Agentic Commerce: Everything SEOs Need to Know About the Future of Online Shopping
The game's changing faster than most SEOs realise.
Whilst you've been perfecting your title tags and building backlinks, AI agents have quietly started making purchasing decisions on behalf of consumers. Traffic to US retail sites from GenAI browsers and chat services increased 4,700% year-over-year in July 2025, and this isn't a trend you can ignore. It's a fundamental shift in how commerce works and it demands a complete rethink of how we approach organic visibility.
Let me be direct: if your product data isn't structured for AI agents to understand, you're already losing sales. Here's everything you need to know about agentic commerce and what it means for your SEO strategy.
What Actually Is Agentic Commerce?
Agentic commerce is a new form of online and mobile shopping where AI agents "close the loop" or complete tasks for users—such as searching for items, comparing options and making purchases with limited or no manual inputs needed from that user.
Think of it this way: instead of a customer typing "running shoes under £100" into Google, browsing ten different sites, comparing options, and checking out they'll simply tell ChatGPT, Perplexity, or Alexa: "Find me the best running shoes under £100 with good arch support that can be delivered by Friday." The AI agent does all the research, comparison, and potentially even the purchasing.
Unlike predictive AI (which helps us forecast) or generative AI (which helps us create), agentic AI takes the crucial step of taking action on a person or company's behalf.
The infrastructure's already being built. PayPal launched agentic commerce services in October 2025, with strategic partnerships with Wix, Cymbio, Commerce, and Shopware allowing merchants to seamlessly enable product discovery in AI platforms including Perplexity. Mastercard and Visa have both launched payment frameworks specifically for agent-driven transactions. OpenAI's introduced the Agentic Commerce Protocol (ACP) that lets ChatGPT handle entire shopping journeys from search to checkout.
This isn't coming, it's here.
Why Traditional SEO Isn't Enough Anymore
Here's the uncomfortable truth: your meticulously crafted website, designed to capture the wandering eye of a human shopper, is now largely irrelevant. The flashy banner ads, the clever copywriting, the influencer endorsements all aimed at triggering an emotional response are rendered obsolete.
AI agents don't care about your brand story or your beautiful homepage. They care about finding the best product at the best price with the most reliable delivery. If your product data is incomplete, inaccurate, or uncompetitive, the agent simply moves on to the next option.
Users may never see your site. Instead, AI agents may retrieve product data, descriptions, or images, then directly transact. Ranking is deeper than SERP rank: it's about being prioritised by AI agents during their inference.
The shift is massive:
Discovery is moving away from search engines to AI assistants and chat interfaces
Conversion happens inside the conversation, not on your carefully optimised landing pages
Traditional metrics like CTR and bounce rate become less relevant when users never click through to your site
Your product data becomes your primary ranking signal, not your content marketing or backlink profile
From SEO to GEO: The New Optimisation Paradigm
The traditional approach of search engine optimisation (SEO) is giving way to generative experience optimisation (GEO), a strategy that enhances content for AI-driven interactions.
Some are calling it Agent Optimisation (AO), others are calling it Agentic SEO—but whatever the terminology, the principles are the same. You're no longer optimising for Google's algorithm; you're optimising for AI agents that act as buyers.
Here's what that actually means:
1. Product Data Quality Is Everything
As a prerequisite for GXO, retailers must invest in AI-ready content operations: structuring data and assets so they are authoritative, semantically rich, factual, and machine-readable.
Your product data needs to include:
Complete, accurate specifications: Material, dimensions, weight, compatibility, certifications
Structured attributes: Not "great quality", but "100% organic cotton, 300 thread count"
Use case mapping: Connect product features directly to customer needs and search scenarios
Real-time accuracy: Pricing, availability, and shipping times must be current
Rich metadata: Reviews, ratings, return policies, warranty information
Variant information: All colour, size, and configuration options are clearly structured
If your listings are vague, inconsistent, or missing key details, AI systems can't confidently interpret or surface them. What used to result in a weaker user experience or reduced visibility now carries a much greater risk: your products may not be recommended at all.
2. Transform Your Product Descriptions
Forget marketing fluff. AI agents need specification-rich, intent-based information.
