How is AI changing ecommerce? For the first time in history, we are witnessing a paradigm shift in digital commerce that not just redefines the venue of shopping but also assigns a new actor. We’re talking about agentic AI commerce that is slated to have a major impact soon. By 2030, the US B2C retail market alone could see up to $1 trillion in orchestrated revenue from this new shopping mode.

For retailers, this is not the time to play it by ear, because any time soon, a lion’s share of their customers will not be human users but rather AI agents. So how can one prepare for the transformation on the scale of the prior web and mobile-commerce revolutions? Our ecommerce software development company has laid out all the whys and hows of agentic AI in commerce, with clear action points ecommerce companies can start implementing right away.

What is agentic commerce?

Agentic commerce is a retail model where autonomous AI agents can discover products, negotiate prices, and execute transactions on behalf of shoppers. Ecommerce agents rely on three specific capabilities that make them a distinctive category:

• Reasoning and planning to break down a complex goal into a step-by-step checklist.
• Cross-platform action to travel across the web to complete the action.
• Tool usage by leveraging APIs to do specific actions autonomously.

Agentic AI commerce isn’t confined to online shopping only and can live within a wide range of commerce experiences, including travel, ticketing, subscriptions, and physical retail integrations.

From the interface point of view, agentic commerce tools come in two forms:

• сonsumer-facing commerce agents that transact on behalf of the customers.
• merchant-facing commerce agents designed to streamline retailer and service provider operations. 

As for the specific adoption approach, retailers can make their products and services readable to external agents, like ChatGPT or Perplexity, and also build their own branded agentic ecosystem to have an exclusive right over first-party customer data.

Core differences between agentic shopping and AI-powered commerce 

Earlier generations of retail AI, such as recommendation engines and chatbots, act mainly as a predictive layer whose reactivity is minimal if present at all. Such forms of AI assistance can guide human decision-making during the product discovery, evaluation, and purchase phases, but lack the authority to take the lead in the transaction.

While traditional AI is somewhat peripheral, agentic AI takes the central stage in the shopping journey and can trigger actions across multiple systems on the user’s behalf. Agents can search, compare, negotiate, decide, and transact within limitations set by the user.

Feature	AI-powered commerce	Agentic commerce
Control and agency	Human-first: AI assists, human controls	AI-led: AI acts autonomously with human approval on key decisions
User’s role	Active driver	Supervisor
Core scope	A set of standalone tools, with each tool being dedicated to a specific task	An end-to-end system that executes multi-step workflows from discovery to purchase
Architecture	Operates on single-model inference embedded in fixed touchpoints	Run multiple models, tools, and APIs
Primary purpose	Optimize and elevate the traditional shopping journey	Re-engineer and automate the traditional shopping journey
Example	Recommendation systems, bots	Autonomous price-negotiators, cross-retailer personal shoppers.

Right now, both operating styles exist on the ecommerce spectrum, and each of these have their time and place. But if we were to draw a clear line between the two, traditional AI is more about persuading the customer, while agentic AI is about executing for the customer. 

Agentic commerce as a new, beneficial frontier for ecommerce teams

The benefits revealed by agentic commerce tools are as unique as the concept itself, and those who adopt early get to reap the best of them and learn the fastest.

Winning in new sales channels

Traffic to US retail sites from GenAI browsers and chat services soared 4,700% year-over-year in July 2025. The engagement quality of such users is materially higher: they spend 32% more time on site, browse more pages, and bounce less often. 

If a retailer doesn’t establish a presence in these sales channels, they risk losing both traffic and decision-making influence on customers in the near future. Conversely, machine-readable and transaction-friendly products will boost AI agent visibility and drive higher conversions. 

Scaling hyper-personalized curation

Having branded commerce agents on hand allows retailers to offer the VIP concierge experience to every customer with no marginal costs. Unlike recommendation systems, agentic transactions make use of the context that goes beyond on-site behavior and includes other cross-platform sources of customer data, such as calendars, emails, wearables, and past receipts. 

So, when the shopper expresses an intent, the agent can return a purchase-ready basket – an all-in configuration that takes into account shipping windows, loyalty benefits, and substitutions. 

