The Agentic Commerce Revolution

The way consumers shop online is undergoing a fundamental shift. For the past two decades, ecommerce has been defined by search bars, category filters, and endless scrolling through product grids. We call this deterministic discovery: the shopper specifies filters, the site returns a finite set of results, and the user makes a choice.

Today, we are entering the era of Agentic Commerce.

Instead of manually drilling through catalog pages, more and more shoppers will start with a single natural-language request to an AI agent and let it do the work. For ecommerce teams, this is not a minor UX tweak. It is a channel shift on the order of “search → marketplaces” or “desktop → mobile.”

This article explains what Agentic Commerce is, why it is happening now, and what practical steps you can take to get your store ready.

What is Agentic Commerce?

Agentic Commerce refers to shopping experiences mediated by AI agents—like ChatGPT, Claude, or Gemini. Instead of visiting a website and doing the work of filtering themselves, consumers simply tell an agent what they need:

“I need a pair of running shoes for marathon training, under $150, that ships by Friday.”
“I’m looking for a carry-on suitcase that fits in United’s overhead bins.”
“I want a gift for my sister who loves baking and lives in Berlin. Budget is €60.”

The agent then acts as a concierge:

1. Interprets the request and turns it into a set of constraints.
2. Searches across multiple merchants and sources of product data.
3. Reads and compares product attributes, reviews, and policies.
4. Presents a small set of recommended options.
5. In some flows, completes the purchase on the shopper’s behalf.

From the shopper’s perspective, the agent feels like a trusted assistant who “knows the whole internet” and can make smart tradeoffs. From the merchant’s perspective, the agent is a new routing layer that decides which products and which brands get surfaced in response to these high-intent prompts.

Importantly, Agentic Commerce is not limited to chat UIs. It will show up in:

• Voice interfaces (“Ask” in iOS, Android assistants, in-car systems)
• Embedded assistants inside marketplaces and comparison sites
• Merchant-owned experiences (AI-driven product finders, concierge flows)
• Browser extensions that help users shop across tabs and sites

Anytime an AI agent is sitting between a buyer and a catalog, you are in Agentic Commerce territory.

Why This is Happening Now

Several trends are converging to make Agentic Commerce viable:

1. Large language models are good enough

Modern models can parse messy, real-world requests and map them to products with surprisingly high accuracy. They can read long product descriptions, Q&A, and reviews and synthesize key attributes:

• “Good for wide feet”
• “Runs small; size up”
• “Materials suitable for sensitive skin”

This lets them act as “soft filters” that go far beyond simple faceted search.

2. Checkout and payments are being standardized

Initiatives like the Agentic Commerce Protocol (ACP) define how agents should talk to merchant systems:

• How to represent a cart and line items
• How to calculate tax and shipping
• How to pass a payment token securely
• How to track order status and returns

As more PSPs and platforms implement these standards, agents can reliably complete purchases instead of just throwing users over the wall to a website.

3. Consumers are already using agents for research

Even before formal “buy” buttons existed, users were asking ChatGPT and other models:

• “What are the best trail running shoes under $150?”
• “What are good entry-level espresso machines?”
• “Which protein powders don’t taste like chalk?”

Once people get used to doing the research in one place, the pressure to complete the transaction there is inevitable.

How Agentic Commerce Changes the Funnel

In traditional ecommerce, the funnel looks roughly like:

Ad / Search → Landing page → Category page → Product page → Cart → Checkout

In Agentic Commerce, the funnel compresses:

Agent prompt → Shortlist of products → Embedded checkout

Several important things change:

You lose control of the “shelf”

On your own site, you decide which products to feature, how to lay out categories, and which filters to show. In an agentic flow, the agent owns the shelf. It chooses which three products to show the user and in what order.

Relevance and trust matter more than branding

The user sees very little of your visual identity. What matters is:

• Are your products eligible to be shown?
• Does your data make them look like a good fit for the query?
• Does your reputation (reviews, policies) give the agent confidence?

If the agent doesn’t trust your data or finds a better-structured alternative, your brand may never appear in the conversation.

The checkout becomes multi-merchant by default

An agent can trivially build a cart that pulls items from three different merchants, each fulfilled separately. That is powerful for consumers but raises the bar for merchants: your systems need to accept orders that may originate from many agent surfaces, not just your own front-end.

Why Product Feeds Matter More Than Ever

In this new world, your website’s visual design matters less than your data’s structural integrity. AI agents don’t “see” your homepage banner; they read your structured data.

