Why We Built the Agentic Web (And What It Means for B2B)

Ask ChatGPT about your company. Go ahead, try it right now.

There's a good chance it gets your pricing wrong. It might hallucinate a feature you don't have. It could describe what you do using language from a competitor's website. And there's nothing you can do about it, because there's no infrastructure for giving AI agents the right answer.

That's why we built the Agentic Web.

Not a product. An open specification. A set of protocols that lets any company create AI-native endpoints so that when an AI agent asks about you, it gets a verified, real-time, first-party answer instead of a hallucination scraped from a two-year-old blog post.

This is the story of why we built it, what it enables, and why agentic commerce is about to change how B2B buying actually works.

The problem: B2B buying infrastructure is broken for AI

B2B buying changed faster than B2B selling. Buyers now research through AI assistants before they ever visit your website. They ask Claude to compare vendors. They ask Perplexity for pricing. They ask ChatGPT whether your product fits their stack.

And the answers they get are often wrong.

This isn't a minor inconvenience. It's a structural failure with three sides:

For buyers: You ask an AI assistant a direct question about a vendor ("Does Acme support Salesforce integration on the starter plan?") and get a confident answer that's completely made up. The AI doesn't know what it doesn't know. You make decisions based on fabricated information, or worse, you get the dreaded "contact sales for pricing" non-answer that wastes everyone's time.

For vendors: You've lost control of your own narrative. AI models trained on stale web data describe your product using outdated information, wrong pricing, and sometimes features from competitors. You can't correct it. You can't update it. You can't even see what's being said about you in these conversations.

For LLMs: The models themselves are stuck. They want to be helpful, but they're forced to guess from training data that's months or years old. They can't verify claims. They can't check current pricing. They can't complete a transaction even when the user wants to buy. They're answering B2B questions with the confidence of an expert and the accuracy of a rumor.

We've written about how AEO (Answer Engine Optimization) addresses the content side of this problem. But content optimization alone can't fix a missing infrastructure layer. The web simply wasn't built for agent-to-agent communication.

The insight: the web needs to be inverted

The traditional web works like this: a human opens a browser, navigates to a website, reads information, fills out a form.

But that's not how buying works anymore. An AI agent researches on behalf of a human. It queries multiple sources. It synthesizes information. It makes recommendations. The human shows up later, often with opinions already formed by what the agent told them.

We realized the web needs to be inverted. Instead of humans visiting company websites, AI agents should interact with company endpoints directly. Not by scraping web pages designed for human eyeballs, but by querying structured, machine-readable endpoints designed specifically for agent-to-agent communication.

We call this the agentic web: a layer of AI-native endpoints that sits alongside (not replaces) the traditional web. Every company exposes a machine-readable interface that any AI agent can discover, query, and transact with.

As we explored in our piece on agent-first web design, the front door of every company is shifting from a human-optimized homepage to a machine-readable endpoint. The agentic web is the infrastructure that makes that shift possible.

What we built: an open specification for AI-native endpoints

The Agentic Web specification defines how any company can expose AI-native endpoints that provide two things:

1. Verified responses: authoritative, cryptographically signed answers that AI agents can trust and cite
2. Possible actions: structured capabilities that let agents complete tasks like booking demos, requesting quotes, or starting trials

It's built entirely on open protocols:

• MCP (Model Context Protocol): Anthropic's standard for AI-tool interaction, extended for enterprise use cases
• A2A (Agent-to-Agent): Google's protocol for agent-to-agent B2B communication and task delegation
• NLWeb: Microsoft's framework for natural language web interaction
• Schema.org: the existing web standard for structured data

Discovery works through a well-known endpoint (/.well-known/mcp) that any AI agent can find. The vendor publishes a manifest describing what questions they can answer and what actions are available. An agent queries the endpoint and gets back a verified, timestamped, signed response, not a guess from training data.

This is the plumbing that makes agentic commerce possible. Without it, every agent-to-agent interaction is built on hallucinations and stale data. With it, AI agents can have structured, verified conversations with any company that exposes an endpoint.

Why it's good for everyone

Most technology shifts create winners and losers. The agentic web is unusual because it creates value for all three parties in every interaction.

For buyers: trustworthy answers, zero friction

When the agentic web works, buyers get something they've never had: AI-powered research they can actually trust.

• Verified information: No more wondering if the AI made something up. Responses come directly from the vendor, are cryptographically signed, and timestamped. You know the answer is real.
• Natural conversation: Ask questions in plain English. No navigating websites, finding the right page, or parsing marketing speak. The AI agent queries the vendor endpoint and brings back the answer.
• Skip the forms: Book demos through conversation. Your context flows naturally (company size, use case, requirements) without filling out the same fields on five different vendor websites.
• Meet the right person: Qualification happens in the conversation. Enterprise buyers get routed to enterprise reps, not generic SDRs doing round-robin. The context you've already shared determines who you talk to.

The end result: you ask your AI assistant "What's the best ASM tool for a 500-person company with SOC2 requirements?" and get actual pricing, verified compliance certifications, and a booked demo with the right AE, all in one conversation.

For vendors: control, leads, and a new channel

For B2B vendors, the agentic web solves the "AI narrative problem" while creating a new distribution channel.

