The internet is shifting from a collection of pages for humans to read, to a network of services for AI to navigate. This is the Agentic Web—an ecosystem where autonomous AI agents don’t just find information but execute complex tasks on your behalf.
For businesses, this transition is as significant as the shift from desktop to mobile. If your website isn’t “agent-ready,” it will soon be invisible.
As a blogger and AI engineer over the years, I have seen the shift in the web landscape. In fact, I lost my one job as a crypto blogger because of a Google algorithm update. Several other newsroom teams took a hit as well, including ones that are non-crypto.
Apart from impacting newsrooms, e-commerce sites are scrambling to prepare themselves for the agentic web as well. With protocols such as Agent-2-Agent, UCP, and others, the idea of an agentic web is no longer a scary, exciting thought, but is quickly becoming a reality.
In this article, I’ll share how I am preparing for the agentic web myself. This will also be a part of an ongoing series since it’s still the early stages of what could be the biggest shift in the web’s history.
What is the Agentic Web?
So what is the agentic web exactly?
At its core, the agentic web (also referred to as the open agentic web) is a vision of the internet where AI agents can autonomously navigate, interact, and perform tasks across different platforms using standardized protocols.
Unlike the “Semantic Web” of the past, which focused on tagging data, the Agentic Web focuses on action. It’s the difference between an AI telling you where to buy a flight and an AI booking that flight, handling the seat selection, and adding the receipt to your accounting software—all without you opening a browser.
The Three Pillars of Agentic Connectivity
To make that autonomous world work, three specific protocols and standards are emerging:
1. MCP (Model Context Protocol)
Think of MCP as the “universal adapter” for AI. It was developed as an open standard and allows AI models to safely and consistently access local or remote data sources and tools. Instead of writing custom code for every integration, a company provides an MCP server that any agent can plug into to understand its data.
I recently built an AI agent for this website to help me with SEO. It uses the Google Analytics MCP server, which allows me to seamlessly pull a large range of data for my site and quickly gain AI analytics.
So, instead of coding all of the functionality to pull the data from Google Analytics, I can now just plug an agent into the Google Analytics MCP server, ask the agent for insights, and it decides what data to pull.
2. Agent-2-Agent (A2A) Protocol
No single agent can do everything. The Agent-2-Agent protocol enables different AI agents to talk to each other. For example, your personal “Travel Agent” can negotiate directly with a hotel’s “Booking Agent” to find a room rate that isn’t listed publicly.
3. NLWEB (Natural Language Web)
NLWEB is the bridge between traditional websites and AI. It provides a set of rules for natural language interaction with a site. It often uses Schema.org and JSON-LD to return information in a format that AI “understands” instantly, rather than forcing the AI to scrape and guess the meaning of HTML.
How Agentic AI Searches the Web
One of the most common questions is: how does agentic AI perform web searches? Unlike a human who types a keyword and clicks the first link, an agentic AI follows a “Reasoning Loop”:
- Intent Interpretation: It breaks your request (e.g., “Plan a 3-day trip to Tokyo”) into sub-tasks.
- Autonomous Execution: It uses “agentic browsers” to visit multiple sites simultaneously.
- Website Analysis: It looks for structured data (APIs or Schema) rather than marketing copy.
- Action & Validation: It interacts with forms, clicks buttons, and verifies the outcome before reporting back.
Agentic AI Search Examples:
- Comparison Shopping: An agent searches five different retailers, applies your “loyalty member” discounts at each, and presents the true lowest price including shipping.
- B2B Procurement: An agent finds three vendors for a specific industrial part, requests quotes via their contact forms, and summarizes the lead times.
How Companies Can Prepare for the AI Era
If you want your business to be discovered and “hired” by AI agents, you need to move beyond “Human-First” design.
1. Prioritize Machine-Readable Data
AI agents don’t care about your beautiful hero images; they care about your JSON-LD. Ensure your product catalogs, pricing, and availability are marked up using Schema.org standards. This makes your site “discoverable” in the open agentic web.
2. Build “Digital Doorways” (APIs)
Agents prefer APIs over clicking buttons. Providing a clear, documented API—or an MCP server—allows agents to interact with your business with 100% accuracy, reducing the risk of “hallucinations” about your services.
3. Simplify the “Action Model”
If your checkout or booking process has 15 steps and 3 pop-ups, an AI agent might fail. Streamline your workflows. Use OAuth 2.0 for delegated access, allowing users to safely give their agents permission to act on their behalf on your site.
Using OAuth 2.0 allows users to grant ‘scoped permissions’—meaning an agent can check a shipping status without having full access to the user’s credit card details.
4. Monitor “Agentic Traffic”
Update your analytics to distinguish between human visitors, malicious bots, and legitimate AI agents. Knowing which agents are “browsing” your site helps you optimize your data for the ones that convert.
Frequently Asked Questions
What is the Agentic Web?
A decentralized internet where AI agents perform autonomous tasks across services.
What is the Open Agentic Web?
An ecosystem based on open standards (like MCP) that ensures no single company “owns” AI interactions.
How does agentic AI perform web searches?
By breaking goals into steps, navigating sites autonomously, and interacting with structured data.
