Agentic Commerce in Retail: How AI Agents are Changing Digital Shopping

Quick Summary:

• Agentic commerce uses AI agents to discover products, compare options across retailers, and complete purchases on your behalf.
• Instead of traditional browsing, AI agents interact directly with commerce platforms through APIs and structured product data.
• Retailers are already using agentic AI for shopping assistants, automated purchasing, merchandising, and inventory decisions.
• Successful adoption of agentic commerce requires modern commerce architecture, clean product data, strong security controls, and governance frameworks.
• As AI agents get more capable, agent-to-agent transactions and AI-mediated shopping ecosystems could completely redefine digital commerce.

The way people shop online is changing fast. Instead of searching websites, comparing products, and checking out manually, shoppers are increasingly relying on AI agents that handle all of that for them.

This shift is called agentic commerce. In this model, AI agents can search product catalogues, compare options across multiple retailers, and complete purchases on a shopper’s behalf. 

As a retailer, it is crucial for you to understand how this shift has transformed how digital commerce platforms must operate.

This Agentic commerce extends far beyond chatbots and recommendation engines, in which intelligent AI agents act on behalf of consumers and collaborate directly with retailer systems to fulfil demand.

Your systems must be structured so AI agents can access product data, pricing, inventory, and checkout services directly through APIs.

Agentic Commerce Market Forecast

The global Agentic Commerce market is entering a period of hyper-growth as autonomous AI systems redefine the relationship between consumers and digital storefronts.

Valued at $5.71 billion in 2025, the market is projected to reach an estimated $65.47 billion by 2033. This represents a robust Compound Annual Growth Rate (CAGR) of 35.7% during the forecast period of 2026 to 2033.

This article breaks down what agentic commerce is, how it works, and why every retailer needs to pay attention right now. We’ll also cover the architecture behind agentic commerce, real-world use cases, key challenges, and the steps your business can take to get ready.

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What Is Agentic Commerce?

Agentic commerce is what happens when the buyer is no longer a person. An AI agent, working on a shopper’s behalf, searches your catalog, compares prices across competitors, applies stored loyalty credentials, and completes checkout without a human touching a keyboard. McKinsey estimates this model could direct up to $1 trillion in U.S. B2C retail revenue by 2030.

That number only lands if you understand what it means for your business right now.

How does Agentic Commerce Differ from Ecommerce?

Traditional ecommerce asks a human to do the work. Search, filter, compare, click, check out. Every step requires a decision.

Agentic AI e-commerce shifts that work entirely to software. AI agents scan multiple retailers simultaneously, evaluate products against user preferences and budget constraints, and execute purchases on the buyer’s behalf, often without a single page visit to your site.

The AI agent is not browsing out of curiosity. It’s making a judgment call about whether your product data is worth routing a buyer to.

From Hype to Reality: AI’s Defining Year

Three things converged fast.

1. Consumer behavior moved first

Adobe tracked more than one trillion U.S. retail site visits and found that generative AI traffic grew 4,700% year-over-year by July 2025, with 38% of U.S. consumers reporting they had already used AI for online shopping.

2. The payment rails followed

On Mastercard’s October 2025 earnings call, CEO Michael Miebach confirmed the first agentic commerce transaction had cleared on its live network, with U.S. Bank and Citibank cardholders already enabled. Visa launched its own program the same week. These are not pilots. 

3. Engagement data made the stakes plain

Adobe data, cited by BCG, shows that AI-driven visitors to U.S. retail sites spent 32% more time on site, viewed 10% more pages, and had a 27% lower bounce rate than traditional shoppers. [STAT-CHECK]

These are not casual browsers. They arrive with a specific objective. If your product data does not give them a clear answer, they move to a competitor who does.

Why Agentic Commerce is Emerging Now?

The agentic commerce opportunity isn’t arriving because of one breakthrough. Three separate forces converged in roughly the same 18-month window: AI capability, open infrastructure, and consumer readiness. Each one alone wouldn’t have been enough. Together, they crossed a threshold.

Here’s what actually changed and why it matters to your business.

Advances in Large Language Models

The models became capable of actually closing a transaction without failing mid-task. That happened faster than most C-suites tracked. McKinsey, citing METR research, found that the duration of tasks LLMs can reliably complete with at least 50% success has doubled every 7 months since 2019. By 2025, Claude 4.5 extended that window to more than 30 human hours.

In retail terms: an AI agent can now research a category, compare specs against a buyer’s constraints, validate inventory across multiple retailers, and complete checkout without a human stepping back in. Two years ago, it couldn’t do that reliably.

