From Conversations to Agents: How eCommerce Will Be Reshaped by Agentic Commerce

In Part I of this series, we looked at how shoppers and buyers are using conversational search to find, evaluate, and buy products. We reviewed the progression from keyword to conversational searches and how companies can help prepare themselves for this new conversational, context-aware commerce experience.

In this article we're going to explore the progression from conversational AI to how agents will intermediate the process, from searching to buying on your behalf.

Natural language search is the first step, including the ability to ask questions and use conversational search to find products. The second step is understanding what role agents will play in the buying journey. Autonomous AI agents may help buyers find products, purchase them, or even orchestrate the entire journey without ever visiting your website.

Let’s review what companies must do to remain competitive in the new era of agentic commerce.

Conversational Search Is the Foundation — Not the Destination

Most companies think of conversational search as the end goal, but it’s not. The goal is to re-imagine your online presence as an agentic experience.

When a person types "I need a non-sparking muffler for a YZ250" and your website understands intent, resolves the SKU, and returns accurate results, that’s not the finish line. That’s the starting point for everything that comes next. The conversational layer you build today is the same infrastructure that powers agentic commerce tomorrow.

The reason agentic commerce is important is because an agent-to-agent (A2A) world is coming, where agents will make these requests instead of people. In an agent-first world, agents won’t work through forms or filters, and they won’t visit your website. They will work through APIs and protocols.

An AI agent analyzing your catalog on behalf of a buyer will query it the same way a human buyer would in a conversational interface — with natural language, context, and intent. If your infrastructure can't respond to that kind of query today with a human on the other end, it won’t respond to it tomorrow when an agent is doing the asking.

The companies investing in conversational search now aren't just improving buyer experience. They're building the technical foundation that makes agentic commerce possible.

From Finding to Doing: What Agents Actually Change

Conversational search answers the question: "What should I buy?" Agentic commerce answers the next three: "Can I have it? What will it cost me? And can you go ahead and order it?"

This is the fundamental difference between a conversational interface and an agent. A conversational interface informs. An agent has agency and can act.

Once a buyer, or an AI agent acting on a buyer's behalf, has identified the right product through natural language, the next logical steps are transactional: add to cart, check pre-negotiated pricing, check inventory at the right branch, confirm the purchase order workflow. An agent doesn’t just navigate those steps; it executes them.

The progression from keywords to conversations to agents looks like this:

1. Keyword search — the buyer does all the work of translating intent into machine-readable fragments
2. Intent-based search — the system understands natural language and returns relevant, ranked results
3. Conversational commerce — the system engages in dialogue, refines results, and answers follow-up questions
4. Agentic commerce — the system acts: adds to cart, applies entitlements, initiates checkout, or executes a transaction on behalf of the buyer

Each step builds on the last. You cannot skip to step four.

What Makes an Agent Different from a Chatbot

The word “agent” gets used loosely. It’s worth being precise about what separates a genuine commerce agent from a sophisticated chatbot.

A chatbot answers questions. It is reactive, stateless, and confined to conversation. Ask it what’s in stock, it tells you if it has access to inventory. Ask it to place an order, it tells you to go click the button yourself.

An agent does something different. It is:

• Catalog aware — it queries your indexed product data, not a loosely trained knowledge base
• Entitlement and rules based — it knows what this specific buyer can purchase, at what price, and under what terms
• Action capable — it can execute transactions such as add to cart, initiate checkout, query order history, and generate a quote
• Connected to systems — it integrates with ERPs, order management systems, pricing engines, and inventory platforms via APIs and emerging A2A protocols
• Context aware — it remembers what the buyer said earlier in the conversation and uses it to refine later responses

Generic AI chatbots can fake the first item on that list, but none of the rest. A commerce-native agent built on top of your actual search index, merchandising rules, and entitlement data can do all of them.

The Entitlement Problem That No Generic AI Can Solve

This is where agentic commerce diverges sharply from sophisticated AI — and where the stakes of getting it wrong are highest.

In some instances, the price a buyer sees may not be the price on the product page. Volume discounts, coupons, geographic or customer-specific catalogs, and regional inventory constraints could all play a role. A generic AI agent trained on your public product catalog without access to your entitlement engine will confidently give that buyer the wrong answer.

A poor product recommendation is an annoyance, but a wrong price or availability signal could mean a lost sale, a return, and lost loyalty.

The only way to solve this is to build agentic experiences on top of infrastructure that is already entitlement-aware, catalog-indexed, and rules-enforced — not to bolt a chatbot onto a public-facing storefront and call it an agent.

A2A Protocols and the Coming Infrastructure Shift

One of the least discussed but most consequential changes coming to eCommerce is the emergence of agent-to-agent (A2A) protocols — standardized interfaces that allow AI agents operated by buyers to communicate directly with AI agents operated by sellers.

What does this mean practically?

• Your search index is structured and attribute-rich enough to respond to natural language queries with precision
• Your pricing and entitlement data is accessible in real time, not just to your storefront but to an authenticated agent
• Your APIs and infrastructure are designed to support MCP and A2A protocols, so buyer agents can interact with your catalog programmatically
• Your merchandising rules and promotional logic apply at the agent layer, not just the human-facing storefront layer

This is why conversational search is the foundation and not the destination. The infrastructure that makes natural language search work — a robust index, attribute-aware relevance, and entitlement enforcement — is identical to the infrastructure that makes your catalog accessible to agents.

You’re not building two separate systems. You’re building one system that serves both buyers and the agents that represent them.

The Path to Conversational Agentic Commerce

The path from conversational search to full agentic commerce can be a progression or a leap. Here are the steps toward conversational agentic commerce:

Step 1: Agentic Actions in Your Conversational Search

Once conversational search is in place and buyers are using natural language to find products, the next investment is enabling actions from within that interface, such as add to cart or reorder from history. These capabilities transform your conversational search from a discovery experience into an agentic experience.

Step 2: Business Rules at the Agent Layer

Merchandising rules, promotions, entitlement pricing, and catalog restrictions should be understood by your search and discovery infrastructure so the right products are shown. That same infrastructure should allow agentic capabilities to understand and apply these business rules.

Step 3: Connect the Agent to Your Systems of Record

An agent that has access to your product catalog is useful, but the more data your agent has, the more useful it becomes. Important systems of record include your ERP for order history and account status, your fulfillment system for real-time inventory, and your help content for support.

Step 4: Prepare Your Catalog to Be Queried by External Agents

As A2A protocols mature and buyer-side agents begin querying seller catalogs directly, the companies with the most structured, attribute-rich, and machine-readable indexes will thrive. Catalog data quality, structured attributes, and API accessibility are becoming table stakes for the next phase of eCommerce.

Conclusion

Conversational search is no longer just about better search. It has become the best path toward building the foundation for agentic commerce. As you develop a roadmap for infrastructure that understands natural language, look at it from the perspective of one year from now, when agentic commerce is mature and agent-to-agent sales are standard.

Companies investing in conversational search today are the most well-prepared for the shift to agentic search and agent-intermediated transactions tomorrow.

The transition from keywords to conversations was a shift in interface. The transition from conversations to agents is a paradigm shift. Today, you sell into channels like retail, wholesale, online, and offline. Tomorrow, you’ll also need to sell into an agentic channel, where agents are buying and selling to other agents.