B2B Agentic Commerce: How AI Agents Are Reshaping the Buyer Journey

What you’ll learn:

• What agentic commerce means for B2B — and why it's gaining momentum

• How AI agents are reshaping product discovery, purchasing decisions, and customer experience

• How to prepare your product data and tech stack to be agent-ready

• Why BigCommerce and Feedonomics are leading the charge toward autonomous B2B buying

Agentic commerce is still on the horizon, but B2B brands are already building toward it. 

Unlike B2C, where shopping often centers around inspiration or brand experience, B2B buying is about efficiency. It is focused on finding the right product, at the right price, as quickly as possible. That makes it an ideal environment for agentic systems to thrive, where tasks like product discovery, pricing validation, and order submission can be handed off to AI with minimal friction.

At its core, agentic commerce refers to AI agents acting on behalf of buyers and sellers to complete transactions. These agents will eventually communicate with one another, negotiate terms, check pricing, and complete purchases without human intervention.

But that future isn’t quite here yet.

Instead, what we’re seeing today is the foundation being laid. Companies are using AI to:

• Help customers discover products faster

• Automate routine workflows like order entry

• Personalize product recommendations in real time

In fact, 95% of ecommerce companies in the US say AI is delivering a good or very good return on investment. And B2B brands, often handling complex catalogs, negotiated pricing, and bulk orders, stand to gain even more.

The real value of agentic commerce will come when AI sales agents and purchasing agents can act autonomously. They will handle everything from discovery to checkout, factoring in customer-specific pricing, permissions, and preferences along the way.

That future may not be fully realized today, but it is already shaping how B2B brands prepare, compete, and grow.

What is agentic commerce in B2B

Agentic commerce brings AI agents into the buying and selling process. These agents do more than respond to questions. They take action based on intent.

Unlike rule-based automations that follow static workflows, agentic systems are context-aware, goal-driven, and capable of making decisions independently. They understand buyer behavior, apply contract logic, and execute multi-step workflows across systems without constant human input.

Here is the difference:

• A traditional chatbot might estimate a delivery date.

• An agentic system can check inventory, apply contract pricing, generate a quote, and prepare the order for approval in a single flow.

What sets agentic AI apart:

• Autonomy: Agents operate without manual prompts.

• Contextual intelligence: They adapt to buyer behavior and improve over time.

• Governance-aware functionality: They follow contract terms, pricing rules, and approval processes.

In B2B, where transactions often involve complex product catalogs, negotiated pricing, and multiple stakeholders, agentic commerce has significant potential. These systems transform AI from reactive to proactive. They can guide product discovery, build quotes, submit orders, and involve humans only when necessary.

The goal is not just greater efficiency. It is to evolve from automation to autonomy and create a smarter, more scalable approach to digital commerce.

How AI is changing product discovery and buying behavior

B2B buyers no longer follow a linear sales process. Many are skipping traditional steps like rep outreach or catalog browsing and going straight to AI tools like ChatGPT, Perplexity, Gemini, or Copilot. But how this shift plays out depends heavily on the type of customer.

• Longtail buyers: These smaller or midsize accounts often don’t have a dedicated rep and historically have been underserved. They are the earliest adopters of AI-driven search, using natural language queries to compare suppliers, interpret technical specs, and get instant answers. For them, agentic AI in commerce is transformative: it gives them the experience of a dedicated rep, but at scale, helping them move from discovery to purchase without human intervention.

• Core customers: Enterprise buyers still rely on human relationships and established channels like EDI or direct sales. For this group, AI isn’t about replacing reps — it’s about making them more effective. Agentic tools act as copilots, surfacing the right configurations, flagging inventory constraints, or accelerating quote-to-order processes.

This distinction matters. The longtail represents the biggest opportunity for growth through agentic commerce, as AI enables B2B brands to profitably serve a wider base of customers. Core customers, by contrast, will see efficiency gains and stronger rep relationships.

