Introduction
Something fundamental is shifting in how people interact with the internet. AI agents — software that doesn't just answer questions but takes actions autonomously — are starting to browse websites, compare products, check reviews, and in some cases, complete purchases on behalf of users. This isn't a theoretical future. OpenAI's Operator, Google's Agentspace, and Anthropic's Claude are already deploying agent capabilities in production.
For businesses, the question is no longer “Will AI agents affect how customers find me?” but “Can AI agents interact with my business at all?”
The answer depends on a set of emerging protocols — standardized ways for AI agents to connect with tools, websites, other agents, and commerce systems. Backlinko's deep-dive on agentic AI protocols provides an excellent technical overview of the six protocols forming the foundation of this new stack. We've also covered how AI agents are evolving beyond chatbots in a previous post.
This article breaks down the six protocols every business should understand, explains what they do in practical terms, and provides a clear action plan for getting your website and digital presence ready.
Key Takeaways
- Six protocols form the agentic AI stack: MCP, A2A, NLWeb, WebMCP, ACP, and UCP
- MCP (Model Context Protocol) already has over 10,000 servers operational and is the de facto standard for agent-to-tool connectivity
- Google and Shopify's UCP (Universal Commerce Protocol) launched with 20+ partners including Target, Walmart, and Wayfair
- AI agents evaluate businesses through multiple specialist checkpoints — data consistency across sources is critical
- Businesses don't need to implement all six protocols now, but understanding the stack helps you prioritize
- Structured data, clean APIs, and machine-readable content are the foundation that supports every protocol
How Do AI Agents Actually Interact with Businesses?
Before diving into individual protocols, it helps to understand the end-to-end workflow. When a user asks an AI agent to “find the best HVAC company near me” or “compare standing desks under $500,” the agent follows a multi-step process that touches several protocol layers:
- MCP activates — The agent connects to external tools and data sources (search APIs, review platforms, product databases) through the Model Context Protocol, which serves as its universal adapter.
- A2A coordinates — The agent may delegate subtasks to specialist agents. A pricing agent checks costs, a review agent evaluates reputation, and a logistics agent confirms availability — all communicating through Agent-to-Agent protocol.
- NLWeb queries — The agent visits candidate business websites and asks natural language questions (“What are your service hours?” “Do you offer emergency appointments?”) through NLWeb, receiving structured answers instead of parsing HTML.
- WebMCP declares capabilities — Websites tell the agent what actions are available (book appointment, request quote, check inventory) through machine-readable capability declarations.
- ACP or UCP completes the transaction — If the user authorizes a purchase or booking, the commerce protocols handle checkout, payment, and post-purchase tracking.
The Protocol Stack at a Glance
| Layer | Protocol | Function | Creator |
|---|---|---|---|
| Agent ↔ Tool | MCP | Universal connector for AI agents to access tools and data | Anthropic |
| Agent ↔ Agent | A2A | Inter-agent communication and task delegation | |
| Agent ↔ Website | NLWeb | Natural language query interface for websites | Microsoft / R.V. Guha |
| Agent ↔ Website | WebMCP | Machine-readable capability declarations | Google & Microsoft |
| Agent ↔ Commerce | ACP | Standardized AI-powered checkout | OpenAI & Stripe |
| Agent ↔ Commerce | UCP | Full-journey commerce (discovery to returns) | Google & Shopify |
Each protocol addresses a different layer of the agent-business interaction. Some are mature and widely adopted, others are experimental. Let's break down each one.
What Is MCP and Why Is It the Foundation?
The Model Context Protocol (MCP), created by Anthropic, is the most established protocol in the agentic AI stack. Think of it as a USB-C port for AI — a universal standard that lets any AI agent connect to any tool, database, or service through a single interface instead of requiring custom integrations for each one.
Before MCP, every AI tool integration required its own custom connector. If you wanted Claude to access your CRM, you needed a Claude-specific integration. If you wanted ChatGPT to access the same CRM, you needed a separate OpenAI-specific integration. MCP eliminates this fragmentation by providing a single, open standard that any AI system can use. We covered the practical implications in our MCP servers guide.
