Agéntica Website​

What is an “Agéntica” Website?

An “Agent-Based Web” is a digital infrastructure optimized to enable Artificial Intelligence agents (both internal and external) to navigate, reason, make decisions, and execute complex transactions or actions autonomously.

It is the evolution of a traditional website into a digital ecosystem designed to interact with artificial intelligence agents.

It involves the comprehensive transformation of a company’s digital presence so that it can be understood, used, and acted upon by people and AI agents.

It’s not just a matter of adding a chatbot or incorporating AI-based functionality.

An “Agency-Style” Website involves transforming a website’s architecture, content, data, and features so that it can be:

• Understood by artificial intelligence (internal or external)
• Interpreted according to different needs.
• Queried automatically.
• Integrated into digital processes.
• Used to carry out specific actions

Until now, websites were built primarily for two types of users:

People

• Who read content.
• They navigate through menus.
• They compare information.
• They click.

Search Engines

• That crawl web pages.
• They interpret links.
• They index information.
• They sort the results.
• They support actions (purchases, reservations) using Google’s UCP and UC, as well as OpenAI’s ChatGPT Shopping

The Agentic Web introduces a third type of user: AI agents. These are systems capable not only of finding information, but also of reasoning about it and taking action.

This requires a website to evolve from a collection of pages into an interconnected system of knowledge and services.

AI Agents:

• Recommend brands or websites
• They browse them
• They make comparisons
• They take actions such as buying, making reservations, subscribing, etc.

What can a Web Agéntica do?

The main difference between a traditional website and an Agéntica website lies in its ability to move from information to action.

A traditional page responds to a navigation action.

An “Agéntica” website plays an active role in addressing a need.

Key features:

1. Be GEO Agentic Friendly. Be a website that is visible, relevant, and capable of enabling other AIs to take actions on it. This ranges from being relevant to an AI search engine, an AI browser, or a user’s personal AI agent for a product sold on the website, to enabling the purchase of that product or service directly through that same AI.
2. Enables external agents to perform actions through APIs, agent-to-system transactions, or MCP
3. It incorporates its own agent-based capabilities, such as having an AI Agent. Not an Assistant—an AI Agent. The difference is that an AI Assistant only provides information based on and interpreted by AI, whereas an AI Agent can perform actions such as registering a user, adding a product to the cart, or completing checkout directly through the AI Agent itself.

Agency Websites: A2A, A2B, A2C

A2A: Agent to Agent

Communication and Actions by an AI Agent to a Web Agent-Friendly Platform or Another AI Agent

Cases:

A personal agent (an AI agent such as Google Spark, an AI browser such as ChatGPT Atlas, Google itself making purchases with Universal Cart, purchases made through ChatGPT, etc.)

Example:

The AI agent searches for shoes based on your preferences and, with the user’s permission, purchases them from a retailer through Google.

A2B: Agent to Business

When an agent interacts with a company. The difference between A2C and A2B is similar to that between B2B and B2C: the end customer is a company.

Cases:

1. Automatic Request for Information.
2. Supplier Comparison.
3. B2B Business Processes.
4. Document Management.

Example:

A corporate agent searches for suppliers, compares proposals, and requests quotes.

A2C: Agent to Consumer

A web-based AI agent provides direct assistance to users visiting the website, if they choose to use it.

The agent can:

• Interpret the intent.
• Analyze different options.
• Compare information.
• Recommend options.
• Personalize the experience.
• Take action

This completely changes the relationship between the user and the brand.

The question is no longer: “How do I get a user to choose my product?”

And it becomes: “How can I get my agent to recommend the product that the user will like the most and is most likely to buy?”

Some examples:

1. Personal shopping assistants, smart comparison tools (which not only make recommendations but can also perform actions such as adding products to the cart or checking out directly). Example: On a website for hotels and activities: “Search for a family vacation for less than €2,000, book a hotel and activities.”
2. 24/7 customer service (electricity company, phone company, etc.). Example: I need to change the direct debit to this new account.

Summary of the Differences Between A2A, A2B, and A2C

A2A: Agent–agent: Two agents exchange context, coordinate tasks, or perform operations through defined interfaces and permissions.

