonment. Early indicators suggest conversion rate improvements of approximately 30%.

Enhanced Customer Data and Insights: AI agents provide detailed data about customer preferences, decision-making patterns, and purchase behaviors that can inform product development and marketing strategies.

Reduced Customer Service Costs: AI agents handle routine inquiries and transactions, freeing human staff to focus on complex, high-value interactions.

New Revenue Opportunities: Businesses can offer subscription-based agent services, premium agent features, or agent-specific product bundles.

Global Market Access: AI agents can navigate language barriers and local payment methods, making it easier for businesses to serve international customers.

How Agentic Commerce Enhances the Shopping Experience

From a consumer perspective, agentic commerce dramatically improves the shopping experience. Checkout.com’s research highlights several enhancements:

Time Savings: Shoppers delegate tedious tasks like searching, reading reviews, and comparing specs to AI agents, making shopping feel effortless.

Personalization at Scale: AI agents learn individual preferences over time, providing increasingly accurate recommendations that feel truly personal.

24/7 Shopping Assistant: Unlike human assistants, AI agents are always available to help with purchases, price monitoring, and reordering.

Reduced Decision Fatigue: For busy or indecisive shoppers, having an AI agent narrow down choices to the best options is a game-changer.

Seamless Multi-Platform Shopping: Agents can shop across multiple stores and platforms simultaneously, finding the best deals and options without the user having to visit multiple websites.

The Competitive Edge of Adopting Agentic Commerce Early

Early adopters of agentic commerce stand to gain significant competitive advantages. According to Bain Capital Ventures, businesses that optimize for AI agent discovery and transactions now will be better positioned when agent-driven shopping becomes mainstream.

Key competitive advantages include:

First-Mover Brand Recognition: Early adopters become known as innovative, tech-forward brands that embrace the future of commerce.

Data and Learning Advantages: Companies that start working with AI agents early will accumulate valuable data about agent behaviors and preferences, informing future strategies.

Partnership Opportunities: Early adopters can form strategic partnerships with AI companies and payment providers, securing preferential terms and integration support.

Customer Loyalty: Businesses that provide superior agent experiences will build loyalty not just with human customers but with their AI agents, which may preferentially recommend their products.

Leading Agentic Commerce Companies and Startups

The agentic commerce ecosystem is rapidly expanding with both established players and innovative startups driving the market forward. According to CB Insights, the enterprise AI agents & copilots space is projected to generate close to $13 billion in annual revenue by the end of 2025, up from $5 billion in 2024.

Major Platform Players in Agentic Commerce

Amazon: Leading with its “Buy for Me” feature and Nova Act AI model for autonomous web browsing and purchasing

Shopify: Providing comprehensive agent infrastructure with MCP servers and Universal Cart system for multi-merchant transactions

Stripe: Powering over 700 agent startups with its Agent Toolkit and processing $1.4 trillion in payment volume in 2024

Google: Creating the foundational A2A protocol for agent interoperability, supported by 150+ organizations

Visa, Mastercard, PayPal: Providing secure tokenization and payment infrastructure specifically designed for agent transactions

Investment Trends in Agentic Commerce

The investment landscape for agentic commerce is experiencing explosive growth:

In Q1 2025 alone, AI investments crossed $30 billion
In 2024, nearly one-third of all venture funding (over $100 billion) went to AI-related startups, an 80% increase from the prior year
The global total addressable market for agentic commerce is estimated to reach $136 billion by 2025, with projections of $1.7 trillion by 2030 (67% CAGR)

Key venture capital firms active in the space include:

Bain Capital Ventures (coined the term “Agentic Commerce Era”)
Sequoia Capital (backing Hugging Face and Gong)
Andreessen Horowitz (a16z) (backing Character.AI and Replika)
General Catalyst (backing Cohere and AssemblyAI)
Accel (backing Adept and Scale AI)
Insight Partners and boldstart ventures (backing CrewAI)

Emerging Startup Business Models

Capital-efficient AI agent startups are showing remarkable performance metrics. Companies like Mercor ($4.5M revenue per employee) and Cursor ($3.2M per employee) already surpass Microsoft ($1.8M per employee) and Meta ($2.2M per employee), rivaling Nvidia’s efficiency levels ($3.6M per employee).