MCP by the Numbers
MCP has achieved remarkable adoption in a short timeframe. Over 10,000 community-built MCP servers are now operational, connecting AI agents to everything from GitHub repositories and Slack workspaces to databases and payment systems. Every major AI platform — OpenAI, Google, Microsoft, Amazon — has adopted or announced support for MCP. It has become the de facto standard for the agent-to-tool layer.
What This Means for Your Website
MCP operates primarily at the tool and data layer, so most businesses won't implement MCP servers directly. However, MCP is why your structured data matters more than ever:
- AI agents use MCP to connect to search APIs, review platforms, and business directories — the same sources where your business information lives
- If your data is inconsistent across platforms, agents will flag it when cross-referencing through MCP-connected tools
- Clean, well-structured data on your website (schema markup, clear product/service information) makes it easier for MCP-connected tools to extract accurate information about your business
- Businesses with APIs or structured data feeds are inherently more accessible through MCP integrations
What Are A2A and How Do Agents Coordinate?
Google's Agent-to-Agent (A2A) protocol solves a different problem than MCP. While MCP connects agents to tools, A2A lets agents talk to each other. This matters because complex tasks — like researching, comparing, and booking a service — often require multiple specialized capabilities.
Imagine a user asks an AI agent to “find a reliable electrician in Fort Wayne who can rewire my kitchen next week.” With A2A, the primary agent can delegate subtasks: one agent searches local business directories, another checks review scores and certification status, a third verifies scheduling availability, and a fourth compares pricing. Each specialist agent publishes an “Agent Card” — a JSON file describing its capabilities — so other agents know what it can do.
How A2A Works
A2A uses a task-based architecture where agents communicate through structured messages. Each agent publishes an Agent Card (typically at /.well-known/agent.json) that describes its capabilities, input requirements, and output formats. When a primary agent needs help, it discovers relevant specialist agents through their Agent Cards and delegates tasks with clear parameters. The specialist agents process independently and report results back.
The protocol supports both synchronous (wait for an answer) and asynchronous (check back later) communication patterns, and includes built-in security features for authentication and authorization between agents.
What This Means for Your Business
A2A means that when an AI agent evaluates your business, it's not just one system making a decision — it's potentially multiple specialist agents each checking a different dimension of your business. A pricing agent verifies your rates are competitive. A reputation agent cross-references your reviews. A logistics agent confirms your service area and availability.
The practical implication: every touchpoint of your digital presence needs to be accurate and consistent. If your Google Business Profile says one thing and your website says another, a specialist agent will catch the discrepancy. If your reviews mention capabilities your website doesn't list, another agent will flag the gap. A2A makes data consistency a competitive advantage, not just a best practice.
What Is NLWeb and How Does It Change Website Discovery?
NLWeb, developed by Microsoft under the guidance of Schema.org creator R.V. Guha, is perhaps the most directly relevant protocol for business websites. It turns any website into something an AI agent can have a conversation with.
Today, when an AI agent visits your website, it essentially has to scrape and parse your HTML — the same way a search engine crawler does, but with the added challenge of understanding context and extracting specific answers to specific questions. NLWeb eliminates this friction by creating a standardized natural language interface. An agent can ask your website “What are your business hours?” and receive a direct, structured answer instead of hunting through your contact page. We've explored related concepts in our piece on AI-discoverable content through standards like LLMs.txt.
How NLWeb Works
NLWeb builds on Schema.org structured data — the same markup you may already have on your website for SEO purposes. It adds a natural language query layer on top, so your existing structured content becomes conversationally accessible. The protocol leverages retrieval-augmented generation (RAG) to provide accurate, context-aware answers drawn directly from your website content.
Microsoft has released NLWeb as an open-source project with built-in support for multiple AI models and vector databases, making it relatively accessible for businesses with technical resources.
What This Means for Your Business
NLWeb is where the “future-proofing” conversation gets concrete. If your website has clean, well-organized content with proper Schema.org markup, you're already laying the groundwork for NLWeb compatibility. The businesses that will benefit most from NLWeb are those that:
- Have comprehensive, accurate content about their products and services
- Use Schema.org structured data consistently
- Maintain up-to-date business information (hours, pricing, availability)
- Organize content in clear, question-answerable formats
The businesses that will struggle are those with thin content, outdated information, or websites that rely heavily on images and PDFs for critical business details that agents can't easily parse.