Bad product description (traditional marketing copy): "Introducing our luxurious artisan coffee beans a symphony of flavour that will transform your morning ritual into an extraordinary experience."
Good product description (agent-optimised): "Single origin Arabica coffee beans, medium roast, 1kg bag. Flavour profile: chocolate notes with citrus undertones. Roasted weekly in small batches. Suitable for espresso, filter, and French press. Fair Trade certified. Ships within 24 hours."
Transform your product descriptions from marketing-focused copy to specification-rich, intent-based information that clearly maps product features to customer use cases and search scenarios.
The difference? The second description gives AI agents concrete, structured information they can match against user queries. When someone asks for "Fair Trade medium roast coffee beans delivered quickly", the agent can confidently recommend your product.
3. Implement Structured Data Feeds
The feed looks to be the most important element for SEO teams. It functions like a sitemap for agents, containing fields for product identifiers, titles, descriptions, pricing, inventory, shipping options and media.
AI agents discover your products through three methods:
Web crawls (least, reliable AI scrapes what it can find)
API access (better, direct connection to your systems)
Structured feeds (best, you control exactly what agents see)
From an SEO perspective, the richer the feed, the better. Providing optional fields such as reviews, ratings, and variant information increases the chances of a product being selected by an agent.
You need to provide feeds in JSON, CSV, TSV, or XML format that are:
Updated regularly (daily minimum, real-time ideally)
Comprehensive (include all optional fields)
Accurate (any discrepancy between feed and checkout gets you rejected)
Well-categorised (proper taxonomy helps agents understand your products)
4. Get Your Schema Markup Right
Traditional schema.org markup is more important than ever, but you need to go deeper. Implement:
Product schema with all properties filled in
Offer schema with price, availability, and delivery details
Review schema with aggregate ratings
Organisation schema for trust signals
FAQ schema for common questions about products
But don't stop at the basics. Structured data becomes the new SEO. The better your data is optimised for AI agents, the more discoverable your products become.
5. Categorisation and Taxonomy
AI systems depend on accurate product categorisation to understand what your product is, who it's for, and where it fits in the broader product ecosystem. If your category is too broad or worse, incorrect, AI engines may struggle to confidently recommend your product or match it to relevant shopper queries.
Audit your product categories:
Are they too broad? ("Clothing" is useless; "Men's Running Trainers Size 10-11" is specific)
Do they match industry standards? (Use Google's product taxonomy as a baseline)
Are they consistent across all your products?
Do they align with how people actually search?
Technical SEO for the Agentic Era
Your technical foundations need to support both human visitors and AI agents. Here's what matters:
Site Performance and Crawlability
AI agents still need to access your site efficiently. That means:
Fast page load times (agents won't wait for slow sites)
Clean, crawlable architecture
No broken links or redirect chains
Mobile-optimised (many agents operate on mobile platforms)
Proper robots.txt configuration for AI crawlers
API Access and Integration
Consider providing direct API access to your product catalogue. PayPal's agentic commerce services include a catalogue and order management offering that helps merchants seamlessly connect product data, inventory, and fulfilment with AI-driven discovery and checkout experiences.
Work with your dev team to ensure:
Product APIs return complete, structured data
Inventory levels are accurate in real-time
Pricing is current across all channels
Checkout processes work smoothly for automated agents
Trust Signals and Authority
You'll need to optimise not just for keywords but for agent relevance signals: trust signals, fulfilment speed, price competitiveness, ratings, return policies, and integration compatibility.
AI agents consider:
Merchant reputation: Reviews, ratings, business credentials
Fulfilment reliability: Shipping speed, on-time delivery record
Return policies: Clear, fair policies build trust
Payment security: Secure checkout processes
Customer service: Availability, response times, resolution rates
These aren't just nice-to-haves anymore, they're ranking factors in the agent-driven world.
Content Strategy for Agentic Commerce
Don't abandon content marketing, but adjust your approach:
Intent-Focused Content
Create content that answers the specific questions AI agents ask when researching products:
Comparison guides with structured data tables
Technical specification breakdowns
Use case scenarios mapped to product features
Buying guides with clear decision criteria
Conversational Query Optimisation
Search queries are becoming more conversational. Consumers are asking questions rather than entering search terms. They are using natural language and nuance, learning to refine further rather than start over.