A2B: Agent–Company: An agent representing the user or another organization accesses or uses the company’s services.

A2C: Agent–Consumer: A brand representative helps the user find information or take action.

Why You Should Transform Your Website into a Web Agéntica Now: Use Cases

More and more users are delegating part of the process of discovery, analysis, and decision-making to AI assistants. They no longer simply ask a search engine where to find information; instead, they directly ask for an answer, a recommendation, or a solution:

• “Find the best option for my needs.”
• “Compare these options for me.”
• “Recommend a product for me to buy.”
• “Please book the best option for me.”

This implies a fundamental change: the user is no longer the sole intermediary between need and action.

We’re entering a new model: User – AI Agent – Brand – Action

AI agents are beginning to act as new digital intermediaries capable of:

• Understanding a need.
• Search for information.
• Compare options.
• Assess trustworthiness and relevance.
• Perform tasks.

In this new landscape, a website can no longer be designed solely for human visitors. It must also be designed to be understood, interpreted, and used by artificial intelligence systems.

The question is no longer: “Can my users easily find what they’re looking for on my website?”

And it becomes: “Can an AI agent understand who I am, what I offer, and take action within my ecosystem?”

This is where the concept of Web Agéntica comes from: the evolution of digital environments toward experiences where humans and artificial intelligence can interact with brands.

Azurally’s Methodology for Building an Agent-Based Website

Azurally’s methodology for building an agent-based website is simple, effective, practical, and scalable.

1. We analyze the current website: content performance, users, AI visibility, marketing strategy, current GEO and SEO, and anything else that may be relevant to the process.
2. We hold a workshop with our client based on the analyzed data.
3. We propose an Action Plan organized by priority and effort.
4. We began working on the products and product categories to create a single, geographically optimized data source for agents.
5. We developed an Agentic Commerce strategy with Grouding GEO
6. We are working simultaneously on GEO Agentic-Friendly Web Optimization, AI Governance, and the other elements outlined in the Agentic Commerce Strategy.
7. We are developing the Supervising AI Agent. All the other AI agents we will create for the website and the organization will depend on this Supervising AI Agent to ensure consistency and adherence to structural rules.
8. We have developed various AI agents, both for users (sales assistant, product recommender, all-in-one agent, etc.) and for internal use by our clients (supplier agent, internal training agent, internal agent for inquiring about website performance, etc.).

Measuring the Results of an Agent-Based Website

To measure the performance of an Agéntica website, we use various data sources in addition to our own events and KPIs within GTM.

• To Track Visits from AI Agents: We currently rely on the data provided by Google Analytics 4 and GSC for Google AI Search.
• For traffic coming from AI: Google Analytics 4.
• To measure visibility across different search engines: we use various tools—the most advanced ones on the market—to track those KPIs.
• To track user behavior within our web-based AI assistants: using events in GTM and internal statistics on questions, actions, and conversions.

Azurally Measurement Model

To measure an “Agentic” website, we combine different sources:

• GEO Tools for AI Visibility.
• Traditional web analytics (GA4, GSC, analytics platforms).
• Server logs.
• Technical monitoring of agents.
• Event-based tracking.
• Agent’s internal data.
• CRM and Business Data.

This way, we can measure the entire journey, regardless of when each user begins it:

• AI Discovers
• AI understands
• IA recommends
• AI interacts
• AI runs
• Business is growing

Why Build an Agency Website with Azurally?

A MarTech agency with awards and a proven track record in GEO (AI-powered search engine optimization). To make an “Agentic” website as comprehensive as possible, it’s essential to have a team that specializes in GEO and Agentic Marketing. The hardest part isn’t creating an AI agent; it’s defining the processes so that responses and actions are as profitable as possible, while also adhering to what was originally defined and incorporating a significant marketing component in line with a strategy designed specifically for that purpose.

Innovation: If a new technology comes along that can give you an edge, we’re already testing it.

Scalability and Modularity: We create processes that prioritize scalability and compatibility with other tools and processes. We build using a modular approach so that nothing goes to waste and we can adapt to any situation.

Focus on results: We don’t get bogged down in endless processes; we design agents that take action, close sales, and drive conversions.

Agile: We build, test, and optimize as quickly as possible. If the market changes, your website changes with it.