What Are WebMCP, ACP, and UCP — and Which Ones Matter Now?
The final three protocols complete the stack. Each addresses a specific gap in the agent-business interaction, and they vary significantly in maturity and immediate relevance.
WebMCP: Declaring What Your Website Can Do
WebMCP, proposed jointly by Google and Microsoft, extends the MCP concept to websites. While MCP connects agents to tools and APIs, WebMCP lets websites declare their capabilities in a machine-readable format. Think of it as a menu for AI agents: “Here's what you can do on this website — search products, check availability, book an appointment, request a quote.”
WebMCP is still in early development, but the concept is important. As AI agents become more capable, they'll need to know not just what information a website contains, but what actions they can take. WebMCP provides that capability map.
ACP: AI-Powered Checkout (OpenAI & Stripe)
The Agentic Commerce Protocol (ACP) from OpenAI and Stripe focuses specifically on the transaction layer. It standardizes how AI agents handle payments, providing a secure, consistent checkout experience regardless of which agent or which merchant is involved. If you've used Stripe's payment processing, ACP extends that model into the agent world. We explored the early signals of this shift in our coverage of ChatGPT Shopping and AI e-commerce discovery.
UCP: Full-Journey Commerce (Google & Shopify)
The Universal Commerce Protocol (UCP), developed by Google and Shopify, is the most ambitious of the commerce protocols. While ACP focuses on checkout, UCP covers the entire commerce journey — from product discovery and comparison through purchase, fulfillment tracking, and even returns. It launched with over 20 partners including Target, Walmart, and Wayfair.
For businesses selling products online, UCP is the protocol to watch. It standardizes how AI agents discover, evaluate, and transact with e-commerce businesses. If your products are available through platforms that adopt UCP, your inventory becomes directly accessible to AI shopping agents. Our guide on AI search for e-commerce product feeds covers the data foundation this requires.
ACP vs UCP: Quick Comparison
| Aspect | ACP (OpenAI & Stripe) | UCP (Google & Shopify) |
|---|---|---|
| Scope | Checkout and payment | Full commerce journey (discovery to returns) |
| Best for | Businesses using Stripe | E-commerce businesses on major platforms |
| Maturity | Early stage | Launched with 20+ retail partners |
| Key feature | Secure agent-driven payments | End-to-end commerce standardization |
| Integration effort | Moderate (Stripe ecosystem) | Low if on supported platforms |
What Should Your Business Do Right Now?
The good news: you don't need to implement all six protocols tomorrow. Most of them are early-stage, and the foundational work that prepares you for all of them is the same digital hygiene you should already be doing. As Search Engine Land's interview with Dell confirms, the current impact of agentic AI on actual conversions is still modest. But the infrastructure is being built now, and businesses that prepare early will have a significant advantage. We explored this dynamic in our post on the AI tipping point.
Immediate Actions (Do This Month)
- Audit your structured data. Ensure your website has comprehensive Schema.org markup — LocalBusiness, Product, Service, FAQ, and Review schemas at minimum. This is the foundation for MCP, NLWeb, and WebMCP.
- Verify cross-platform consistency. Check that your business name, address, phone number, hours, pricing, and service descriptions are identical across your website, Google Business Profile, Yelp, industry directories, and social media profiles.
- Create or update your LLMs.txt file. This simple text file helps AI systems understand your business at a glance. Our MCP servers guide covers how this fits into the broader AI discoverability stack.
- Organize content for question-answer extraction. Structure your service pages, FAQ sections, and product descriptions so that specific questions have clear, extractable answers.
Near-Term Actions (Next Quarter)
- Evaluate API opportunities. If your business has services that could be automated (appointment booking, quote generation, inventory checks), consider building or exposing simple APIs. These become the endpoints that MCP servers and WebMCP declarations can point to.
- Monitor your product feed accuracy. For e-commerce businesses, ensure your product feeds (Google Merchant Center, Facebook Catalog, etc.) are accurate, complete, and automatically updated. UCP will rely on these feeds.
- Implement comprehensive review management. AI agents weigh reviews heavily in their evaluation process. A systematic approach to generating, responding to, and monitoring reviews across platforms will directly influence how agents rank your business.