Accelerated funding cycles are becoming common, with companies like LangChain raising a $10M seed in early 2023 and a $25M Series A by Q1 2024, condensing what might have been a 2+ year journey into approximately 10 months.

Preparing for Agentic Commerce

How to Adapt eCommerce Operations for Agentic Commerce

Adapting e-commerce operations for agentic commerce requires strategic changes across multiple areas. GridDynamics recommends several key steps:

Optimize Product Data: Ensure product information is structured, comprehensive, and machine-readable. AI agents rely on quality data to make decisions, so incomplete or poorly formatted product information will hurt discoverability.

Implement API-First Architecture: Move toward headless commerce solutions that allow AI agents to interact directly with your systems through APIs rather than traditional web interfaces.

Develop Agent-Specific Pricing Strategies: Consider how pricing will work when AI agents can instantly compare prices across competitors. Value propositions beyond price become crucial.

Create Agent-Friendly Policies: Establish clear terms for agent purchases, returns, and customer service interactions. Consider how to verify agent authority and handle disputes.

Invest in Real-Time Inventory Management: AI agents expect accurate, real-time inventory data. Systems must be able to handle rapid queries and transactions from multiple agents simultaneously.

Building an Infrastructure for Agentic Commerce

Building the right infrastructure is critical for agentic commerce success. According to Mirakl’s research, essential infrastructure components include:

Robust APIs and Integration Capabilities: Your systems must be able to communicate seamlessly with various AI agents through standardized protocols and APIs.

Scalable Cloud Infrastructure: Agent interactions can create traffic spikes. Cloud-based solutions provide the flexibility to scale resources as needed.

Advanced Security Measures: With AI agents handling transactions, security becomes paramount. Implement tokenization, encryption, and fraud detection specifically designed for agent interactions.

Real-Time Analytics and Monitoring: Track agent behaviors, preferences, and transaction patterns to optimize your offerings and identify opportunities.

Flexible Payment Processing: Work with payment providers like Visa, Mastercard, and PayPal that offer agent-specific payment solutions and tokenization.

Essential Tools for Managing Product Data in Agentic Commerce

Product data management is crucial for agentic commerce success. Coveo and other experts recommend these essential tools and practices:

Product Information Management (PIM) Systems: Centralize and standardize product data across all channels, ensuring consistency and completeness.

Structured Data Markup: Implement schema.org and other structured data formats that make product information easily parseable by AI agents.

Dynamic Pricing Engines: Tools that can adjust pricing in real-time based on market conditions, inventory levels, and agent demand patterns.

Content Enrichment Tools: Automatically enhance product descriptions with specifications, compatibility information, and use cases that agents can analyze.

Data Quality Monitoring: Continuously monitor and improve data quality, as poor data will directly impact agent decision-making and sales.

Challenges in Implementing Agentic Commerce

The Product Data Challenge in Agentic Commerce

One of the biggest challenges in agentic commerce is product data quality and standardization. According to Mirakl’s analysis, the product data challenge involves several key issues:

Inconsistent Data Formats: Different suppliers and manufacturers use varying formats for product information, making it difficult for AI agents to compare products accurately.

Incomplete Information: Many product listings lack comprehensive specifications, making it challenging for agents to match products to user requirements.

Rapidly Changing Inventory: Real-time inventory accuracy is crucial but difficult to maintain across multiple channels and warehouses.

Multilingual and Regional Variations: Products may have different names, specifications, or availability in different regions, complicating agent operations.

To address these challenges, businesses must invest in data standardization, enrichment, and quality control processes that ensure AI agents can effectively discover and evaluate their products.

Risks Associated with Transitioning to Agentic Commerce

Transitioning to agentic commerce involves several risks that businesses must carefully manage. Checkout.com’s research identifies key risks:

Technology Dependencies: Heavy reliance on AI systems creates vulnerabilities if these systems fail or produce errors.

Privacy and Security Concerns: AI agents handle sensitive customer data and payment information, creating potential security risks.