As AI becomes mainstream, buyers in both cohorts will expect agentic experiences, whether that’s self-service autonomy for longtail customers or AI-enhanced interactions for core accounts. Anything slower, less personalized, or more manual will quickly feel outdated.

How agentic commerce changes the experience for complex B2B products

For B2B companies that sell technical or configurable products, agentic commerce will offer a major upgrade to the buyer experience.

Traditionally, finding the right product meant reading spec sheets, searching through hundreds of SKUs, and waiting on responses from a sales team. That process is slow, especially when the buyer needs to make a fast, confident decision.

AI agents will help eliminate that friction. Instead of clicking through page after page, a buyer can describe what they need in natural language. The agent interprets the request, scans product data, and recommends the best-fit options instantly.

Here is how it works in practice:

• A buyer says, “I need a clamp that supports 200 pounds and works with a product of these dimensions."

• The AI agent pulls up matching options from your catalog.

• It explains why each product fits, referencing specs like materials and tolerances.

• If the product is configurable, it walks the buyer through available options step by step.

But this kind of agentic experience only works if your data is ready.

To support this level of intelligent interaction, your site needs to include:

• Structured technical specifications for every product

• Compatibility details for configurable items

• Clear documentation that AI models can access and interpret

When that information is available and properly structured, agentic discovery becomes seamless. Buyers get the answers they need. Your products get surfaced more often. And the path to purchase becomes faster and more intuitive.

AI is evolving fast — your team needs to keep up

Agentic commerce is still emerging. That means the tools, best practices, and buyer behaviors are evolving in real time.

To stay ahead, your teams need more than just technical knowledge. They need a mindset built around curiosity, adaptability, and speed.

Build a culture of agility and learning.

The businesses that will thrive in this new era are the ones that treat AI not as a one-time project, but as an ongoing process.

Encourage your teams to:

• Test new tools, even if they’re imperfect

• Learn from AI outputs and buyer behavior

• Iterate quickly on site content, FAQs, and product data

• Monitor how your brand shows up in AI search responses, and adjust accordingly

There is no definitive playbook for agentic commerce yet. That’s why agility is a competitive advantage.

Invest in data literacy and operational skills.

Your team’s ability to work with product and customer data will directly impact your AI performance.

Make sure you have people who can:

• Normalize and structure data from multiple sources

• Understand how attributes affect searchability and recommendations

• Collaborate across departments to keep information accurate and up to date

AI is data-hungry. The better your internal processes for managing product information, the better your outcomes will be.

How to prepare your business for agentic commerce

For B2B brands early in their digital transformation, the best place to start is not with advanced use cases. It’s with preparation — making sure your data, systems, and workflows are ready for the next generation of digital buying.

Agentic commerce will be powered by AI agents that need information to discover products, communicate with other agents, and complete transactions on your behalf. The more organized and accessible your data is today, the more future-ready your business will be.

Here are three key steps to start.

Optimize your product data for AI visibility on your site.

If you want to appear in AI-generated product recommendations, you need to provide the right data in the right places. That means:

• Publishing clear product titles and descriptions

• Embedding technical specifications directly on the product detail page (not buried in PDFs)

• Including customer reviews, FAQs, and use cases to provide context

The better the information on your site, the more likely AI agents will recommend your product as the best answer to a buyer’s question.

This is already driving growth for BigCommerce customers like Movora, a veterinary product manufacturer. By building detailed, educational content into their site, they appear prominently in AI search results for terms like “implants for Great Danes,” because they’ve provided the data AI tools are looking for.

If you're unsure how your site ranks in these AI-powered tools, try this:

• Ask an AI assistant what product it recommends in your category

• See which brands show up first

• Ask why it recommends those options

• Use that feedback to identify gaps on your own site

Automate PO intake with structured workflows.

Agentic commerce will eventually involve AI agents completing transactions independently, including quoting, ordering, and fulfillment.

To prepare for that, start by automating common workflows like purchase order conversion. Many B2B companies still receive POs via email or fax, which must be manually entered into an ERP or commerce platform.