IBM identifies three ways modern AI agents differ from earlier retail AI: they act without constant user input, they adapt to changing conditions such as price shifts or stock depletion, and they operate across multiple systems rather than on a single platform.

That third capability is what makes agentic AI commerce real. An AI agent confined to one platform is a chatbot. An AI agent that moves across your catalog, a payment processor, and a fulfillment API is a buyer.

API-Driven Commerce Platforms

LLM capability only matters if there’s something for the AI agent to connect to. That infrastructure is now an open standard.

The Agentic Commerce Protocol (ACP) gives AI agents like ChatGPT a deterministic, programmatic checkout path with a seller, handling buyer selections and payment credentials directly, without the agent traversing a website page by page. Think of it as MCP for commerce checkouts.

For AI agents to transact at scale, retailers must expose APIs for product catalogs, real-time pricing, inventory, and return policies. That machine-to-merchant layer enables an AI agent to validate and complete a purchase without human intervention.

If your checkout can’t be reached programmatically, AI agents route buyers to a competitor that can.

Emerging Models of Agentic Commerce

The types of agentic commerce taking shape aren’t uniform. Industry research maps six distinct models, each representing a different level of agent autonomy and merchant integration.

1. Conversational Assistant

The AI agent guides discovery and comparison through natural language. The human still confirms and clicks. Amazon Alexa, Google Gemini Shopping, and retailer-embedded chatbots operate here today.

2. Purchase-in-Chat

The entire transaction takes place within the conversational interface. The Paypers identifies this as the model where the website becomes optional. The AI agent manages discovery and checkout in one uninterrupted flow, as seen with ChatGPT Instant Checkout and Klarna’s in-chat experiences.

3. Browser-Based Autonomous Agent

The AI agent navigates a retailer’s website on the shopper’s behalf, browsing, comparing, and initiating checkout. AWS identifies CAPTCHA friction as one of the biggest obstacles to reliable browser-based agentic commerce workflows.

The agent halts mid-task, waiting for human intervention at exactly the moment the shopper expected seamless automation. Structured protocols like ACP exist specifically to replace this model with a deterministic, programmatic checkout path.

4. Token-Based Credential Relay

The AI agent facilitates checkout without holding card data. Mastercard’s Agent Pay framework is built on this model. Tokenized credentials allow verified AI agents to transact securely while merchants retain full visibility, audit trails, and dispute rights.

5. AI Platform as Merchant of Record

The agentic commerce platform accepts payment, orders from the merchant, and ships directly. Visa’s agentic commerce research identifies this as a distinct model where the AI application acts as both wallet and payment aggregator, with Visa expecting this to coexist alongside token-based models.

6. Fully Autonomous Agent-to-Agent

AI agents negotiate directly with merchant networks, logistics providers, and payment systems without any human input. McKinsey describes this as the frontier model where commerce is structured around agent-to-agent interactions, with personal AI agents negotiating directly not just with merchant sites but with specialized agent networks for pricing, logistics, payments, and loyalty.

Most U.S. retailers will encounter models 1 through 4 within the next 12 to 18 months. Models 5 and 6 are where agentic e-commerce gets structurally disruptive. Your catalog is being evaluated, priced, and routed without a single human page visit.

How Agentic Commerce Works

A 2026 IBM Institute for Business Value study found that 45% of consumers already use AI for part of the buying journey, from interpreting reviews to completing purchases. The process moves through six stages.

Stage 1: The shopper sets the goal

The user sets a goal with constraints such as budget, brand preference, or delivery window. The AI agent interprets the request, accesses structured product data, and filters results based on price, specs, and availability. At this stage, the AI agent visits your store. The shopper does not.

Stage 2: The agent takes over

The AI agent plans multistep workflows, calls external APIs, and adjusts in real time. Low-risk purchases are fully automated. High-value or sensitive purchases require human approval before they can be completed.

Stage 3: Comparison happens without the buyer

Discovery shifts from browsing to goal-achievement. AI agents compare prices, availability, delivery times, and reviews simultaneously across multiple retailers. Whichever catalog is cleaner and more complete wins the routing.

Stage 4: Merchant-to-agent interaction

Retailers must expose APIs for product catalogs, pricing, real-time availability, and return policies. This machine-to-merchant layer enables AI agents to validate inventory and execute purchases on a user’s behalf. This is a core requirement of any working agentic commerce platform integration.

Stage 5: Payment clears without a checkout page

Purchases are completed using delegated authentication systems, including Google’s Agent Payments Protocol, Visa’s tokenized credentials, and Stripe’s integration with ChatGPT Instant Checkout. These provide transaction transparency and audit trails for fraud detection.

