Agentic AI represents one of the biggest shifts in how we think about digital decision-making. Unlike traditional generative AI, which focuses on creating content or predicting outcomes, agentic AI focuses on autonomy, perceiving data, reasoning over it, acting on it, and learning from the results.
Where a generative model produces an answer, an agentic model executes an action. It does something on your behalf. That distinction will reshape eCommerce far more deeply than chatbots or search improvements ever could.
The Agentic Framework
Agentic systems follow a four-step loop: Perceive, Reason, Act, and Learn.
Perceive means gathering structured and unstructured data from multiple sources. Reason involves applying logic and context to determine intent or next steps. Act means executing a decision, such as a purchase, negotiation, recommendation, or request. Learn captures feedback to improve future actions.
The better your data and contextual grounding, the better your agentic system performs. Without structure or visibility, even the smartest agent will make flawed assumptions.
How It Differs from Generative AI
Generative AI creates, while agentic AI decides. A generative model might write a product description. An agentic model could identify inventory issues, update pricing, and trigger a marketing campaign autonomously. In eCommerce, that means we are no longer asking AI to describe our store. We are asking it to manage parts of it, communicate with other agents, and eventually even transact.
| Aspect | Generative AI | Agentic AI |
|---|---|---|
| Purpose | Create content (text, images, code, etc.) | Make autonomous decisions and take action |
| Core Workflow | Predicts next token/output | Perceive → Reason → Act → Learn |
| Examples | ChatGPT writing copy, MidJourney generating images | AI agent negotiating a deal, fraud detection in real time, warehouse automation |
| User Role | Directs prompts, evaluates outputs | Sets goals/parameters, approves outcomes |
| Output | Creative artifacts | Action, transaction, or operational decision |
This distinction is critical: while generative AI augments creativity, agentic AI augments execution. This shift changes the stakes. Instead of asking AI to write a product description, we will soon ask it to negotiate a purchase, orchestrate supply chains, or run fraud detection at scale.
Steps to Go Agentic
For most eCommerce companies, the first question is where to start. Based on experience, here is a practical roadmap for preparing your business for the agentic era:
- Setup structured data. It is essential to ensure product information is accurate, available, and accessible. Structured data becomes the foundation of trust between your systems and agentic AI models.
- Ensure visibility. Provide landing pages specifically optimized for AI-driven discovery and inquiries. Agents use rich snippets, JSON-LD, and schema markup to find and interpret content.
- Optimize for speed. Make your pages fast, cacheable, and responsive. Reduced latency helps AI systems retrieve your data quickly, which will matter more as agents operate on real-time timeouts.
- Setup endpoints. Expose data through APIs or well-defined endpoints that include consistent field names and predictable structures.
- Enable payment protocols. Work with your payment processor to implement the latest agentic payment systems. Stripe currently leads with ACP and PayPal with AP2. As open protocols like AP2 (Agent Payments Protocol) emerges, expect other processors to come on board..
Each of these steps ensures your business is visible and accessible to agentic systems.
The Ethical and Legal Risk
The biggest ethical risk I see is around authorization, verifying human intent. It will be essential to ensure that eCommerce sites can accurately capture and store proof of authorization in an agentic transaction.
New store policies will have to address edge cases we have not encountered before, such as:
- I authorized my bot and then changed my mind.
- My bot bought the wrong item.
Those are not typical refund scenarios. They will need agentic-specific policies and terms of service that establish when an AI agent’s authorization becomes binding.
Chargebacks are also going to evolve. If a consumer claims an agent made an unauthorized purchase, who is responsible, the consumer, the merchant, or the AI vendor? Capturing clear digital proof of authorization will be crucial.
Understanding AP2 and ACP
There are two main architectures taking shape.
AP2 (Agent Payments Protocol) is an open-source, decentralized standard, champined by Google. With AP2, each transaction leaves an auditable trail of authorization, and mandates act as lightweight digital contracts between agents and merchants.
ACP (Agentic Commerce Protocol) is more centralized. It functions like a marketplace where the AI agent acts as a purchasing intermediary inside a controlled ecosystem. Merchants provide data feeds and approve transactions manually.
Right now, PayPal’s support of AP2 and Stripe’s integration with ACP & chatGPT are the most visible examples of agentic payment processing. It is clear that both models will play major roles. AP2’s decentralized nature promises interoperability and auditability. ACP’s marketplace approach will make onboarding simpler for early adopters.
For now, eCommerce developers should follow the early demos and prepare their data for either approach, ensuring it is well structured, verifiable, and machine readable.
Building Trust Through the Four A’s
Most researchers refer to the three A’s, Authorization, Authenticity, and Accountability. I believe we need to add a fourth, Accuracy.
If an AI agent misreads a product description or miscalculates a price, the consequences are not just technical, they are contractual. Accurate data is the foundation for agentic trust.
That means prioritizing clean product data feeds, such as those in ACP or Merchant Center, rich structured data using schema.org JSON-LD, and transparent store policies for AI-driven transactions. The more reliable your data, the more reliable your agentic transactions will be.
Vendor Agnostic but Implementation Specific
Agentic commerce is not tied to any one platform, but the implementation will differ depending on where you run your store.
Shopify: Focus on structured data optimization and Shopify’s upcoming ChatGPT integration.
Adobe Commerce / Magento Open Source: Emphasize schema, API readiness, and integrating payment gateways that support agentic protocols. Lean on open data standards and modular APIs to stay compatible with AP2 as it matures.
The key is to remain flexible. Your infrastructure should be able to integrate with whichever agentic ecosystems emerge, rather than locking into a single vendor.
Why It Matters
Agentic commerce is not a distant concept, it is already appearing in production. Etsy integrations are live, and Shopify’s rollout is on the horizon. ChatGPT now ranks listings by availability, price, quality, and instant checkout readiness.
If you are a merchant, that means your product data and fulfillment SLAs directly influence how often your products appear in agentic results. The future of commerce will be conversational and autonomous. Whether your business benefits from that future depends on how well you prepare your systems today.
The shift from recommendation to transaction is the real frontier of agentic commerce. The question is: are businesses ready?
Conclusion
Agentic AI is reshaping the way decisions are made online, and eCommerce will be one of the first industries to feel the full impact. By focusing on structured data, visibility, speed, and payment readiness, businesses can prepare to integrate seamlessly into this new agentic economy.
The foundation is clear. Build for accuracy and authorization. Be transparent in how data is exposed. Stay flexible with platforms and protocols. The businesses that prepare now will be the ones best positioned when agentic commerce becomes the default way to transact.
Generative AI has shown us what happens when machines create. Agentic AI will show us what happens when machines decide.
For businesses, especially in eCommerce, the implication is clear: prepare your systems and data not just to be read by AI, but to be acted on by AI.