They care about things like:

• title, description, brand, category
• price, currency, availability, inventory_quantity
• size, color, material, age_group
• shipping options, return policy, warranty
• review counts, average rating, common pros/cons

If your product feed is messy, incomplete, or outdated, three bad things happen:

1. You get filtered out.
   If a required attribute is missing or malformed (e.g., no GTIN, broken URL), the agent may simply ignore the product. In some setups, the entire feed is dropped from eligibility.

2. The agent hallucinates.
   If the feed omits key details (e.g., materials, fit, compatible devices), the model may fill in the blanks using similar products or guesses. That leads to disappointed customers and returns.

3. You look worse than competitors.
   If another brand has richer, cleaner data—clear sizing, accurate shipping estimates, well-structured attributes—the agent has every reason to prefer their products over yours.

In deterministic discovery, a mediocre feed might still muddle through because users were willing to click around and refine. In Agentic Commerce, the agent is aggressively narrowing to a few options. If your data doesn’t clear the bar, you don’t even get a chance to compete.

What “Agent-Ready” Data Looks Like

To participate in Agentic Commerce, you need more than a CSV dump. An “agent-ready” feed has a few key characteristics:

1. Complete for the domain

Every product that should be eligible in agentic flows needs all required fields:

• Stable ID and canonical URL
• Well-structured title and description
• Brand, category, and attributes relevant to your vertical
• Price, currency, and tax behavior
• Stock status and inventory quantity
• Shipping methods, regions, and SLAs
• Returns and warranty details

It is better to have fewer products with perfect data than a huge catalog with half-filled records.

2. Normalized across the catalog

Agents perform better when fields are consistent:

• Colors mapped to a controlled list ("navy" → "blue")
• Materials split into arrays instead of a single free-text string
• Sizes mapped to regional systems (US / EU / UK)
• Categories aligned to a taxonomy, not arbitrary labels

Normalization makes it much easier for an agent to compare products and filter by constraints.

3. Fresh and trustworthy

If the feed says an item is in stock and the checkout fails, the agent learns that your data is unreliable. That may reduce your exposure in future recommendations.

You want:

• Inventory updates at least every few minutes
• Prices updated as soon as promotions change
• Discontinued items removed promptly
• Policies kept in sync with your real terms

Trust is cumulative—both for users and for agents.

Practical Steps to Prepare Your Store

You don’t need to overhaul your entire stack to get ready for Agentic Commerce. But you do need a plan. Here’s a pragmatic starting point.

1. Inventory your data

Gather your current product feed(s):

• Whatever you send to Google Merchant Center
• Any exports you use for marketplaces or comparison engines
• Internal catalog exports from your ecommerce platform

Audit them for:

• Missing required fields
• Inconsistent attributes (colors, sizes, categories)
• Broken or outdated links
• Misaligned inventory or pricing

2. Decide what should be eligible

Not every SKU needs to be in agentic flows. You might choose to:

• Exclude products with complex configuration steps
• Focus on high-margin, low-return items
• Prioritize core categories where you’re truly competitive

A smaller, cleaner subset is a good first target.

3. Normalize and enrich

Introduce simple rules:

• Standardize colors, materials, and size formats
• Ensure every product has at least one high-quality image
• Add key attributes that agents are likely to care about (e.g., “waterproof,” “carry-on compliant,” “vegan leather”)

If you have reviews and Q&A, consider summarizing common themes into structured fields the agent can read.

4. Establish an update cadence

Figure out how you’ll keep the feed fresh:

• Change-data-capture from your ecommerce platform
• Webhooks for inventory and price changes
• Nightly full refresh with hourly incremental updates

The exact schedule depends on your business, but “once a week manual export” is not going to cut it.

Where Pesto Fits

Doing all of this once is work. Keeping it correct and up to date as schemas, platforms, and ACP requirements evolve is an ongoing operational burden.

This is where Pesto comes in.

Pesto:

• Connects to your existing platform (Shopify alternatives included)
• Cleans and normalizes your product data
• Maps it to OpenAI’s product feed and checkout spec
• Hosts a clean, always-on feed for agents to consume
• Monitors compliance and alerts you when products are dropped or auto-fixed

In other words, Pesto sits in the middle of the Agentic Commerce stack as the feed and eligibility layer between “Your Store” and AI agents like ChatGPT.

Whether you use Pesto or not, the strategic point remains the same:

Agentic Commerce is coming. The brands that treat their data like a first-class product and get agent-ready early will own the first wave of AI-driven revenue.

Now is the time to make sure your catalog is one of the ones the agents actually see.