Stage 6: The agent manages what comes next

After purchase, AI agents handle shipment tracking, returns, and recommendations for complementary goods. The AI agent decides whether to return to your store based on the performance of your last fulfillment. For retailers, this is where loyalty and repeat-purchase behavior are restructured.

The AI agent, not the shopper, decides whether to come back to your store. That decision is based entirely on how well your last fulfillment performed.

Want to understand the infrastructure behind this? Read our guide on Ecommerce Architecture: The Ultimate Guide for 2026 to see how modern commerce systems are built to support agent-driven transactions.

Use Cases of Agentic Commerce in Retail

Agentic commerce is already reshaping how shoppers discover products and how retailers operate online stores. Instead of customers navigating every step of the buying journey, AI agents can now assist with product discovery, price comparisons, purchasing decisions, and post-purchase support.

Major research firms and payment networks such as McKinsey & Company, Salesforce, Mastercard, and JPMorgan Chase have highlighted agent-driven shopping as one of the next major shifts in digital commerce.

Retailers are exploring several practical applications of agentic commerce.

1. AI Shopping Assistants

One of the most visible agentic commerce use cases is the rise of AI shopping assistants.

These assistants help shoppers discover products through conversational interactions rather than traditional search and browsing. Customers describe what they need in plain language, and the AI agent returns product recommendations instantly.

For example, a shopper might ask:

“Find a lightweight laptop under $1,000 for school.”

The AI agent analyzes the request, searches the product catalog, compares options, and recommends the best matches.

Retailers are integrating these assistants into ecommerce websites, mobile apps, and messaging platforms. They combine product data, customer preferences, and real-time inventory to guide shoppers toward relevant products.

AI shopping assistants cut the time it takes to search large catalogs and get shoppers to the right product faster.

See how AI powers smarter product discovery: AI-Powered Product Discovery for Ecommerce and Retail

2. Autonomous Purchasing Agents

Another emerging agentic commerce use case is autonomous purchasing AI agents.

These AI agents evaluate products, compare options across merchants, and complete purchases automatically once the shopper has approved certain preferences or budgets.

For example, a consumer might instruct an AI agent to automatically reorder household items when prices fall below a specific threshold. The AI agent monitors pricing and inventory across retailers and completes the purchase when the conditions are met.

Financial institutions and payment networks are actively building on this model. These systems combine AI decision engines with secure payment credentials to ensure purchases are executed safely.

Autonomous purchasing AI agents could significantly change how consumers shop online by taking routine buying decisions off their plate entirely.

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3. AI-Driven Merchandising and Pricing

Retailers are also using AI agents to sharpen merchandising and pricing strategies.

These systems analyze customer demand, inventory levels, competitor pricing, and seasonal trends. Based on that analysis, AI agents recommend pricing adjustments, promotional offers, or product placements.

For example, AI-driven merchandising tools can identify products likely to sell quickly and push them into search results or recommendation slots. Pricing AI agents can also adjust product prices dynamically to stay competitive while protecting profit margins.

These capabilities help retailers respond fast to changing market conditions.

4. AI Agents for Inventory and Supply Chain Management

AI agents can help retailers manage inventory and supply chain operations more efficiently.

These AI agents continuously monitor product demand, stock levels, and supplier availability. When inventory drops below a defined threshold, the system automatically triggers restocking or recommends adjustments to supply orders.

For example, if demand for a product suddenly spikes, an AI agent detects the trend and recommends increasing inventory before stockouts hit.

Retailers can also use AI agents to spot supply chain disruptions and suggest alternative suppliers or fulfillment strategies.

By automating these decisions, retailers improve product availability while reducing manual operational work.

5. AI-Negotiated Pricing and Procurement

Another emerging application of agentic commerce is AI-negotiated pricing.

In this model, AI agents negotiate prices, discounts, or purchasing terms on behalf of buyers and merchants. Instead of a fixed price, the AI agent evaluates multiple offers and determines whether a deal meets the user’s preferences.

For example, a purchasing AI agent might contact several suppliers to negotiate the best price for a product. It evaluates available inventory, delivery timelines, and pricing conditions before recommending the most favorable option.

This type of automated negotiation is already being explored in procurement and marketplace environments. AI agents can represent both buyers and sellers, exchanging offers until they reach an acceptable agreement.

But experts are clear: organizations must implement governance and safeguards before letting AI agents negotiate independently. That means defining limits for pricing decisions, approval thresholds, and agentic commerce compliance requirements.

5. Voice Commerce Agents

Another fast-growing agentic commerce use case is voice-based shopping.

Voice commerce lets customers discover and purchase products through spoken conversations with AI agents. Instead of typing a search or browsing a website, shoppers simply ask for what they need.

