Is Agentic AI Actually Helpful for E-Commerce or Just More Hype?

Another Shoptalk has come and gone. As usual, we saw one topic garnering more attention than almost every other one combined. For 2025, that topic was agentic artificial intelligence. It’s been the topic du jour ever since, and for good reason — the tech is legitimately moving forward as rapidly as I’ve ever seen. However, it’s essential to separate progress and prospect from hype.

It isn’t easy to stay ahead of trends in this rapidly evolving industrial revolution, but it would be more useful to know your strengths and your limits and actually earn the right to promote your offering's AI capabilities.

Agentic AI is the latest iteration in the AI hype cycle, but it's already a very real thing delivering real, disruptive energy, with major vendors like Anthropic and Google publishing the protocols which establish and shape everything going forward. Let’s dig into what agentic AI is and what it can do through the lens of the e-commerce industry. These aren’t the only contexts, but should provide a baseline understanding of agentic AI use cases.

Software Developers

As a software developer or really anyone working in a developer or dev-adjacent role will tell you, they constantly juggle a lot of tasks and contexts while doing their jobs. The list could truly go on for pages, but here are just a few examples:

Related story: Why Agentic AI is Taking Over in 2025 — and What This Means for Marketers

• What are the requirements and the problems I'm solving, and what are the domains involved?
• What are the capabilities, constraints and best practices of the tech stack I'm using, including the application and frameworks most proximal to userland features?
• How do I best create maintainable code?
• How do I catch bugs, regressions and other issues as I go rather than relying solely on some ritual internal/external release process?

Generative AI tools became mainstream over the past couple of years, and developers can now easily create code that works and does novel things even in complex contexts such as an e-commerce app. However, that code may not always be the best code on a long timeline. Consistency in approach is important lest you add even more context for future maintainers to consider, and generative coding tools, by default, operate within the boundaries of what they're tasked (literally "asked") to do; this is the classic, deterministic approach to software which has existed since the beginning.

These tools can speed up the development process by providing a good starting point, but the AI often still requires direct human review before going live. The agentic era promises something much more powerful: autonomous agents not bound to respond to strict requests or operate along explicit workflows, but instead acting independently in a multicontext environment which better represents the real world, almost like an omniscient companion executing all of the best practices that developers often leave "for later."

Agentic tools can build and run tests, manage integration pipelines, and write documentation. While all of this is possible with agentic AI, it still requires immense knowledge and effort to orchestrate.

Now that we’re fully stepping into the agentic era, it’s clear that the future state of the AI industrial revolution will reward people and businesses that use AI most efficiently.

E-Commerce Companies

For an e-commerce software company, especially one delivering services, most people might say an L1 support chatbot is "agentic" (in fact, this is what many businesses were presenting at Shoptalk). On its own, this is absolutely not what is meant by agentic AI, though the confusion is perhaps understandable.

So, do chatbots fit into an agentic approach at all? Of course! A proper agentic approach with chatbots could be:

• An agent building or acquiring and then deploying a chatbot.
• Connecting it to internal and external knowledge bases while ensuring proprietary details aren't disclosed.
• Automating ticket creation and delegation.
• Proactive outreach by the chatbot, recognizing customer issues and inefficiencies.
• Ensuring operational updates and uptime.
• Suggesting fixes and features to codebase or even for broader operations.
• Reporting out the right details to each stakeholder.

Consumers

For consumers, it’s easiest to imagine how agentic AI can play a role if we go over a hypothetical scenario: You’re shopping for a dress for a wedding you’re attending soon. You already have a plethora of tools at your disposal, just to name a few:

• Honey can find and automatically apply discount codes.
• Fakespot can do a deep dive on reviews and detect scams.
• Karma is an AI-powered price-tracking tool that helps consumers find the best deal.

One thing these plug-ins all have in common is that they work seamlessly in browsers. Each utility has been curated by the user, who had to discover, install and actively incorporate it into her browsing habits. This setup is honestly good enough for commodity purchases or items available from multiple vendors.

However, the current use case isn't fully suited to this set of tools because if you’re really shopping for a specific look within a budget, they don’t offer much help.

Looking at how agentic AI can be a difference-maker in this scenario, it would understand and automate this process in the most natural way possible. An agentic AI assistant can help consumers understand their desired look, timeline for purchase, and other key considerations.

It wouldn’t just be a one-time search, but an ongoing back-and-forth conversation that adapts and learns over time. It would be able to surface options within or near budget, factoring in practical details like lead times, return policies, and real-world fit, while also learning and refining suggestions based on evolving preferences.

It could even weigh the benefits of slightly more expensive local options that allow for in-person evaluation or added services like alterations. Ultimately, this whole process would be quicker than the consumer doing it independently and would create smarter, more personalized decisions.

If you’re wondering — or worried — about how agentic AI will affect your job, your tech stack, or your life, just focus on the general understanding of these contexts and know that there are real resource limitations to the technology at the moment, as the actual cost of AI calculations when providing agents access to one another is geometric rather than arithmetic. As we learn more about the cost and nature of transactions, optimization will be the next frontier. And as people living with this new agentic reality, we will ultimately benefit.

Ben Marks is director of global market development at Shopware, an e-commerce platform.