Regulatory Compliance: The regulatory framework for AI agents making autonomous purchases is still evolving, creating uncertainty.

Market Disruption: Traditional business models may be disrupted as AI agents change how consumers discover and purchase products.

Investment Risk: Significant upfront investment in technology and infrastructure may not yield immediate returns.

Consumer Trust Issues in Agentic Commerce

Consumer trust remains a significant challenge for agentic commerce adoption. According to eMarketer and CivicScience research, only 24% of U.S. consumers said they’re comfortable sharing data with an AI shopping assistant, and just 47% are comfortable with AI agents purchasing recommended products on their behalf.

KPMG’s Steve Chase emphasizes: “As agents move into workflows, trust is back at the center of the AI conversation. There is no agent-powered future without a strong foundation of trust – grounded in governance, data integrity, and responsible use.”

To build trust, businesses must:

Provide transparent information about how AI agents work and what data they collect
Implement robust security measures and communicate them clearly to customers
Offer granular controls that let users set specific parameters for agent actions
Ensure human oversight and intervention options remain available
Build track records of successful, secure agent transactions

Payments in Agentic Commerce

Navigating Payments in the Agentic Commerce Era

The payment infrastructure for agentic commerce is rapidly evolving. According to Navigate Visa, the key to successful agentic payments is tokenization – creating secure, limited-use payment credentials specifically for AI agents.

This tokenization approach offers several advantages:

Enhanced Security: Tokens can be restricted to specific merchants, amounts, or time periods
User Control: Consumers maintain full control over spending limits and agent permissions
Fraud Prevention: Unusual agent behavior can be detected and blocked automatically
Seamless Integration: Tokens work with existing payment infrastructure

The Future of Payments with Agentic Commerce

The future of payments in agentic commerce is being shaped by major innovations from payment providers. Retail TouchPoints reports that payment companies are developing new capabilities specifically for agent transactions:

Programmable Money: Payment credentials that come with built-in rules and restrictions, ensuring agents operate within defined parameters.

Instant Settlement: Real-time payment processing to match the speed of AI agent decision-making.

Multi-Party Payments: Systems that can handle complex transactions involving multiple sellers, agents, and payment methods.

Cross-Border Capabilities: Solutions that enable AI agents to shop globally while handling currency conversion and international regulations.

How to Develop Payment Policies for Agentic Commerce

Developing appropriate payment policies for agentic commerce requires careful consideration of multiple factors. Mastercard’s guidelines suggest:

Clear Authorization Protocols: Define how AI agents are authorized to make purchases and what verification is required.

Spending Limits and Controls: Establish default and customizable spending limits for different types of agent transactions.

Dispute Resolution Procedures: Create clear processes for handling disputes when AI agents make purchases customers didn’t intend.

Refund and Return Policies: Adapt existing policies to account for agent-initiated purchases and returns.

Compliance Framework: Ensure policies comply with existing regulations while remaining flexible enough to adapt to evolving standards.

Insights and Resources on Agentic Commerce

For businesses looking to stay informed about agentic commerce developments, several authoritative sources provide ongoing coverage:

PYMNTS.com’s AI Commerce Coverage: Regular updates on AI and agentic commerce developments
Payments Dive: In-depth analysis of payment industry adaptations to agentic commerce
TechCrunch: Coverage of technology innovations and startup activity in the space
Bain Capital Ventures Insights: Strategic analysis of the agentic commerce landscape

Exploring Generative AI in Agentic Commerce

Generative AI plays a crucial role in enabling agentic commerce. According to GridDynamics, generative AI powers several key capabilities:

Natural Language Understanding: Enables agents to interpret complex, conversational requests from users.

Content Generation: Creates personalized product descriptions and recommendations for individual users.

Negotiation and Communication: Facilitates agent-to-agent negotiations and communications in marketplace settings.

Predictive Analytics: Anticipates user needs and preferences based on historical data and patterns.