Watch-and-Wait Actions (2026–2027)
- NLWeb implementation. Once the open-source tooling matures and hosting options become more accessible, consider adding a NLWeb endpoint to your website. This will likely become a differentiator for service businesses.
- WebMCP capability declarations. When the standard solidifies, publish a capability map for your website so agents know exactly what actions they can take.
- Agent Card publication. If your business develops its own AI capabilities (like an AI-powered quoting tool), publishing an A2A Agent Card will make it discoverable by other agents.
Why This Matters for Northeast Indiana Businesses
If you're a business in Fort Wayne, Northeast Indiana, or a similar mid-market region, the agentic AI shift presents a genuine opportunity. Here's why: the businesses that get found by AI agents are the ones with clean, consistent, well-structured digital presences. In many local markets, the bar for this is still relatively low.
Most local competitors haven't updated their Schema.org markup, haven't created LLMs.txt files, and have inconsistent business information across platforms. The structured data and content hygiene work described in this article — which is accessible and affordable for any business — creates a measurable competitive advantage in how AI agents evaluate and recommend local options.
This is not about being first to implement cutting-edge protocols. It's about having the strongest foundation when those protocols go mainstream. Our AI solutions services are designed to help businesses build exactly this kind of future-ready digital infrastructure. If you're unsure where to start, talk to Button Block about AI-readiness — we'll help you assess your current foundation and build a practical roadmap.
Ready to Prepare Your Business for Agentic AI?
Button Block helps businesses build the structured data, API, and content foundations that support both today's AI search and tomorrow's agentic interactions. Our AI solutions services are designed for businesses that want to stay ahead of the curve.
Frequently Asked Questions
Frequently Asked Questions
- What is agentic AI and how is it different from chatbots?
- Chatbots respond to prompts in a single conversation turn. You ask a question, you get an answer. Agentic AI goes further by taking autonomous, multi-step actions on your behalf. An AI agent can browse multiple websites, compare prices, check reviews, verify availability, and even initiate a purchase or booking — all without you directing each step. The key difference is autonomy: chatbots assist, agents act.
- Which agentic AI protocol should my business prioritize?
- For most businesses, the answer is none of them directly — yet. The foundation that supports every protocol is clean structured data, consistent business information across all platforms, and machine-readable content on your website. If you sell products online, keep an eye on UCP and ensure your product feeds are accurate. If you offer services, focus on schema markup, reviews, and a clear API or structured content layer. The protocols are built on top of these fundamentals.
- Do I need to implement these protocols to show up in AI search results?
- No. AI search results (like Google AI Overviews or ChatGPT answers) currently pull from your existing web content, structured data, and authority signals. The protocols discussed in this article are about enabling deeper interactions — letting AI agents book appointments, compare products, or complete transactions on your behalf. You can appear in AI search results today without implementing any of these protocols. But as agents become more capable, businesses with protocol-ready infrastructure will have a competitive advantage.
- How much does it cost to implement agentic AI protocols?
- Most of the foundational work — structured data, clean APIs, consistent business listings — falls within normal web development and SEO budgets. Implementing specific protocols like MCP servers or NLWeb endpoints requires developer time, but open-source tools and documentation are available for all six protocols. For a typical small business, the cost is less about the protocols themselves and more about getting your digital foundation in order, which you should be doing regardless.
- Are AI agents actually making purchases for consumers right now?
- In limited cases, yes. OpenAI’s Operator can navigate checkout flows, and Google’s AI Mode is testing agentic shopping features with major retailers. However, widespread autonomous purchasing is still early-stage. Most current agent activity involves research, comparison, and recommendation rather than completing transactions. The infrastructure is being built now, and the protocols discussed in this article are what will enable full agent-driven commerce at scale.
- What happens if my pricing is different across my website and review platforms?
- Inconsistency is the fastest way to lose trust with AI agents. When an agent cross-references your website pricing with what appears on Google Business Profile, Yelp, or industry directories and finds discrepancies, it flags your business as unreliable. This can result in the agent recommending a competitor instead. Maintaining consistent, accurate information across all platforms is the single most important thing you can do to prepare for agentic AI.