Going from reactive support to autonomous service 

Autonomous ecommerce agents don’t need an open ticket to spot a looming issue. Since they have the connection to the customer’s journey and the retailer’s supply chain on speed dial, they can locate friction before it impacts the customer experience. For example, if the package is canceled due to a logistics issue, the agent can proactively suggest a similar in-stock item from another store instead of sending the customer a disappointing cancel notification.

Frictionless checkout 

Agent payments protocols like UCP (Universal Commerce Protocol) and AP2 (Agent Payments Protocol) allow retailers’ systems to securely talk to multiple agents, payment providers, and platforms. Through these protocols, agents can pass along verified payment credentials, shipping information, and identity data to make purchases on behalf of the customer. This gives way to zero-click fulfillment, where customers don’t have to go through endless forms and logins to check out. 

Streamlining the back end

Standard rule-driven automation is pretty much blind to evolving context, which means that it can suffice for repetitive backend office tasks, but needs manual recalibration for out-of-the-box changes. Agentic AI is more capable when it comes to complex inventory management, pricing, and support scenarios, because it can adjust reasoning on the fly based on the changing demand, supply, and customer context.

How agentic commerce actually works

On a high level, agentic commerce is a multi-step process that bridges customer intent with the merchant’s data. But this can play out in different ways, because the specific operating pattern of ecommerce agents depends on the interaction model: agent to site, agent to agent, or orchestration agent to site. 

Below, our AI agent development team has described a step-by-step flow of the agent-to-site model, which is enabled by Google’s Unified Commerce Protocol and OpenAI’s Agentic Commerce Protocol.

1. Goal definition 

Users prompt an intermediary system, such as ChatGPT or Google AI Mode, with a shopping brief in natural language. The brief can be anything from a specific technical request (“Find me a 4K OLED monitor with a 144Hz refresh rate”) to a complex lifestyle-driven problem (“I’m going on a 2-day trip to London next week, and I realized I don’t have a waterproof rain jacket”). From that brief, the system’s LLM distills defined parameters, like the size, budget, shipping time, and necessary specs.

If the user’s prompt is too vague or broad, the agent asks a series of follow-up questions to gain a deep understanding of the user’s preferences and hard or soft constraints.

2. Autonomous discovery 

Using protocols such as the MCP (Model Context Protocol) or specialized commerce APIs, the agent heads out to retailers’ databases to query product feeds. The agent can scan dozens of machine-readable stores simultaneously. However, it doesn’t look at marketing banners but goes straight to the retailer’s real-time inventory levels, SKU data, and shipping calculators to fish out accurate information.

3. Reasoning 

The agent studies the discovered options and pits them against the non-negotiables set by the user. If no option has a 100% match with the user’s query, the agent weighs the trade-offs and curates a list of products with the most optimal specifications.

4. Execution 

Once the user approves one of the offered options, the agent closes the loop. Via API, it hands over the order to the merchant’s system, using secure payment gateways like Google Pay to finalize the agent-led transaction. From a technical standpoint, agentic payments take place within the headless checkout environment, which means that the customer doesn’t have to leave the AI interface to have their order placed.

As for the security aspect, sensitive data such as the credit card number, shipping address, and other information is tokenized.

The reality check: current limitations of commerce agents

The workflow we’ve described earlier is a textbook representation of how agentic commerce should work in theory. In practice, though, AI shopping agents face constraints that stem not so much from the technology itself, but rather from an immature ecosystem.