Optimise for how people actually talk to AI agents:
"What's the best laptop for video editing under £1,500?"
"Find me running shoes with good arch support for overpronators"
"Which coffee maker makes the hottest coffee and has a timer?"
Your content and product data need to match these natural language patterns.
FAQ Content
Comprehensive FAQ sections become more valuable. Implement FAQ schema and answer:
Product-specific questions
Common comparison queries
Technical specifications explained simply
Purchasing and delivery questions
The Competitive Landscape Is Shifting
More than half of consumers anticipate using AI assistants for shopping by the end of 2025, according to Adobe. But this is more than just a story of adoption—customers arriving via AI agents are 10% more engaged than traditional visitors, reaching retailers further down the sales funnel with a stronger intent to purchase.
This creates both risks and opportunities:
The Risks:
Loss of direct brand interaction. When assistants mediate the buying process, customers may spend less time on branded websites, reducing opportunities for storytelling, upselling, or loyalty-building.
Increased competition: AI agents compare multiple options instantly
Price transparency: Harder to compete on anything but price if differentiation isn't clear
Platform dependency: You're relying on AI platforms to present your products fairly
The Opportunities:
Higher intent traffic: Users coming through agents are further down the funnel
Reduced acquisition costs: No paying for top-of-funnel awareness clicks
Global reach: Agents can recommend you to international customers more easily
Efficiency gains: Automated discovery means less spent on traditional advertising
What You Need to Do Right Now
Don't wait for this to become mainstream the infrastructure's already here. According to AI e-commerce data, 33% of ecommerce enterprises will include agentic AI by 2028, though today, less than 1% do. That's your window to gain a competitive advantage.
Immediate Actions (This Month):
1. Audit Your Product Data
Check every product for completeness
Identify missing attributes, specifications, and images
Look for inconsistencies across your catalogue
Test a sample of products by asking AI agents to find them
2. Implement or Improve Schema Markup
Add comprehensive Product schema
Include Review aggregates
Implement FAQ schema
Add Organisation schema for trust
3. Start Testing with AI Agents
Try finding your products using ChatGPT, Perplexity, and Gemini
See which competitors AI agents recommend
Identify gaps in your discoverability
Document what AI agents say about your products
4. Create Structured Product Feeds
Export your complete product catalogue
Include all attributes, not just basics
Format in JSON or XML
Update daily minimum
Medium-Term Actions (Next Quarter):
5. Transform Product Descriptions
Rewrite key product pages with specification-rich content
Map features to use cases
Remove marketing fluff, add concrete details
A/B test new descriptions with AI agents
6. Improve Technical Infrastructure
Provide API access to your catalogue
Ensure real time inventory accuracy
Implement automated price syncing
Test checkout processes for agent compatibility
7. Build Trust Signals
Collect and display more customer reviews
Improve shipping and fulfilment speed
Clarify return policies
Get business verification badges
8. Create Agent-Optimised Content
Develop comprehensive buying guides
Create comparison tables with structured data
Build FAQ sections for each product category
Optimise for conversational queries
Strategic Actions (This Year):
9. Consider Protocol Integration If you're on Shopify with Stripe, Shopify is the first eCommerce platform to integrate with the Agentic Commerce Protocol (ACP), giving its merchants a front-row seat for this shift. Get set up now.
For other platforms, monitor developments in ACP and similar protocols. Early adoption will give you significant visibility advantages.
10. Develop an Agentic SEO Strategy Don't treat this as a side project. It needs dedicated resources:
Assign someone to own agent optimisation
Set KPIs around agent-driven traffic and conversions
Budget for improved product data management
Plan regular testing and optimisation cycles
11. Prepare for Paid Agent Placement As third-party AI agents gain influence, retailers need to ensure discoverability by enhancing earned visibility and capitalising on emerging paid advertising opportunities.
Just like traditional SEO evolved into a mix of organic and paid, expect the same with agent optimisation. Budget accordingly.
The Tools You'll Need
To succeed in agentic commerce, you'll need:
Product Information Management (PIM) Systems Tools like Pimberly, Akeneo, or Salsify help manage product data at scale, ensuring consistency and completeness.
