Picture this: you’re out of groceries, so you fire up your go-to shopping app. You scroll through endless categories, manually filtering search results to find what you need. Then comes the tedious price-checking, hopping between sites to ensure you’re getting the best deal. Maybe you hold off on buying, saving the item to a wishlist or a sticky note on your phone. Eventually, when you do pull the trigger, you enter payment details, confirm delivery preferences, and wait.
That’s how shopping works today—still very much a hands-on process, still requiring human effort at every step. But what if all of this could be handled for you? What if shopping became less of a task and more of a seamless experience, driven not by your manual inputs but by artificial intelligence agents working on your behalf?
Welcome to the dawn of agentic commerce.
Why would agentic commerce matter? Well, for one, if AI agents can help users shop faster, smarter, and more efficiently, the ripple effects on e-commerce could be massive. Already, forecasts for the sector are staggering. According to Statista, global e-commerce revenue is expected to hit nearly USD 4.8 trillion this year, growing at a compound annual rate (CAGR) of 7.83% to reach close to USD 6.5 trillion by 2029.
If AI-driven shopping experiences can remove friction and elevate personalization to new levels, the potential disruption could push these forecasts even higher, sending e-commerce businesses into a state of frisson.
The idea of AI taking over shopping isn’t new. The idea of AI taking over shopping isn’t new. Personalization tools, recommendation engines, and virtual assistants have been shaping e-commerce for years. For example, Amazon’s Rufus and Alibaba’s AliMe have demonstrated how AI can refine product discovery, helping users find relevant items based on browsing patterns and contextual cues.
But OpenAI’s latest project, Operator, takes this concept further. Designed as an autonomous AI agent, Operator can navigate the web, interact with browser interfaces, and execute tasks on behalf of users. In the realm of digital commerce, it has the potential to independently analyze product data, compare options, make purchase decisions, and even complete transactions with minimal human input.
Unlike traditional AI, which relies on platform-specific APIs to exchange data, Operator can access and interpret information directly from browser screens. That means it doesn’t need deep platform integration to function—it can interact with web pages just as a human user would.
In essence, Operator and other agentic AI tools could make shopping feel effortless—becoming your digital double and handling everything from finding the best deal to clicking the buy button.
What’s agentic AI?
Agentic AI isn’t new, but Nvidia CEO Jensen Huang put the term in the spotlight when he dubbed it “a multi-trillion-dollar opportunity” at this year’s Consumer Electronics Show (CES).
While definitions vary, agentic AI is broadly understood as AI systems that don’t just respond to inputs—they can think, decide, and act autonomously.
In the context of shopping, that means AI agents won’t just suggest what to buy—they could actually complete purchases, handling the entire process from discovery to checkout.
This new model of commerce—agentic commerce—is forecast to follow a four-step cycle:
- Perceive: The AI gathers and processes data from multiple sources, such as shopping history, price trends, and user preferences.
- Reason: It analyzes data to determine the best options based on personal buying habits, budget constraints, and available promotions.
- Act: The AI executes the purchase, completing transactions in real time.
- Learn: It continuously refines its decision-making through feedback, improving accuracy over time.
For users, these steps happen invisibly, turning shopping into a one-click—or even zero-click—experience.
Platform-specific AI versus independent AI agents
Today’s AI-powered shopping tools, such as Amazon’s Rufus and Alibaba’s AliMe, operate within closed ecosystems, leveraging data available within their respective platforms.
Agentic AI could disrupt this model. Instead of being limited to a single platform or retailer, AI agents could act as third-party intelligence layers, aggregating insights across multiple sources and systems to optimize its processes.
This shift may raise major implications.
Consumers can expect more control over their purchases. Instead of being nudged toward products that platforms want to push, AI agents could prioritize user interests and secure the best deals across different retailers.
On the flip side, companies that have hitherto relied on proprietary recommendation algorithms to improve business performance may face disruption as independent AI agents reduce their influence over consumer choices.
Another possible outcome is that every app or platform could eventually be powered by its own AI agent. While still speculative, AI agents could evolve to network and coordinate with one another, enabling dynamic and complex interactions across multiple systems.
This would mark a significant update from current models. OpenAI’s Operator, for instance, is (currently) designed to interact with existing platforms through a browser interface. Future AI agents, however, could be capable of planning and synchronizing actions across robots, agents, users, and systems.
In the context of e-commerce, consider how this progression might unfold:
- AI assistants (current): These AI-powered tools assist users in product discovery, price comparisons, and recommendations within the platforms they are embedded in.
- Platform-specific AI agents: These agents help users track and make purchase decisions within a specific platform, learning from explicit user input and behavior as well as backend data collected and analyzed by the platform.
- Independent AI agents: These agents operate across multiple platforms, tracking products and making purchase decisions based on predefined user preferences. However, their capabilities may be constrained by the framework established by their provider. For example, OpenAI’s Operator interacts with browser screens but may lack access to backend data that platform-specific agents can leverage.
- Future AI agents: Every digital touchpoint—retail sites, logistics providers, payment gateways—could be fronted by AI agents capable of communicating with each other. These AI agents wouldn’t just retrieve data but could dynamically negotiate, plan, and execute transactions based on user-defined goals.
The future of commerce: Fully automated, or human in the loop?
Agentic commerce will likely streamline routine shopping, but will humans be entirely removed from the process? Not necessarily.
Everyday essentials like groceries and household supplies are well-suited for full automation. AI agents can monitor inventory, track price fluctuations, and make purchases at optimal moments—removing the need for manual oversight.
On the other hand, high-value or experience-driven purchases—such as cars, luxury goods, and fashion items—are less likely to be entirely delegated to AI. While agentic AI can streamline research, facilitate comparison shopping, and even assist in negotiations, the final decision will likely remain with the human buyer, where personal preference and tactile experience play a crucial role.
After all, would you trust an AI agent to pick your next car or luxury handbag based purely on past data and algorithmic predictions?
Risks and challenges of agentic commerce
Despite its promise, agentic AI presents serious challenges that must be addressed before it reaches mass adoption:
- Trust and reliability: AI agents will need near-perfect accuracy in decision-making. The risks of purchasing the wrong item, misunderstanding a user’s intent, or making unauthorized purchases could erode consumer trust.
- Ethical and security concerns: Delegating financial authority to an AI raises concerns about fraud, unintended purchases, and misuse of personal data. How do users retain control over autonomous systems acting on their behalf?
- Business model disruption: If AI agents optimize purely for price and efficiency, what happens to brand loyalty, advertising, and retailer-driven engagement? Businesses may have to rethink how they engage customers.
Agentic commerce is on the horizon, and OpenAI’s Operator is a significant step toward making it real. As AI agents become more capable, shopping is expected to shift from a manual, decision-heavy process to an optimized and more automatic experience.
Still, the transition won’t happen overnight. Retailers, AI developers, and regulators will need to collaborate on guardrails that ensure transparency, security, and user control.
For now, we stand at a tipping point: the shopping cart is still in your hands—but maybe not for much longer.