For example, a driver could say:

“Order coffee from the nearest café.” “Find a charger compatible with my phone.” “Reserve parking near the airport.”

The AI agent interprets the request, searches for available options, and completes the transaction through integrated payment systems.

At CES 2026, SoundHound AI introduced an agentic commerce voice platform for vehicles that lets drivers interact directly with retailers, restaurants, and services via voice commands. The system connects voice AI to merchant systems, allowing purchases to happen without leaving the driving interface. 

6. AI Agents for Customer Service and Support

Another major agentic AI use case in retail is customer service automation.

AI agents can answer customer questions, track orders, process returns, and provide support across chat, messaging apps, and voice interfaces.

Instead of waiting for a human agent, customers get immediate help from AI-powered systems that understand natural language.

For example, a customer could ask:

“Where is my order?” “How do I return this item?” “Do you have this in stock?”

The AI agent retrieves the relevant information and responds instantly.

These systems reduce support workloads for retailers while dramatically speeding up customer service.

Agentic Commerce Architecture

Agentic commerce architecture is the technical framework that allows AI agents to discover products, interact with merchants, and complete purchases automatically.

In traditional ecommerce, shoppers browse websites and make decisions themselves. In agentic commerce, AI agents handle much of that process. They search product catalogs, compare options, evaluate pricing, and trigger transactions through machine-to-machine interactions.

Most modern commerce platforms rely on MACH architecture to support this. MACH stands for Microservices, API-first, Cloud-native, and Headless. This model allows commerce services to run independently while staying connected through APIs.

Because of this modular structure, AI agents can access product catalogs, pricing systems, inventory data, and checkout services directly.

Without this architecture, AI agents often rely on scraping web pages. Scraping can miss important product data and may redirect buyers to competing merchants.

The 8 Layers of Agentic Commerce Architecture

1. AI Agent Layer

This is the layer the shopper experiences directly. AI agents interpret natural language requests and translate them into structured actions. Product discovery and comparison begin before any results are surfaced to the shopper.

For example, a shopper might ask an assistant to find the best running shoes under $120. The AI agent immediately queries commerce systems to retrieve, filter, and surface relevant product data.

2. Agent Orchestration Layer

This layer coordinates how AI agents plan and execute tasks. It manages workflows such as product discovery, comparison, and checkout. If a product is unavailable, the orchestration system automatically searches for alternatives. This layer also enables multi-agent workflows, where specialized AI agents handle different parts of the shopping process.

3. Discovery and Capability Layer

Before interacting with a retailer, an AI agent must first discover that the retailer supports agent-based transactions. This layer allows AI agents to identify merchants and determine which services they provide. 

Protocols such as Model Context Protocol (MCP) allow AI agents to query a retailer’s capabilities and identify available APIs for search, checkout, and inventory access. If a retailer can’t be discovered at this layer, AI agents may never reach the merchant’s systems.

4. Merchant API Layer

This layer acts as the machine-readable interface for the commerce platform.

AI agents use structured APIs to access:

• Product catalog data
• Pricing information
• Inventory availability
• Shipping options
• Checkout services

APIs provide a reliable interface for automated transactions. Without them, the integration of agentic commerce platforms becomes unstable or inaccurate.

5. Commerce Backend Layer

The backend layer contains the core systems that power ecommerce operations. Examples include:

• Product information management (PIM)
• Order management systems (OMS)
• Pricing engines
• Inventory systems
• Customer data platforms

Because modern agentic commerce platforms rely on microservices, each service can scale independently without disrupting the entire platform.

6. Payment and Transaction Layer

This layer manages secure payments initiated by AI agents. Protocols such as the Agentic Commerce Protocol (ACP) aim to standardize programmatic checkout between AI agents, buyers, and merchants.

Payment tokens and delegated authentication allow AI agents to initiate transactions without exposing sensitive card information.

7. Security, Identity, and Compliance Layer

This layer manages secure payments initiated by AI agents. Protocols such as the Agentic Commerce Protocol (ACP) aim to standardize programmatic checkout between AI agents, buyers, and merchants.

Payment tokens and delegated authentication allow AI agents to initiate transactions without exposing sensitive card information.

8. Data, Analytics, and Feedback Layer

The final layer captures system performance and transaction data. This data supports:

• Recommendation engines
• Personalization systems
• Pricing optimization
• Post-purchase automation 

It also highlights catalog gaps. If an AI agent can’t match products to shopper requests, the signal appears here.

The Layer Many Retailers Miss

Most architecture reviews focus on APIs and backend systems. But the most overlooked layer is discovery. If a retailer’s platform can’t be discovered by AI agents, those AI agents never reach the merchant APIs. That means the retailer won’t appear in AI-driven product searches. 