Agent	Capabilities	Limitations	Specs
GPT Instant Checkout	Can complete full checkout inside ChatGPT (single-item purchases) via the Agentic Commerce Protocol	Initially supports single-item transactions; multi-item carts are planned but aren’t fully rolled out; no returns in chat; US only rollout.	Needs headless commerce to operate; uses Stripe and OpenAI’s ACP
Perplexity AI shopping	Users can search, review, and buy products directly in chat via PayPal or Venmo.	Only for participating merchants/products; for single-item shopping only; US only/Pro Plan rollout.	Payments are processed through PayPal/Venmo; merchants remain the seller of record.
Microsoft Copilot	Supports checkout flows inside Copilot conversations across partners; users can complete purchases inside chat.	Merchant participation required; supported partners include PayPal and Stripe; US-only rollout.	Built on open standards and payment integrations; semi-autonomous flow.
Google Gemini/AI mode	Allows users to discover products and complete purchases directly within the Gemini app or Google Search AI Mode using integrated checkout (Google Pay)	Available initially in the U.S. only; only eligible merchants participate; requires Google Pay; limited coverage.	Powered by Unified Commerce Protocol
Shopify AI agent	Enables embedded checkout within AI agents like ChatGPT, Copilot, etc.; users can browse and complete purchases conversationally.	Early access feature; merchants must enable it; available for US stores.	Merchants see orders in Shopify admin and control data; integrates with broader AI ecosystems.

As you see from the table above, agentic commerce and agentic checkout are currently represented by several platforms in some form, but their availability is limited and conditional due to feature maturity, subscription requirements, and regional availability. 

Most importantly, only a handful of merchants have dabbled in agentic interfaces and made their products machine-readable, so the speed and magnitude of adoption are dependent both on the agent’s functionality and the merchant’s participation. This highlights where early innovators can differentiate by solving for trust, compliance, and integration at scale.

The tech foundation for AI ecommerce agents, four core layers

Retail agents can travel across different retailers without requiring custom integrations with every single shop. This capability of agentic AI tools is fuelled by a universal, interoperable technology stack that allows the participating systems to plug into each other and team up for transactional tasks. 

Function/layer	Key components	Core role
Intelligence	Personalization, Memory	Who is buying? User profile, preferences, and needs.
Planning	Dynamic Planning, Reasoning	How to buy? Strategy, step-by-step logic, and troubleshooting.
Communication	MCP, A2A	How do agents/tools negotiate? Shared context, capability exchange, secure collaboration.
Transaction and action	Computer use, Headless APIs, AP2, UCP	How does execution happen? Cart/checkout/order actions, payment initiation, and UI automation when APIs aren’t available.
Infrastructure and governance	Middleware infrastructure, orchestration framework	How are agents built and controlled? Multi-agent coordination, guardrails, monitoring, and cost management.

The reasoning layer

As the brain behind the brawn, this layer gives the agent the reasoning power to capture the essence of the prompt, keep track of the interactions, and make decisions. Technically, this layer is what allows for zero-click commerce in the first place, because the agent can carry the context, both historical and real-time, and automatically bring it into the transaction. 

The interaction and intelligence tier of the AI agent tech stack is represented by:

• Contextual AI-driven personalization – thanks to memory-driven architectures like RAG and Vector Databases, agent AI platforms can capture and infer exactly what the user needs based on real-time context. Instead of relying on static tags, the agent can store the user’s preferences as embeddings and form an identity vault for the user, which allows it to persist ground-truth parameters, such as shoe size and specific aesthetic, across different shopping sessions. 
• Dynamic planning with real-time adjustment – this capability enables agents to adapt in the midst of a multi-step workflow when something changes (e.g., the product goes out of stock) and update the outcomes in real time without going off context. This component is powered by APIs, which allow the agent to regroup without engaging the user.

The interoperability layer

Open-source protocols for programmatic commerce, such as MCP, A2A, AP2, ACP, and UCP, equip ecommerce agents with the ability to communicate with other agents and the outside world in general. Thanks to this layer, agents can all speak a common language.

Key standards shaping this layer:

• Model Context Protocol (MCP) allows AI agents and systems to exchange context, intent, and data about prior activities across models and tools. 
• Agent2Agent (A2A) allows different agents to securely exchange capabilities, status, and context through standardized protocols like JSON-RPC and HTTP. 

The transaction and action layer

As the last mile of agentic commerce, this layer provides the digital or physical ways for agents to seal the transaction on the customer’s behalf. 

Two primary ways agents take action:

• API-first commerce surfaces (headless commerce), which provides a direct, machine-to-machine interface, so that an agent can trigger checkout and inventory via API. 
• Computer use as a fallback. If a retailer doesn’t have a UCP-compliant API, agents have the option of resorting to computer-use capabilities, such as UI automation, to go through the website. 