Feed Management Platforms Services like Feedonomics, GoDataFeed, or DataFeedWatch help optimise and distribute product feeds to multiple channels, including AI platforms.
Schema Markup Tools Structured data plugins, Google's Schema Markup Helper, or dedicated schema management platforms.
Testing and Monitoring
Regular testing with ChatGPT, Perplexity, Gemini
Monitor referral traffic from AI platforms
Track which products AI agents recommend
A/B test different product data approaches
Analytics Evolution Traditional GA4 might not capture agent-driven sessions properly. You'll need:
Server-side tracking for agent interactions
Custom events for product recommendations by agents
Conversion attribution for agent-originated sales
New KPIs focused on agent visibility
Measuring Success in the Agentic Era
Your KPIs need to evolve:
New Metrics to Track:
Agent mention frequency: How often do AI agents recommend your products?
Agent-driven traffic: Referrals from ChatGPT, Perplexity, etc.
Product data completeness score: Percentage of attributes filled
Agent conversion rate: Sales from agent-referred traffic
Competitive agent visibility: How often are you recommended vs competitors?
Traditional Metrics That Still Matter:
Overall organic traffic (humans still use Google)
Traditional SERP rankings
Backlink profile (still builds authority)
Domain authority
The goal isn't to abandon traditional SEO, it's to layer agent optimisation on top of your existing strategy.
Common Mistakes to Avoid
1. Treating This as a Future Problem The question isn't whether this transformation will happen, but how quickly and whether your eCommerce operations will be ready. Start now, not when you've lost market share.
2. Focusing Only on Technical Implementation Yes, you need structured data and feeds. But you also need great products, competitive pricing, and excellent service. AI agents recommend the best overall value, not just the best-structured data.
3. Ignoring Traditional SEO Humans will still use search engines. Traditional SEO remains relevant for discovery via web search, branding, content strategies, and human users. But to win in the agentic era, you need to layer agentic SEO over it.
4. Poor Data Quality Brands lose $15 million annually due to poor data quality and as AI agents take over product discovery and pre-selection, these costs will skyrocket. Fix your data foundations first.
5. Forgetting About Brand Just because agents mediate the purchase doesn't mean brand is dead. Agents still recommend brands they recognise and trust. Continue building brand awareness through traditional channels.
What Happens to SEO Teams?
This shift doesn't eliminate SEO, it evolves it. Agentic SEO isn't a substitute for SEO professionals. It acts more like a power-up, amplifying what your team can accomplish without replacing the strategic thinking and expertise they bring.
SEO professionals will need to:
Understand product data management
Work closely with the eCommerce and dev teams
Learn about AI agent behaviour and protocols
Master structured data implementation
Develop new measurement frameworks
Stay on top of rapidly evolving agent platforms
The role becomes more technical and data-focused, but also more strategic. You're not just chasing rankings, you're ensuring your entire product catalogue is optimised for discovery by autonomous AI systems.
The Bottom Line
Agentic commerce represents the biggest shift in online shopping since mobile. Agentic commerce is a fundamental shift that will reshape how consumers discover, search, and purchase products. In the coming years, shoppers will embrace AI agents that discover products, compare options, negotiate prices, and complete purchases to give them exactly what they want, at prices that work for them.
For SEOs, this isn't about abandoning everything you know, it's about expanding your skillset and approach. The fundamentals still matter: great products, excellent user experience, strong technical foundations, and valuable content. But how you optimise for discovery is fundamentally changing.
The winners in this new era will be those who:
Act now, not later
Invest in product data quality
Build for both humans and AI agents
Stay flexible as the technology evolves
Measure and iterate constantly
The infrastructure's being built right now. PayPal, Mastercard, Visa, OpenAI, Google, they're all creating the rails for agentic commerce. Your job is to ensure your products are discoverable when AI agents come looking.
Start with an audit. Test your products with AI agents. Fix your data. Implement structured feeds. Measure results. Iterate.
The agentic era isn't coming—it's here. The question is: will you be ready?
About the Author
Marius Badenhorst is an SEO leader with 15+ years of experience driving digital success for major brands. When he's not optimising for the next wave of search evolution, he's probably thinking about how to make it work even better.