agentic commerce – As shopping agents mature, executives are realizing the real fight isn’t building an AI front end—it’s earning the choice. Retailers are being quietly ranked today, and the ranking will expand as agents become more autonomous, making operational excellence and
Earlier this year. a room in Istanbul filled with CEOs turned to the same subject: AI-powered shopping agents. and how to win on the “agentic commerce” front end. A few weeks later, the conversation moved to a European grocery chain’s board call. Then came investors in Australia, followed by investors in North America. Most recently, it was an operating team in the U.S.
The markets differed—competitive pressures, and how far AI has traveled in each region—but the question kept landing the same way.
Everyone wanted to talk about the shopping agent. But the executives who were positioning their organizations best asked a sharper one: how do you become the retailer an agent selects?
Agentic commerce is expected to change how people shop, and also who actually makes the purchase decision. When a shopping agent builds a basket under constraints—price. availability. loyalty value. and delivery speed—it makes choices that used to belong to the customer. In that setup, the agent selects the retailers it judges most worthy of the transaction.
The uncomfortable part is that most retail organizations are not ready for that evaluation. And the reason, as the conversation returns again and again, has less to do with technology stacks than with readiness for what the agent will measure and how decisions will be governed.
Agentic commerce is real, but it is still early.
Fully autonomous shopping agents are not yet operating at scale, and most deployments today are described as narrow, assisted, and still maturing. That doesn’t mean retailers can sit back. Executives who feel they have time are right about the current state, but wrong about the window.
The signals are already there. Services like Instacart already factor availability, substitution rates, and delivery reliability into how they rank and route retailers. Retailers are being evaluated by systems they do not fully see or control.
As agentic commerce matures. the scope of that evaluation is expected to expand—and so is the autonomy of the systems conducting it. The retailers positioned to compete. in this telling. are already making foundational decisions now: how they structure data. how they govern decisions. and how they negotiate platform partnerships. Those choices compound over time. The takeaway is not framed as panic. but as sequencing—engaging with agentic commerce while it is still forming. and doing so on your own terms instead of someone else’s.
That pressure shows up most sharply as friction moves from the customer experience to the internal machinery.
Inside 84.51°. one of the largest retail data science organizations in the country. an early read on where agentic AI could create value was described as not being on the front end. but in the back office—especially in innovation pipelines and in the “unglamorous work” of connecting data to decisions at scale.
Food manufacturing is used as an example of the kind of operation an agentic system can change. By connecting customer segment data directly to product formulation decisions. the system could simultaneously optimize tradeoffs across taste. quality. shelf life. margin. and production feasibility. An agentic system. the argument goes. can hold those constraints at once. simulate scenarios. and surface options that might take human teams weeks to evaluate.
The link to agentic commerce runs through operations. Today’s fulfillment platforms already use fill rates, substitution logic, and delivery reliability as ranking signals. As agentic systems grow more capable. the evaluation is expected to expand to service commitments. loyalty economics. and how well a retailer handles exceptions. Those signals are produced inside the company—by internal operations.
If those operations are fragmented or inconsistently executed, the system routes demand elsewhere. The message is blunt: internal excellence is not a consolation prize. It is an entry requirement.
There is another risk, too—one that has played out in other sectors before.
The term is customer disintermediation. In financial services, disintermediation did not arrive in one sudden wave. Algorithmic tools inserted themselves between firms and their clients. replicated core offerings at a fraction of the cost. and made the original relationship feel unnecessary. The firms that moved early protected what automation couldn’t replicate. Those that waited lost ground that was difficult to recover.
The retail parallel, in this account, is nearly identical. The agentic platform becomes the interface. The retailer becomes the inventory source. If retailers fail to protect the data relationship and surface their full value proposition before scale creates dependency. they end up competing on price and availability against every other retailer on the same platform—“a race to the bottom.”.
Retailers. the argument continues. need visibility into how they are being ranked. why demand routes the way it does. and what signals the platform reads. The negotiation needs to cover operating rights—not just commercial terms—before dependency makes the conversation academic. The guidance is not to publish a catalog, but to publish what makes the business the best choice.
In an agentic environment, agents are expected to choose retailers that can deliver the right product at the right price to customers without friction—on the front end. Operational and fulfillment excellence will differentiate the brand—on the back end.
Under all of it sits a central belief about what agents reward.
Agentic AI rewards execution. These systems. in the described experience. expose what was already broken: fragmented data. unclear decision rights. and accountability gaps nobody wanted to formalize. When a system needs to act autonomously, those gaps become immediate blockers. The first months of deployment, then, are about fixing the enterprise.
The retailers making real progress are said to share one characteristic: the AI agenda is owned by business leaders, not the technology team. Operations, merchandising, and supply chain set the priorities. Technology enables. Value is owned by the people accountable for the outcome.
The approach is framed as disciplined investment. Anchor AI investment in operating problems with real economic weight. Build the internal foundation before negotiating an external platform deal. Negotiate that deal early and aggressively, with data rights written into the terms before scale creates dependency.
As agentic commerce matures, it is expected to route demand toward retailers that deserve it by every operational measure a system can evaluate. Retailers that treat that as a future problem are described as already behind on the problem that exists now.
Todd James is founder and CEO of Aurora Insights LLC.
agentic commerce shopping agents retail operations customer disintermediation fulfillment platforms platform partnerships AI strategy 84.51° Instacart loyalty economics data rights