Open standards increasingly formalize the commerce and payment steps themselves:

• Agent Payments Protocol enables semiautonomous and autonomous agents to make secure purchases on behalf of users.
• Universal Commerce Protocol (UCP)  is designed to unlock seamless commerce journeys between consumer surfaces, businesses, and payment providers. UCP is compatible with AP2.
• Agentic Commerce Protocol (ACP) for structured commerce conversations and programmatic purchase flows between buyers’ agents and businesses.

The infrastructure and governance layer

Along with other layers, the tech architecture of ecommerce agents can include a separate infrastructural overlay on which agents are built, deployed, and managed. For example, our vendor-agnostic multi-agent framework serves as a home base for all agents, keeps track of context and memory, and helps all agents work together without bumping into each other. 

On the governance side of things, multi-agent platforms also provide built-in guardrails for AI and make it easier for companies to monitor the performance of each agent, along with its interactions, performance, and token burn. 

Strategic use cases of agentic commerce with the highest ROI potential

When retail businesses decide to bring agentic ecommerce AI solutions into the fold, they need to identify the right adoption approach. Some solutions demand an innovation springboard built on the back of brand-new tech structures. Others can slot into the existing technology infrastructure, as long as it’s upgraded to be AI-native. Understanding the difference between the two is important because the winning agentic AI use cases in ecommerce are the ones that align with retailers’ tech readiness, not the ones chasing AI trends.

Customer engagement and product discovery

Use cases from this cohort are often the fastest paths to ROI for agentic commerce, because they revolve around the combination of intent, context, and conversion. In simple words, users already understand what they want, why they want it, and what constraints matter. All agents have to do is read those signals. 

As these use cases draw on existing product catalogs, customer data, and commerce workflows, they don’t require significant transformations in operating workflows from retailers. But that’s the case only when the retailer has accessible, machine-readable data at the ready. Otherwise, this application requires a data foundation setup.

Depending on the interaction model, agents can:

• Curate product sets from the brand based on the user’s intent
• Compare offerings based on multiple criteria and shortlist the most fit options
• Communicate preferences to the brand’s agent to refine and retrieve options
• Check in with other agents to fine-tune recommendations based on subtle or indirect user preferences

Clienteling and loyalty

Concierge agents are another application of agentic AI in the retail market that is picking up steam. Deploying agents into this area of impact, companies get new-era personal assistants that can:

• Act as search engines that remember customers’ past purchases, favorite brands, sizing preferences, and style choices across multiple sessions and channels.
• Proactively show up for customers with timely reminders for upcoming life events, anniversaries, or seasonal needs.
• Find personalized “just-for-you” offers for select customers based on their purchase history.
• Negotiate with the shopper’s personal agent about the trade-offs in price, style, availability, or timing.

Here, retailers bake existing clienteling right into the agent’s reasoning to make the customer experience more hyper-personalized, enabling, and predictive. However, if the retailer’s data is fragmented or locked behind legacy systems without APIs, the company will need to revamp the existing data infrastructure before deploying such agents.

Payments and fraud detection

Beyond customer relationships, merchants can make agentic commerce a part of their backend team to make transactions safer, smarter, and more autonomous for all sides. 

For example, agents can:

• Authenticate and greenlight payments on the user’s behalf according to the set limits and integrate with the merchant’s payment networks.
• Enable Know Your Agent authentication that verifies whether the user’s agent is authorized and compliant with security policies.
• Reject suspicious activity by reasoning over transactions in real time and analyzing patterns across devices, locations, and customer behaviors.
• Automate routine reconciliation and settlement activities. 

The adoption approach varies based on the merchant’s tech readiness and the specific application. Some use cases, such as semi-autonomous transaction agents, can sit on top of the existing payment rails, as long as the company has modern APIs and clean data in its stack. However, as agent autonomy increases, retailers need to build out new capabilities, including agent-aware protocols, headless checkout, and trust layers, to harvest value from the technology.

Core commerce systems

Commerce companies can also fold agents into their pillar systems, such as product catalogs, inventory, checkout, orders, and fulfillment, to automate select processes. In this case, retailers let AI do the thinking and doing on their behalf – safely, at scale, and following all the rules.