• Control the narrative: You define what AI can say about you. No more hallucinated features, wrong pricing, or outdated information. Your endpoint is the source of truth.
• Gate sensitive information: Pricing, security documentation, roadmap details: release information progressively based on qualification level. Anonymous browsers get overview information. Qualified buyers get specifics.
• Structured lead capture: Every agent interaction collects qualification data (company size, role, use case), structured and flowing directly into your CRM. These aren't anonymous website visits. They're qualified conversations with context.
• Intelligent routing: Qualification determines segment. Enterprise leads go to enterprise reps. SMB leads go to self-serve. No more round-robin assignments that waste everyone's time.
• New discovery channel: AI agents become a distribution channel. When a buyer asks their AI "What's the best option for [your category]?", your endpoint makes you part of the answer with verified data, not scraped guesses.

This is what we described in The Intelligent Front Door: every touchpoint becomes a product. The agentic web makes your company's AI touchpoint as intentional and controlled as your website.

For LLMs: ground truth instead of guessing

The agentic web solves the LLM's biggest problem in B2B contexts: the gap between user expectations and available information.

• Stop hallucinating: Instead of guessing vendor details from stale training data, the model calls an API and gets the real answer. Ground responses in verified facts, not probabilistic predictions.
• No more scraping: Websites aren't designed for machines. The agentic web provides structured, machine-readable data that's easy to parse and reason about. No HTML interpretation, no JavaScript rendering, no guessing what's content vs. navigation.
• Real-time information: Training data is inherently stale. Agentic web endpoints deliver live pricing, current certifications, and today's available demo slots. The answer is always current.
• Complete transactions: Go beyond answering questions. Actually book the demo, schedule the call, request the quote. The AI agent becomes genuinely useful, not just informational.
• Universal interface: One tool (ask_company) works with any endpoint-enabled vendor. No custom integrations per company. Standardized interaction that scales.

Instead of saying "I think they might be SOC2 compliant," the model can say "They are SOC2 Type II certified, verified March 2026." That's the difference between useful and unreliable.

Agentic commerce: where this is going

The agentic web goes beyond better Q&A. It's the infrastructure layer for agentic commerce, a future where AI agents don't just research on behalf of buyers but actually transact.

Think about what becomes possible when agent-to-agent B2B communication has real infrastructure:

Autonomous vendor evaluation: A procurement AI agent queries multiple vendor endpoints, compares verified pricing and capabilities, and presents a shortlist with actual data, not synthesized marketing copy. The human decision-maker gets a brief with verified facts, not AI-generated summaries of web pages.

Progressive qualification: An agent-to-agent conversation unfolds over multiple interactions. The buyer's agent shares requirements. The vendor's endpoint responds with relevant capabilities. Qualification happens naturally, and when both sides agree on fit, a demo is booked with the right person. No forms, no SDR sequences, no wasted meetings.

Real-time deal orchestration: Pricing, contracting, and procurement move from weeks of email chains to structured agent-to-agent exchanges. The agentic commerce platform handles the back-and-forth that currently bogs down every B2B transaction.

This is where agentic commerce diverges from traditional e-commerce. E-commerce digitized the transaction. Agentic commerce digitizes the entire buying conversation (research, evaluation, qualification, negotiation, and close) through structured agent-to-agent protocols.

Why open protocols matter

We could have built this as a proprietary platform. We chose not to.

The agentic web only works if it's universal. A vendor endpoint that only works with one AI assistant is just another walled garden. The whole point is that any AI agent (Claude, ChatGPT, Gemini, custom enterprise agents) can discover and interact with any company that exposes an endpoint.

That requires open protocols. MCP provides the interaction standard. A2A enables agent-to-agent handoffs. Schema.org provides the data vocabulary. NLWeb provides the natural language layer. Together, they create an interoperable infrastructure that doesn't depend on any single AI provider.

This is the same pattern that built the original web. HTTP didn't belong to Netscape or Internet Explorer. HTML wasn't proprietary. The protocols were open, and innovation happened on top of them. The agentic web follows the same playbook.

What this means for B2B companies right now

You don't need to wait for the agentic web to be "ready." Parts of it are working today, and early movers are building advantages that compound.

Here's what matters now:

1. Audit your AI presence. Ask ChatGPT, Claude, and Perplexity about your company. What they say is what buyers see. If it's wrong, that's your baseline.
2. Structure your data for agents. Machine-readable content, Schema.org markup, FAQ architectures: these are investments that pay off immediately for AEO and compound as the agentic web matures.
3. Think about your agent-facing front door. What happens when an AI agent asks about your product? Today it's scraping. Tomorrow it should be querying a verified endpoint you control.
4. Follow the protocols. MCP, A2A, NLWeb: these are emerging standards, not hypothetical frameworks. Companies building on them now will have infrastructure in place when adoption accelerates.

The agentic web for B2B isn't a prediction. It's an architectural shift that's already underway. The companies that build for it now will own the agent-to-agent interactions that increasingly determine where buyers end up.

The bottom line

We built the agentic web because the infrastructure for AI-powered B2B buying didn't exist. LLMs were hallucinating vendor information. Buyers were making decisions based on AI-generated fiction. Vendors had no control over what AI said about them.

The specification at agentic-web.ai is our answer: an open, protocol-based infrastructure that gives every company an AI-native endpoint. Verified responses. Possible actions. Structured lead capture. Agent-to-agent communication that actually works.

Agentic commerce is coming. The question isn't whether AI agents will mediate B2B buying (they already do). The question is whether they'll do it with verified data from your endpoint, or hallucinated guesses from stale training data.

We built the infrastructure to make it the former. The specification is open. The protocols are standard. The front door is ready.

Your move.