To stay ahead in the rapidly evolving agentic commerce landscape, businesses should:

Subscribe to payment provider newsletters (Visa, Mastercard, PayPal) for product updates and best practices
Join industry associations focused on AI and commerce innovation
Participate in developer programs offered by major AI and payment companies
Attend conferences and webinars on agentic commerce and AI in retail
Follow thought leaders and researchers in the field on professional networks

Frequently Asked Questions about Agentic Commerce

What is an Autonomous Agent in Agentic Commerce?

An autonomous agent in agentic commerce is an AI-powered system that can independently perform shopping tasks on behalf of users. According to Coveo’s definition, these agents possess several key characteristics:

Goal-Oriented Behavior: They work toward specific objectives set by users
Decision-Making Capability: They can evaluate options and make purchasing decisions within defined parameters
Learning Ability: They improve their performance over time by learning from interactions and outcomes
Autonomous Operation: They can complete entire shopping workflows without constant human intervention

These agents differ from simple chatbots or recommendation engines because they can take actions, not just provide suggestions. They represent a new paradigm where AI doesn’t just assist shopping but actually conducts it.

How Do AI Agents Personalize the Shopping Experience?

AI agents personalize shopping through sophisticated learning and adaptation mechanisms. GridDynamics research explains that agents create “consumer digital twins” – detailed models of individual preferences and behaviors.

Personalization occurs through:

Preference Learning: Agents analyze past purchases, browsing history, and explicit feedback to understand individual tastes
Contextual Understanding: They consider factors like season, occasion, budget, and current needs when making recommendations
Predictive Modeling: Agents anticipate future needs based on patterns and life events
Real-Time Adaptation: They adjust recommendations based on immediate feedback and changing circumstances
Cross-Platform Learning: Agents can aggregate learning from multiple touchpoints to create comprehensive user profiles

Can AI Understand Buyer Intent in Agentic Commerce?

Yes, AI agents are increasingly sophisticated at understanding buyer intent. Checkout.com’s analysis shows that modern AI agents can interpret both explicit and implicit intent signals:

Explicit Intent: Direct statements like “I need running shoes for marathon training”Implicit Intent: Contextual clues like browsing patterns, time of day, or seasonal factorsEmotional Intent: Understanding urgency, excitement, or hesitation in user communicationsComparative Intent: Recognizing when users are comparing options versus ready to buyLong-Term Intent: Tracking evolving needs and interests over time

This understanding enables agents to provide more relevant recommendations and take appropriate actions, whether that’s immediately purchasing a needed item or waiting for a better price.

Is Agentic Commerce the Future of E-commerce?

Industry experts strongly believe agentic commerce represents the future of e-commerce. Bain Capital Ventures calls it the “third wave” of digital commerce, following e-commerce and mobile commerce.

Supporting evidence includes:

Major Investment: Visa, Mastercard, and PayPal are all investing heavily in agentic commerce infrastructure
Rapid Adoption: 65% of organizations were piloting AI agents in Q1 2025, up from 37% the previous quarter
Consumer Interest: 65% of shoppers express interest in using AI to make purchases when prices hit targets
Projected Growth: PayPal expects 20-30% of customers to start shopping through AI agents within five years

However, agentic commerce won’t replace all traditional shopping. It will coexist with conventional e-commerce, particularly in categories where browsing and discovery are part of the enjoyment. The future likely involves a hybrid model where AI agents handle routine purchases and research-intensive buying decisions, while humans remain engaged in experiential and creative shopping.

Conclusion

Agentic commerce represents a fundamental shift in how businesses and consumers interact in the digital marketplace. As AI agents become more sophisticated and payment infrastructure evolves to support them, we’re witnessing the emergence of a new commerce paradigm that promises greater efficiency, personalization, and convenience.

For businesses, success in the agentic commerce era requires strategic preparation: optimizing product data, building agent-friendly infrastructure, and partnering with the right technology providers. Early adopters who embrace these changes stand to gain significant competitive advantages as agent-driven shopping becomes mainstream.

While challenges remain – particularly around consumer trust and data standardization – the momentum behind agentic commerce is undeniable. With major players like Visa, Mastercard, PayPal, and leading AI companies driving innovation, the question isn’t whether agentic commerce will transform retail, but how quickly businesses can adapt to thrive in this new landscape.

The future of commerce is agentic, and the time to prepare is now.