Discovery infrastructure isn’t optional. It’s the entry point for participating in agentic commerce.

Building personalized experiences on top of this architecture? See how AI-driven search works in practice: Building a Personalized Shopping Experience with AI Search

Multi-Agent Workflows in Agentic Commerce

Agentic commerce often relies on multiple specialized AI agents working together. Instead of a single AI agent handling every task, several AI agents collaborate throughout the shopping workflow. Here’s a simple example of how it works:

Each AI agent communicates with the others through APIs or shared data. This coordination enables retailers to automate end-to-end complex buying processes.

Autonomous Decision-Making in Commerce

Another key component of agentic commerce architecture is automated decision engines.

These systems analyze information and determine the best outcome using rules, algorithms, or machine learning models.

They support decisions such as:

• Recommending products
• Selecting promotions
• Choosing inventory sources
• Triggering automatic reorders

For example, if a shopper asks an AI agent to find wireless headphones under a certain price, the system analyzes reviews, pricing, and availability to identify the best option within seconds.

This ability to analyze data and act fast is what allows AI agents to participate directly in modern agentic commerce workflows.

Key Challenges of Agentic Commerce

Agentic commerce is moving fast. But it comes with real challenges that retailers must address before they can benefit from it. Here are the four biggest obstacles standing between your business and a working agentic commerce setup.

1. Data Fragmentation

One of the biggest barriers to agentic commerce is fragmented data.

Retail systems often store information across multiple platforms, including product information management systems, inventory databases, pricing engines, and order management systems. When this data is inconsistent or poorly structured, AI agents struggle to interpret it correctly.

For example, product descriptions may differ across channels, inventory levels may not update in real time, or pricing data may live in multiple disconnected systems.

Because agentic commerce relies on machine-readable data, these inconsistencies can prevent AI agents from discovering products or completing transactions.

Retailers must ensure their product catalogs, inventory systems, and pricing data are structured and accessible via APIs so AI agents can interact with them reliably.

2. Security and Fraud Risks

Agentic commerce introduces new security challenges because transactions may be initiated by automated systems rather than human users.

Fraud detection systems are traditionally built to monitor human behavior, such as login patterns or browsing activity. But AI agents behave differently. They can interact with platforms much faster than any human shopper.

If businesses don’t implement proper safeguards, bad actors could create automated AI agents to manipulate pricing, exploit promotions, or generate fraudulent purchases.

Security experts recommend building an agent trust framework that verifies each AI agent’s identity, monitors its activity, and enforces transaction limits.

These safeguards help ensure AI agents operate within approved boundaries and that suspicious behavior is flagged early.

3. Governance and Agentic Commerce Compliance

Another critical challenge is governance and compliance with agentic commerce.

When AI agents can make decisions and complete transactions automatically, businesses must set clear rules about what they are allowed to do.

For example, companies may need to define limits for:

• Purchasing thresholds
• Pricing decisions
• Promotional offers
• Data access permissions

Regulatory requirements also apply when automated systems make decisions that affect consumers. In some regions, regulations require transparency about how automated decisions are made and allow customers to request a human review.

Without proper agentic commerce compliance frameworks, organizations risk compliance violations or unintended financial decisions made by AI systems acting outside their intended scope.

4. Infrastructure and Integration Complexity

Another challenge retailers face is integrating AI agents with existing commerce infrastructure.

Many older ecommerce platforms were designed for human users navigating websites, not for machine-to-machine interactions between AI agents and backend systems.

To support agentic commerce, businesses often need modern architecture built on APIs, microservices, and cloud infrastructure. This allows AI agents to access product data, inventory systems, and checkout services directly.

Retailers using legacy monolithic platforms may need significant upgrades to support AI-driven commerce workflows.

Organizations that invest in modular architectures such as MACH (Microservices, API-first, Cloud-native, Headless) are typically better positioned to integrate AI agents into their agentic commerce systems.

Despite these challenges, many retailers are already preparing their platforms for agentic commerce. The key is a structured implementation approach that strengthens data, infrastructure, and governance step by step.

Implementation Roadmap for Agentic Commerce

Retailers rarely adopt agentic commerce in a single launch. Instead, they prepare their platforms in stages so AI agents can safely interact with product data, commerce systems, and payment infrastructure.

A phased roadmap helps businesses introduce agentic commerce capabilities while reducing operational risk.

Step 1: Clean and Structure Product Data

The first step is preparing product data so AI agents can understand it.