A merchant agent, powered by AI, can now:

• Extract data from a purchase order

• Match it to your live product catalog

• Flag errors or missing SKUs

• Create a sales order automatically

This is not just about efficiency. It’s about building the kind of structured environment where autonomous agents will eventually operate.

Push structured product data directly into AI tools.

Publishing content on your site is important, but it’s not enough. To prepare for agentic commerce, you also need to control how that data reaches the tools your buyers are using.

That’s where Feedonomics comes in.

Feedonomics enables B2B brands to:

• Ingest data from suppliers or ERPs

• Normalize and structure product attributes

• Push optimized feeds directly to AI tools like Perplexity

This gives you direct control over how AI models interpret your products, improving discoverability, reducing errors, and accelerating buying decisions.

New growth opportunities for B2B brands

AI adoption in B2B has often started with operational efficiency. But the real opportunity (and the next competitive frontier) lies in growth, and agentic commerce will be the unlock.

By embedding agentic tools into their digital commerce experiences, B2B brands can open up new revenue streams, serve customers at scale, and reduce friction in complex buying processes.

Long-tail buyers.

Long-tail customers are where agentic AI will make the biggest impact. These buyers:

• Make up the majority of your customer base

• Contribute a small share of your revenue

• Often receive limited or no sales support

Agentic AI will enable them to get the experience of a dedicated rep, without requiring a human team.

With the right structure in place, your business can start laying the groundwork today for experiences that will:

• Guide product discovery using natural language queries

• Respond to purchase requests and suggest alternatives

• Automatically create and route orders across your backend systems

Core customers.

For core customers, agentic tools will enhance, not replace, relationships.

Enterprise buyers still expect human interaction. But agentic workflows will make those relationships more efficient, personalized, and scalable.

In the future, AI-powered merchant agents will:

• Convert POs into structured sales orders automatically

• Flag inventory issues and suggest suitable replacements

• Equip sales teams with real-time insights, tailored recommendations, and contextual data

These systems will act as intelligent copilots, helping your team move faster, answer questions more accurately, and focus more time on relationship-building.

Whether it's a long-tail buyer submitting their first order or a strategic account renewing a complex contract, agentic commerce will create smarter, more seamless buying experiences across the board.

How to build trust with customers and employees

Trust is critical in any AI-powered experience.

As B2B companies introduce agentic systems, both customers and employees will have questions. They want to know what AI is doing, how it is being used, and whether it is delivering accurate results.

The best way to earn that trust is through transparency and clarity, not just technology.

Reassure customers with human-in-the-loop support.

Customers need to feel confident that AI is helping, not replacing, their ability to get the right product or support.

To build trust:

• Be clear about when AI is being used and what it can do

• Let customers know their data is secure and not being shared to train external models

• Keep a human available for support — someone who can jump into a live chat or email thread when needed

Agentic systems work best when paired with real people. Buyers are more likely to engage when they know a trusted team is still behind the scenes.

Align internal teams around the value of AI.

Employees may worry that automation will replace their roles or devalue their expertise. Help shift that mindset by showing how AI can enhance their work, not replace it.

Focus on how AI tools:

• Free up time by automating low-value, manual tasks

• Support faster decision-making with better data

• Help sales, operations, and support teams deliver more personalized service

When teams understand how agentic commerce supports your strategic goals, and improves their own workflows, they are far more likely to adopt it enthusiastically.

How BigCommerce is preparing B2B brands for agentic commerce

The foundation is being built.

While fully autonomous buyer and seller agents are still emerging, B2B brands need to prepare today. BigCommerce is helping customers get ready for that future by making their data more discoverable, their systems more flexible, and their storefronts easier to integrate with AI tools.

Tools built for the next generation of commerce.

BigCommerce has developed a model context protocol (MCP) server that enables AI agents to:

• Access product details from your storefront

• Build shopping carts based on buyer input

• Generate checkout-ready URLs that simplify purchase flows

This gives AI tools the ability to interact with your catalog in real time, a foundational step toward agentic commerce.

