Let’s Clear the Air Surrounding Agentic Solutions at Shoptalk Spring 2026

If there was one word that defined Shoptalk Spring this year, it was agentic.

The volume was turned all the way up, literally, in the meetings hall, and figuratively, across every conversation, pitch, and panel. Everyone had an “agentic” solution to show. Every booth, every deck, every demo seemed to orbit around the same promise: “we’re the ones who will analyze, automate, and improve your retail business.”

It hit home for me during a 1:1 meeting that opened with a blunt question:

“What AI agent are you pitching me?”

My response: “Whoa. Nice to meet you too. Now, let me tell you about Product Agent.”

The industry has moved so quickly from curiosity to commercialization that “agent” has become the default wrapper for nearly every product narrative. But here’s the problem: when everything is agentic, nothing is differentiated.

In this blog, I want to clear the air on what exactly the market is offering by way of AI brand visibility and catalog optimization. There’s a thick fog surrounding what’s available out there, and I’ll give you a useful way to frame solutions so it’s easier to understand where they can help.

Let’s go!

Big announcements accelerate agentic commerce discovery

Let’s cover a few big announcements surrounding Shoptalk Spring and what they mean for your e-commerce strategy.

1. Agent-ready storefronts become Shopify’s default

Shopify made it clear that agentic commerce isn’t a feature; it’s becoming infrastructure. Their push toward agent-ready storefronts means product catalogs are now expected to be machine-readable, structured, and accessible to AI agents by default.

Shopify is positioning itself as the source-of-truth layer in agentic commerce. If agents are the new browsers, Shopify wants to be the cleanest, most reliable backend that those agents pull from.

The implication for brands is significant:

• Your competitive advantage won’t just be merchandising; it’ll be data quality and structure.
• The “storefront” is no longer your site; it’s how your catalog renders inside someone else’s agent.

2. OpenAI moves to the decision layer

Despite the recent news that “Instant Checkout” is on hold for the moment, OpenAI showcased an evolved ChatGPT shopping experience, including real retailer integrations and conversational product discovery. But the more important signal was strategic: they’re positioning themselves as the decision-making layer, not the checkout layer.

By stepping back from native checkout and focusing on helping users evaluate, compare, and choose products, OpenAI is defining where value accrues in agentic commerce: influence the decision, not who processes the payment.

OpenAI is going after the highest-leverage moment in commerce: intent resolution.

For brands, this creates a new reality:

• You’re no longer just competing for search rank; you’re competing to be selected by an AI system.
• Differentiation shifts from branding and UX to clarity, completeness, and contextual richness of your product data.

3. Google is closing the loop with agent-based transactions

Google took the opposite angle. Picking up from their NRF 2026 announcement of Universal Commerce Protocol (UCP) earlier this year, Google has continued to lean into end-to-end agentic transactions through Gemini, including a big announced partnership with GAP that enables purchases directly within the AI chat experience.

Where OpenAI is optimizing for decisions, Google is pushing to own the full funnel, from discovery through conversion, loyalty, and customer service.

Google is attempting to collapse the traditional funnel entirely.

From: Search > Site > Browse > Cart > Checkout

To: Prompt > Agent > Purchase

This is a direct evolution of Google’s long-standing goal to reduce friction between intent and transaction.

For brands, this raises a more existential question:

• If the transaction happens inside the agent, what role does your owned experience play?
• And more importantly, how do you maintain visibility and control when the interface is abstracted away?

Clearing the air: what to make of GEO and AEO agent solutions

The common thread across all three announcements is clear: your product data is no longer just operational. It’s now your primary distribution strategy. And there were a lot of vendors pitching their answer for improving brand visibility and catalog accessibility on agentic commerce channels.

Broadly, those approaches fall into two camps.

GEO for brand visibility

On one side, you have solutions focused on what’s often framed as generative engine optimization (GEO). They promote the idea that visibility in AI experiences comes from increasing your brand’s presence across the open web. The pitch is straightforward:

• Generate large volumes of content.
• Expand brand mentions, citations, and topical coverage.
• Influence how models “learn” and reference your brand.

In theory, more content equals more surface area, which leads to more opportunities for an AI system to recognize and include your brand in responses. It’s a familiar playbook, one that echoes the early days of SEO, now adapted for generative interfaces.

But there’s a limitation to this approach.

Agentic commerce isn’t just about being mentioned—it’s about being selected. And selection requires a different level of understanding.

When an agent is tasked with actually helping a user choose a product, comparing options, filtering based on constraints, and reasoning through trade-offs, it doesn’t rely on loosely connected content across the web. It relies primarily on access to structured, precise, and context-rich product data.

AEO for data accessibility

This is where the second camp, answer engine optimization (AEO), takes a fundamentally different approach. This is where Product Agent lands.

Instead of trying to influence the outer layer of visibility, it focuses on improving accessibility to the underlying data layer:

• Structuring pages, backend schema, robots.txt to welcome AI search agents.
• Enriching product attributes beyond the basics.
• Adding context that helps agents understand use cases, compatibility, and intent.
• Ensuring consistency and accuracy so outputs can be trusted.

Our own studies have found that content depth on each SKU is 4x more likely to get cited by AI shopping agents in answers than brand mentions.

In an agent-driven flow, the question isn’t just, “Does the model know your brand?” It’s, “Can the model confidently interpret your product well enough to recommend it?”

That’s a much higher bar.

It’s why simply generating more content, more pages, more mentions, and more citations doesn’t solve the core problem. If the underlying product data is thin, inconsistent, or ambiguous, the agent has nothing reliable to reason over.

While GEO approaches may help you show up at the edges, AEO determines whether you show up at the moment of decision.

In a world where agents are collapsing discovery, evaluation, and recommendation into a single step, that distinction matters. A lot.

Product data is your IP in multi-surface agentic commerce—invest in it with Product Agent

My takeaway from Shoptalk Spring is clear: Brands need to view their catalog as IP as new surfaces for product data expand and channels evolve.

Think about it this way: Agentic commerce isn’t about the agent itself. It’s about whether your products can be understood, selected, and surfaced by an agent in the first place.

That shift in framing is critical, especially for solutions targeting visibility and catalog optimization. In this new paradigm, the bottleneck isn’t access to agents. It’s the quality, structure, and context of the data that agents rely on to make decisions.

The real question isn’t: “Do you have an agent strategy?”

It’s: “Are you legible to the agents that are already reshaping how commerce works?”

Find out if your catalog data is accessible and ready today for this new multi-surface, agentic commerce era by taking our free AI Search Readiness assessment. Want to see Product Agent in action? Schedule a demo with our team.

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