AI agents rely on structured information such as product attributes, pricing, availability, and shipping details. If this data is inconsistent or incomplete, AI agents may fail to match products with shopper requests.

Start by improving the quality of your product catalog and ensuring all product data is machine-readable. This is the single most important foundation for any agentic commerce implementation.

Step 2: Expose Commerce Capabilities Through APIs

Once product data is structured, retailers need to make their commerce services accessible to AI agents.

AI agents interact with commerce platforms through APIs, not traditional storefront pages. Systems such as product search, pricing, inventory, and checkout must all be accessible programmatically.

Platforms built on modular architectures make this easier because each service can be accessed independently. This is a core requirement of any working agentic commerce platform integration.

Step 3: Establish Security and Governance Controls

Before enabling automated transactions, organizations must define exactly how AI agents operate within their systems.

This includes verifying AI agent identities, defining transaction limits, and monitoring automated activity. Governance frameworks also help ensure AI-driven decisions remain transparent and compliant with regulations.

Strong governance reduces the risk of fraud, misuse, and unintended automated purchases. It also keeps your agentic commerce compliance requirements on track from day one.

Step 4: Launch a Limited AI Pilot

Rather than automating the entire commerce journey at once, most retailers start with a focused pilot.

Common starting points include AI shopping assistants, product-discovery AI agents, and automated customer service tools. These pilots help organizations test their infrastructure and see how AI agents interact with existing systems.

Starting small lets teams identify data gaps and refine agentic commerce workflows before expanding further.

Step 5: Scale to Multi-Agent Commerce Workflows

After early pilots prove successful, retailers can expand into multi-agent systems.

In these environments, different AI agents handle different parts of the shopping journey. One AI agent searches for products, another compares options, and another manages checkout or post-purchase support.

These coordinated workflows allow retailers to automate more complex agentic commerce processes while remaining fully in control of their systems.

This is how agentic commerce grows from a pilot into a fully operational channel that scales for your business.

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What is the Future of Agentic Commerce?

Agentic commerce is still evolving. But early research already points to major changes in how digital retail works. Instead of customers navigating multiple websites, AI agents will increasingly manage product discovery, comparisons, and purchases on every shopper’s behalf.

Industry research shows the shift toward AI-driven shopping will affect how retailers structure their platforms, how transactions get completed, and how merchants compete for visibility in agentic commerce systems.

Here are the key trends shaping the direction of agentic commerce.

1. AI-Mediated Shopping Ecosystems

One major change will be the rise of AI-mediated shopping ecosystems. In this model, a consumer’s AI assistant serves as the primary shopping interface.

Instead of visiting multiple stores, the shopper relies on an AI agent to search product catalogs, compare options across retailers, and recommend the best purchase.

Research from McKinsey suggests that AI agents could coordinate large parts of the buying journey, including discovery, evaluation, and post-purchase management. This shifts competition away from website design toward the quality and accessibility of product data.

One major change will be the rise of AI-mediated shopping ecosystems. In this model, a consumer’s AI agent serves as the primary shopping interface.

Instead of visiting multiple stores, the shopper relies on an AI agent to search product catalogs, compare options across retailers, and recommend the best purchase.

Research from McKinsey suggests that AI agents could coordinate large parts of the buying journey, including discovery, evaluation, and post-purchase management. This shifts competition away from website design toward the quality and accessibility of your product data.

2. New Revenue Channels

Agentic commerce may also open up new revenue opportunities for retailers and platforms. As AI agents begin to control product discovery, merchants need to ensure their products appear in AI-generated recommendations.

Industry analysis from commercetools shows that structured product data, detailed attributes, and real-time availability will become critical for visibility in agentic AI commerce systems. Retailers may also see entirely new advertising and promotion models built specifically for agent-mediated marketplaces.

3. Agent-to-Agent Commerce Networks

Another key trend is the move toward machine-readable commerce systems. Traditional ecommerce platforms are designed for people browsing websites. But AI agents require structured access to product data and services to function.

Industry experts are clear: retailers need clean product catalogs, structured metadata, and accessible APIs to enable AI agents to evaluate products accurately. Platforms that deliver clean, machine-readable data will be far easier for AI agents to work with and far more likely to win the sale.

4. Machine-Readable Commerce Infrastructure

Another key trend is the move toward machine-readable commerce systems. Traditional ecommerce platforms are designed for people browsing websites. But AI agents require structured access to product data and services to function.

Industry experts are clear: retailers need clean product catalogs, structured metadata, and accessible APIs to enable AI agents to evaluate products accurately. Platforms that deliver clean, machine-readable data will be far easier for AI agents to work with and far more likely to win the sale.

