Blog

The Agentic Web Standards: MCP, A2A, and What It Means for SEO

The web is evolving from pages designed for humans to infrastructure built for AI agents. Four emerging standards are defining how this new layer works.

If you thought SEO was complex before, wait until AI agents start browsing your website autonomously.

Here’s what’s happening, what these protocols do, and what it means for your SEO strategy.

The agentic web is a layer on top of the traditional web where AI agents—autonomous software programs—browse, interact, and complete tasks on behalf of users.

Traditional web: Human visits your site → reads content → takes action

Agentic web: AI agent visits your site → parses structured data → completes task → reports back to user

The shift is fundamental. Instead of optimizing for human attention, you’re optimizing for machine understanding. And just like the early web needed HTTP and HTML to function, the agentic web needs its own protocol stack.

Without standards, connecting your systems to AI agents looks like this:

• M AI platforms: Claude, ChatGPT, Gemini, Copilot, Perplexity, and whatever launches next
• N business systems: Databases, CRMs, inventory systems, internal tools

Result: M × N custom integrations. Unsustainable.

The solution? Standardized protocols that work everywhere.

The Model Context Protocol (MCP) is an open standard for connecting AI applications to external tools, data sources, and workflows.

Think of MCP like a USB-C port for AI. Just as USB-C provides a standardized way to connect devices, MCP provides a standardized way to connect AI systems to data.

Before MCP:

• Want Claude to access your database? Build custom integration
• Want ChatGPT to query your CRM? Build another integration
• Want Gemini to read your inventory? Build a third

With MCP:

• Build one MCP server for your data
• Every MCP-compatible AI system connects automatically

MCP launched November 2024. By early 2026:

• 97 million monthly SDK downloads
• 10,000+ public MCP servers
• Supported by: Claude (Anthropic), ChatGPT (OpenAI), Gemini (Google), Copilot (Microsoft)

If your data or services are MCP-accessible, AI agents can use them directly. This creates new traffic and visibility opportunities:

• Product inventory: AI agents check real-time availability
• Pricing data: Agents compare prices across sites
• Service availability: Agents book appointments
• Content libraries: Agents retrieve and cite your articles

Action item: Consider which of your data sources could benefit from MCP exposure.

The Agent2Agent protocol (A2A) enables AI agents from different vendors to discover each other’s capabilities and collaborate.

If MCP connects agents to tools, A2A connects agents to agents.

Every A2A-compatible agent publishes an Agent Card—a JSON metadata document at /.well-known/agent-card.json that describes:

• Agent identity
• Capabilities and skills
• Authentication requirements
• Communication protocols

When one agent needs help, it reads another agent’s card, understands what it can do, and requests collaboration.

Your business uses:

• Salesforce agent for CRM
• ServiceNow agent for IT
• Internal billing agent for invoices

With A2A, when a customer asks “What’s my invoice status?”, these agents coordinate:

1. Salesforce agent receives query
2. Discovers billing agent via A2A
3. Requests invoice data
4. Returns unified response to customer

A2A shifts focus from single-agent optimization to ecosystem positioning:

• Agent discoverability: Your AI services can be discovered by other agents
• Cross-platform collaboration: Your tools work across AI ecosystems
• New acquisition channels: Agents recommend your services to other agents

Action item: If you’re building AI-powered tools, consider A2A compatibility for discoverability.

NLWeb is an emerging standard for querying websites using natural language instead of structured URLs or APIs.

Traditional web:

GET /products?category=shoes&color=red&size=10

NLWeb:

"Show me red running shoes in size 10 under $100"

The website’s NLWeb interface parses the natural language, understands intent, and returns structured results.

AI agents prefer natural language queries. If your site supports NLWeb:

• Agents can query your content conversationally
• Complex filters become simple requests
• User intent is preserved in translation

NLWeb creates a new optimization layer:

• Natural language patterns: Optimize for conversational queries, not just keywords
• Intent preservation: Ensure your NLWeb interface understands context
• Structured responses: Return data in formats agents can parse

Action item: Monitor NLWeb adoption. If your site has complex query interfaces, natural language support may become a differentiator.

AGENTS.md is a Markdown file at the root of your repository that gives AI agents spatial awareness of your codebase or website structure.

When an AI agent starts working with your repository or website, it looks for AGENTS.md. If present, the agent reads it to understand:

• Project overview: What the project does
• Architecture: How components connect
• Conventions: Naming, structure, patterns
• Boundaries: What not to modify

This reduces discovery time and improves agent accuracy.

AGENTS.md applies to more than code repositories. For websites:

• Agent navigation: Help AI agents understand your site structure
• Content discovery: Guide agents to important pages
• Constraint communication: Define what agents should/shouldn’t do

Action item: Consider adding AGENTS.md to your website root with guidance for AI agents.

These four standards work together as layers:

1. MCP: Connects agents to data and tools
2. A2A: Connects agents to each other
3. NLWeb: Natural language interface for queries
4. AGENTS.md: Documentation for agent understanding

Together, they form the infrastructure layer for the agentic web.

AI agents parse structured data, not just HTML.

Actions:

• Implement comprehensive JSON-LD schema markup
• Consider MCP servers for key data sources
• Structure content for agent consumption, not just human readers

Keywords become conversation patterns.

Actions:

• Analyze natural language query patterns
• Optimize for question-based searches
• Create content that answers conversational queries

Visibility expands beyond search engines.

Actions:

• Publish Agent Cards if you have AI services
• Make your tools A2A-compatible
• Position your services in agent ecosystems

Early adopters gain advantages.

Actions:

• Experiment with MCP for your data
• Test NLWeb interfaces
• Document agent boundaries with AGENTS.md

• Audit existing JSON-LD schema markup
• Identify data sources that could benefit from MCP exposure
• Document your site structure for AI agents

• Build MCP server for key data (inventory, pricing, availability)
• Test natural language query interfaces
• Create AGENTS.md for your website

• Publish Agent Card if you have AI services
• Implement A2A compatibility for agent discovery
• Monitor agent traffic and behavior

• Analyze agent query patterns
• Refine natural language interfaces
• Update structured data based on agent usage

The agentic web isn’t replacing traditional SEO—it’s adding a layer.

Traditional SEO: Human visitors → content → actions AI Search Optimization: AI answers → citations → traffic Agent Optimization: AI agents → data → tasks

Your strategy needs all three.

1. Four protocols are emerging: MCP, A2A, NLWeb, and AGENTS.md form the infrastructure layer for the agentic web

2. Adoption is accelerating: Major AI vendors (OpenAI, Anthropic, Google, Microsoft) are building on shared standards

3. SEO is evolving: Optimize for machine understanding, not just human attention

4. Early preparation matters: Experiment with these protocols now to gain advantages as agent traffic grows

5. Structured data is foundational: JSON-LD schema markup becomes even more critical for agent consumption

1. Audit your structured data: Ensure comprehensive JSON-LD markup
2. Identify MCP opportunities: Which data sources could benefit from standardized access?
3. Consider AGENTS.md: Document your site for AI agent understanding
4. Monitor protocol adoption: Track how these standards evolve
5. Test natural language interfaces: Can your site handle conversational queries?

The agentic web is being built right now. The protocols defining it are open, collaborative, and gaining momentum.

Your SEO strategy needs to evolve with it.

Need help with structured data? Check out SEWWA’s Schema Generator to create JSON-LD markup that works for both search engines and AI agents.