A platform designed for flexibility and speed.

Beyond specific tools, the BigCommerce platform is designed for adaptability. As new AI providers emerge, you can:

• Quickly integrate third-party AI agents or chat tools

• Modify product pages and site content to align with new data standards

• Customize storefronts using our open APIs and composable architecture

Because we are API-first and partner-friendly, you are not locked into a single approach. You can adopt the best tools for your industry, and evolve your strategy as the technology matures.

Feedonomics helps you control the data layer.

Feedonomics gives you the power to transform and distribute your product data, whether to marketplaces, ad platforms, or, increasingly, AI search engines.

With direct integrations into platforms like Perplexity, and more coming soon, Feedonomics ensures your data is structured, optimized, and ready for the next generation of discovery.

In short, we are not just talking about the future of agentic commerce. We are building it.

The final word

Agentic commerce may not be mainstream yet, but the foundation is already being laid, and B2B brands are leading the way.

From AI-powered product discovery to automated purchase order processing, the tools available today are reshaping how buyers engage and how sellers operate. The key is preparation. That means:

• Structuring product data so it is usable by AI

• Embedding technical specs, reviews, and FAQs directly into PDPs

• Using platforms like Feedonomics to control how that data is distributed

The next phase will move beyond smarter discovery to fully autonomous transactions — with AI agents on both sides of the buying process. That future is coming quickly, and the brands that start now will be the ones best positioned to lead.

B2B commerce is evolving. The only question is whether your business is ready to evolve with it.

FAQs about B2B agentic commerce

What is B2B agentic commerce?

B2B agentic commerce refers to the use of autonomous AI agents that act on behalf of buyers and sellers to streamline complex purchasing decisions. These AI-powered agents go beyond traditional chatbots or bots — they can analyze real-time data, understand product catalogs, and initiate actions across systems.

In a B2B digital commerce environment, these agents can assist with everything from product discovery to order orchestration, improving both operational efficiency and the overall customer experience. As part of a larger commerce platform ecosystem, agentic commerce is helping retailers, distributors, and manufacturers evolve how they operate and serve customers.

How is an AI agent different from a rules‑based automation or chatbot? 

Rules-based chatbots and automations typically follow predefined scripts. They respond to keywords and execute limited functions, like retrieving a shipping status or offering a basic FAQ.

Agentic AI, on the other hand, uses artificial intelligence and algorithms to adapt dynamically. These autonomous AI agents can:

• Learn from prior interactions

• Pull from metadata, PIM systems, and real-time data

• Orchestrate actions across platforms like Salesforce, your ERP, and even marketplaces like Amazon

They understand intent and context, making them far more useful across complex digital commerce scenarios, especially in B2B ecommerce environments where logic is not always linear.

What data do I need before piloting an agent? 

High-quality, structured data is essential for any agentic AI implementation.

Start with:

• Accurate product catalogs with enriched metadata

• Clean, normalized product information from your PIM or ERP

• Customer information, such as previous order history and behavior across touchpoints

You’ll also want to evaluate data quality and ensure your systems can provide consistent real-time data. This helps your agent make smarter purchasing decisions, respond with relevant recommendations, and operate seamlessly across your sales channels and online stores.

How do I ensure safety and approvals with autonomous agents?

Even the most advanced generative AI and other AI capabilities should not run unchecked.

In B2B workflows, it is critical to set guardrails for AI agents to protect against errors or unauthorized actions. This includes:

• Role-based permissions and approval hierarchies

• Integration with systems like Salesforce for human signoff where needed

• Monitoring tools to track behavior across the supply chain and digital touchpoints

Most commerce platforms, including BigCommerce, are evolving to support AI orchestration in ways that ensure safety, accountability, and transparency.

By putting the right controls in place, your business can safely explore the power of agentic commerce without compromising trust or compliance.