Learn how AI and data analytics are already transforming retail operations: How AI & Data Analytics Are Transforming Retail Operations

5. Agent Trust and Identity Frameworks

As AI agents begin initiating transactions, security and trust frameworks are becoming more important than ever. Automated AI agents introduce new risks, including fraud and unauthorized transactions.

Security research highlights the need for systems that verify AI agent identities, define permissions, and monitor automated activity. These controls ensure AI agents operate within approved limits and that every agentic commerce transaction remains secure.

6. Open Standards for Agentic AI

The development of open standards will also shape the evolution of agentic commerce. Technology organizations are building shared frameworks that let AI agents interact across platforms and services.

For example, the Linux Foundation recently announced the creation of the Agentic AI Foundation, focused on open collaboration around agentic AI technologies. Initiatives like this aim to improve interoperability between AI agents, merchants, and digital services so the entire agentic e-commerce ecosystem can grow together. 

How RBMSoft Can Help You Implement Agentic Commerce in Your Retail Business?

Agentic commerce requires more than experimenting with AI tools. Retailers need the right data infrastructure, scalable platforms, and well-designed AI systems that work with existing commerce operations.

Rather than replacing what you already have, RBMSoft focuses on helping retailers modernize their platforms so AI agents, automation tools, and data systems can all work together reliably.

AI/ML Engineering for Retail

Retail businesses need specialized AI capabilities to support product discovery, recommendations, pricing decisions, and automation across agentic commerce workflows.

RBMSoft provides IT services for retail to help retailers design and implement these systems. This includes building recommendation models, developing intelligent search and discovery systems, and integrating AI services with ecommerce platforms and enterprise data sources.

The goal is simple: ensure your AI agents operate on structured product data, reliable APIs, and scalable infrastructure. These are the essential foundations for any working agentic commerce experience.

End-to-End Digital Transformation

Many retailers still run legacy ecommerce platforms built for human browsing. But agentic commerce depends on modern architecture that supports machine-to-machine interactions between AI agents and backend systems.

RBMSoft helps organizations modernize their commerce ecosystems through digital transformation consulting services initiatives that include:

• Migrating legacy platforms to modern architectures
• Implementing API-first commerce services
• Integrating data systems across catalog, inventory, and pricing platforms
• Enabling cloud-based and microservices-driven infrastructure

These improvements make it easier to introduce new technologies, including agentic commerce capabilities and AI agent integrations that scale with your business.

See how retailers are doing this in practice: 10 Successful Retail Digital Transformation Case Studies

Your 90-Day Path Forward

Retailers exploring agentic commerce often struggle with where to start. Instead of a full transformation all at once, a structured short-term roadmap lets you test and validate new capabilities with far less risk.

Here’s what a typical 90-day approach looks like:

• Weeks 1 to 3: Platform Assessment. Evaluate your current ecommerce architecture, product data quality, and API readiness. This tells you exactly what needs to change before AI agents can interact with your systems.
• Weeks 4 to 8: AI and Automation Pilot Launch a focused pilot, such as an AI shopping assistant, a product discovery AI agent, or an intelligent customer support workflow. Keep it small. Learn fast.
• Weeks 9 to 12: Architecture Readiness Prepare your commerce platform for scalable AI agent integrations by improving data structures, APIs, and system interoperability.

This phased approach lets retailers explore agentic AI e-commerce while keeping operational risk low. For retailers, that means one thing: you need structured data, modern APIs, and flexible architecture that AI agents can actually work with.

If your organization is exploring AI-driven retail experiences or preparing for agentic commerce, our team can assess your current architecture and identify practical next steps. Contact our team to discuss your retail innovation roadmap.

FAQs

1. What is agentic commerce?

Agentic commerce is a model where AI agents handle the shopping process on behalf of a human user. Instead of a person searching, comparing, and checking out manually, an AI agent does all of that automatically.

The AI agent searches product catalogs, evaluates options based on the shopper’s preferences and budget, and completes the purchase without the shopper visiting a single webpage. McKinsey estimates agentic commerce could direct up to $1 trillion in U.S. B2C retail revenue by 2030.

2. How do you implement agentic commerce workflows?

Implementing agentic commerce workflows happens in five steps. First, clean and structure your product data so AI agents can read it. Second, expose your commerce services through APIs for product search, pricing, inventory, and checkout. Third, set up security and governance controls to define what your AI agents are allowed to do.

Fourth, launch a small pilot with one use case such as an AI shopping assistant or product discovery AI agent. Fifth, scale to multi-agent workflows once your pilot proves successful. Each step reduces risk and builds the infrastructure agentic AI e-commerce needs to work reliably.