Here are some examples of what agents can do without human intervention once deployed into the core commerce software:

• Validate and complete orders based on the retailer’s business rules and inventory levels.
• Route tasks across multiple internal and partner systems to select the fastest or cheapest shipping method across multiple warehouses.
• Keep tabs on stock levels and initiate reallocation between warehouses to avoid overstock or stockouts.
• Ensure all orders, returns, and transactions comply with internal policies, taxes, and shipping regulations.

Typically, retail companies don’t need to rebuild existing systems to augment them with agentic autonomy. However, retailers still need to make sure that APIs are accessible, data is well-prepared for AI, and business rules are readable by agents,  before they invest in the agentization of core platforms.

In-store point of service

Agentic AI can also go beyond the digital realm into physical commerce to elevate the in-store experience. Brands can equip the staff with agents that can go through multiple sources of information, like inventory, customer history, and such, to serve real-time insights on the shop floor.

For example, in-store agentic AI can:

• Instantly check if the product is in stock, saving staff from the back-and-forth of searching through multiple systems.
• Suggest products based on the customer’s past purchases, preferences, and loyalty data. 
• Speed up checkout by pre-filling customer data and discounts.
• Support staff with guidance on promotions, store policies, and special requests. 
• Navigate the on-the-floor team and customers through the store to help them find the right items.

How ecommerce teams can prepare for the agentic AI in the retail market

For existing business models and tech architectures in the industry, ecommerce agents are a clear inflection point, one that pushes companies to disrupt their own processes to stay ahead. To dynamically adapt, ecommerce teams must double down on a small set of foundational readiness areas that determine whether this technological moat can be deployed safely and at scale.

Prepare data, APIs, logic, and architecture

Autonomous, multi-step reasoning places unique demands on data accessibility and system interoperability. Because of that, no matter what the retailer’s starting point is, AI agents almost always require some sort of technical regrouping. 

To build owned agentic capabilities, retailers have to get the following ducks in a row:

• Make product data both human- and machine-readable
• Standardize APIs and expose core services, such as inventory, pricing, promotions, and orders
• Transform tribal knowledge into formalized business rules 
• Tailor the architecture for the specific application (headless, composable, etc.)

Integrate open APIs to allow seamless cross-agent interactions

Open APIs allow retailers’ agents to communicate not just with the internal ecosystem but also to coordinate with third-party services, partners, and other AI agents. Without these APIs, agents have to use the manual interface, which limits their capabilities. 

Retailers don’t have to embed every open API they know. Instead, they should:

• Determine high-impact, transaction-critical services (inventory, pricing, orders, etc.)
• Select protocols based on the needs (e.g., AP2 for internal systems, MCP for marketplaces, etc.)
• Implement solid authentication (OAuth2.0, API keys), authorization, and audit trails for all API interactions. 

Apply clear guardrails to uphold trust and compliance

When companies bestow AI with execution power, they must level up their security and ethics policies accordingly. We’re talking about a comprehensive trust architecture that consists of multiple dimensions:

• Adopting identity verification for agents similar to human KYC
• Embedding human-in-the-loop controls to override agent decisions when necessary
• Setting up end-to-end encryption for all sensitive data and minimizing data sharing
• Ensuring compliance with global standards such as GDPR and ISO 27001
• Defining accountability for every stage of the autonomous transaction

Get your business ready for agentic commerce with Instinctools 

This year offers an early read on new shopping behaviors impacted by generative AI in ecommerce. One thing is clear, though: agentic commerce is a reset, and it’s only a matter of time before its widespread adoption hits home. To redesign around agent-mediated shopping, retailers must rearchitect the existing infrastructure, which, in practice, means making product data machine-readable, adopting transactional APIs, and introducing trust layers that are unprecedentedly comprehensive. 

But architecture alone is not a strategy. Retailers must also locate the right AI ecommerce use cases that tie in with their data maturity, platform, flexibility, and growth priorities. 

If you need help gearing up for the disproportionate value of agentic AI ecommerce, our AI agent development company can help you design, build, and scale production-ready AI agents tailored to your commerce infrastructure and use case.