3. Who are the best providers for agentic commerce?

The best agentic commerce providers depend on your business size and technical needs. Leading agentic commerce platforms include commercetools for enterprise MACH architecture, Shopify for mid-market retailers, and Salesforce Commerce Cloud for businesses already in the Salesforce ecosystem.

For AI agent orchestration, platforms such as LangChain and CrewAI are widely used. Payment infrastructure providers including Stripe, Mastercard Agent Pay, and Visa’s tokenized credential framework are essential for enabling secure agent-driven transactions.

The best agentic commerce solution for your business is the one that connects cleanly to your existing commerce infrastructure through open APIs.

4. What is the best solution for agentic commerce?

The best agentic commerce solution isn’t a single product. It’s a combination of three things working together: structured product data that AI agents can read, an API-first commerce platform that AI agents can connect to, and a governance framework that controls what AI agents are allowed to do.

For most retailers, the fastest path to a working agentic commerce setup is a MACH architecture platform combined with an MCP or ACP integration layer and a secure delegated payment system. Start with clean data and reliable APIs. Everything else builds from there.

5. How does agentic commerce differ from ecommerce?

Traditional ecommerce puts the shopper in control. The person searches, filters, compares, and checks out manually. Agentic commerce shifts that work entirely to AI agents.

The AI agent searches multiple retailers at once, evaluates products against the shopper’s rules and budget, and completes the purchase automatically, often without the shopper visiting your website at all.

The biggest difference is who makes the decisions. In traditional ecommerce it’s the human. In agentic commerce it’s the AI agent acting on the human’s behalf.

6. What is the future of agentic commerce?

The future of agentic commerce is one where AI agents become the primary interface between shoppers and retailers. Instead of browsing websites, consumers will rely on personal AI agents to handle all product discovery, comparison, and purchasing.

Agent-to-agent commerce networks will allow buyer AI agents to negotiate directly with merchant AI agents on pricing, availability, and delivery. Open standards such as ACP, UCP, and MCP will make agentic AI commerce interoperable across platforms.

McKinsey projects the global agentic commerce market could reach $3 to $5 trillion by 2030. Retailers that build machine-readable infrastructure and clean product data now will be best positioned to compete in that world.

7. How does an agentic commerce agent work?

An agentic commerce agent works by following a structured workflow on the shopper’s behalf. First, the shopper sets a goal with constraints such as budget, brand preference, or delivery deadline.

The AI agent then accesses structured product data through APIs and a Model Context Protocol (MCP) server to retrieve live inventory and pricing. Next, the AI agent uses a RAG (Retrieval-Augmented Generation) system to find the most relevant products.

It reasons through the options, selects the best match, and initiates checkout through a protocol such as ACP or UCP. Finally, it logs every action in an audit trail for security and compliance. The entire process happens in seconds, without the shopper touching a keyboard.

8. How can marketplaces prepare for agentic commerce?

Marketplaces can prepare for agentic commerce by focusing on four areas. First, make sure every seller’s product data is structured, consistent, and machine-readable with complete attributes, GTINs, and real-time inventory updates.

Second, expose marketplace APIs for product search, pricing, availability, and checkout so AI agents can interact with seller catalogs programmatically. Third, implement agentic commerce platforms integration by registering with ACP for ChatGPT surfaces and deploying a UCP profile for Google AI Mode.

Fourth, build a governance framework that verifies AI agent identities and monitors automated transaction activity across the marketplace. Marketplaces that do these four things become visible to every major AI agent platform. Those that don’t risk being skipped entirely.

9. Why is agentic commerce important for businesses?

Agentic commerce is important for businesses because it changes where and how shoppers make purchasing decisions. AI agents are already driving real commercial traffic. Adobe data shows generative AI traffic to U.S. retail sites grew 4,700% year-over-year by July 2025.

AI-driven visitors spend 32% more time on site and have a 27% lower bounce rate than traditional shoppers. Businesses that structure their product data and open their APIs to AI agents gain access to a fast-growing, high-intent traffic channel.

Businesses that don’t will become invisible to AI-powered shoppers as agentic commerce becomes the default way people buy online. The agentic commerce benefits for businesses are clear: higher conversion, new revenue channels, and a structural advantage over competitors still waiting to act.

10. How Do AI-Powered Product Recommendations Work?

AI-powered product recommendations work by analyzing shopper behavior like browsing history, past purchases, and cart activity to predict what a person is most likely to buy next. Machine learning models use collaborative filtering and content-based filtering to match the right product to the right shopper in real time.

In agentic commerce, this goes further. AI agents use these same recommendation models to evaluate options across retailers and complete the purchase automatically, without the shopper lifting a finger.