The Open Agentic Web Concept¶
The concept of the Open Agentic Web, often associated with the emerging idea of "Web 4.0", represents a significant evolution of the web, where interactions between users and websites are mediated by autonomous intelligent agents powered by artificial intelligence (AI).
These agents act as proactive intermediaries, capable of understanding user intentions, negotiating, making decisions and executing tasks autonomously.
To analyze this concept and its implications using a first-principles approach, we will decompose the problem into its fundamental elements, understand the underlying needs, and propose recommendations for business and website adaptation.
1. First-principles analysis of the Open Agentic Web concept¶
a. Breaking down the concept: what is the Open Agentic Web? The Open Agentic Web is based on the idea that the web is evolving from a static or interactive space (Web 1.0 to 3.0) to an ecosystem where AI agents act as autonomous entities to facilitate interactions between users and online services. Here are the basic elements:
Intelligent agents: These are AI programs capable of interpreting user needs, analyzing complex data, negotiating with other agents (on the business or other user side), and performing tasks without constant human intervention. For example, an agent could plan a trip by negotiating directly with booking sites.
Decentralization and interoperability: The "open" in Open Agentic Web suggests a non-partitioned ecosystem, where agents can interact with various services and platforms without being constrained by proprietary silos. This requires open standards and interoperability protocols.
Intent-centric: Unlike today's web, where users manually navigate to find information, the Open Agentic Web focuses on understanding user intent. Agents anticipate needs, personalize experiences and automate processes. Web as conversational ecosystem: Interactions are no longer limited to clicks or searches. AI agents communicate with each other (for example, a user's agent dialogues with an e-commerce site's agent) to optimize results, such as getting the best price or personalizing an offer.
Historical context: Web 1.0 was static (read-only), Web 2.0 interactive (social networks, user-generated content), Web 3.0 semantic and decentralized (blockchain, connected data). Web 4.0, or Open Agentic Web, introduces autonomous agents that act as extensions of users and businesses, making the web proactive and predictive.
b. Fundamental problems solved by the Open Agentic Web¶
Using a first principles approach, let's identify the basic problems of today's web and how the Open Agentic Web addresses them: Information complexity: The volume of information online is overwhelming, making manual searching inefficient. Today's websites are often compared to a "library without a librarian". AI agents solve this problem by filtering, analyzing and presenting only relevant information.
Interaction friction: Users have to navigate manually, compare options and perform repetitive tasks. Autonomous agents reduce this friction by automating processes (e.g. comparing prices, filling in forms). Lack of personalization: Today's websites offer limited personalization, often based on cookies or basic algorithms. AI agents can understand complex contexts and propose tailor-made solutions.
Proprietary silos: Many platforms trap data and limit interoperability. An "open" web favors common standards, enabling agents to operate seamlessly across different services.
c. Fundamental assumptions¶
Users want fast, relevant and effortless experiences.
Companies seek to maximize customer engagement while reducing operational costs.
AI technology is sufficiently advanced to enable reliable and secure autonomous interactions.
Open standards (interoperability, protocols) are needed to avoid fragmentation.
2. How should websites adapt?¶
To thrive in the Open Agentic Web, websites must evolve to become platforms compatible with AI agents, adopting technical, structural and strategic approaches. Here are the necessary adaptations, deduced from the first principles:
a. Agent-compatible interfaces (APIs and open standards)¶
Problem: Today's websites are designed for human interaction (clicks, forms), not for AI agents. Agents require robust machine-to-machine interfaces (APIs).
Solution: Sites need to expose structured APIs enabling agents to read and interact with their data (e.g. product catalogs, availability, prices). This includes adopting open standards such as JSON-LD or GraphQL for maximum interoperability.
Example: An e-commerce site could provide an API enabling a user agent to check stock, compare prices and place an order automatically.
b. Semantic and structured data¶
Problem: Today's websites often use non-standardized data structures that are difficult for AI agents to interpret.
Solution: Adopt semantic schemas (such as Schema.org) to structure data (products, services, customer reviews) in a way that can be understood by machines. This enables agents to understand the content and use it effectively.
Example: A restaurant could structure its menus with semantic metadata, enabling an agent to reserve a table based on a user's dietary preferences.
c. Dynamic personalization¶
Problem: Sites offer static personalization based on predefined profiles. AI agents require real-time personalization based on intent.
Solution: Integrate AI systems capable of dynamically responding to user agent queries, adjusting offers or content based on contextual data. This could include advanced chatbots or recommendation engines.
Example: A travel site could adjust its flight recommendations based on preferences transmitted by a user's agent (budget, flexible dates, preferred destinations).
d. Security and confidentiality¶
Problem: Interactions between agents increase the risk of data leaks or attacks.
Solution: Implement robust security protocols (agent authentication, data encryption) and comply with regulations such as RGPD. Sites must also allow users to control the data shared with agents.
Example: a site could use authentication tokens to limit agent access to certain sensitive data.
e. Accessibility and user experience¶
Problem: Agents need to be able to navigate sites designed for humans, while providing a fluid experience.
Solution: Adopt web accessibility principles (clear navigation, semantic structure, responsive design) to facilitate agent interaction while enhancing the human experience.
Example: A site with clear semantic navigation enables an agent to quickly locate relevant information, such as product return policies.
f. Optimization for negotiation¶
Problem: AI agents can negotiate (for example, to get the best price), which is not supported by current sites.
Solution: Integrate AI-based negotiation engines capable of responding to offers from user agents and optimizing transactions in real time.
Example: A commerce site could enable its agent to offer personalized discounts based on parameters negotiated by the user's agent.
3. How can companies extend their functionality on the web?¶
Companies need to rethink their online presence to take advantage of the Open Agentic Web. Here are some strategic recommendations based on the first principles:
a. Develop proprietary agents¶
Why? Companies can create their own AI agents to represent their interests, negotiate with user agents, and optimize interactions. These agents can handle tasks such as customer service, inventory management or offer personalization.
How can we do this? Invest in AI solutions such as advanced language models (for example, via the xAI API) to develop agents capable of understanding complex intentions and acting autonomously.
Example: A retail company could deploy an agent that negotiates prices with customer agents, proposes bundled offers, or manages returns automatically.
b. Integrate conversational functionalities¶
Why should you do this? AI agents rely on conversational interactions. Companies need to enable their sites to interact with these agents.
How can they do this? Integrate chatbots or AI assistants capable of understanding natural language and responding to user agent queries. This includes the use of technologies such as LLMs (large language models) for fluid conversations.
Example: A hotel booking site could integrate an AI assistant that responds to user agents by suggesting rooms according to specific criteria (view, budget, amenities).
c. Leverage data for advanced personalization¶
Why? User agents demand ultra-personalized responses, which requires in-depth data analysis.
How? Use data analysis tools (CRM, Martech tools) to collect and analyze user behavior, then pass this information on to enterprise agents for tailored recommendations.
Example: A streaming platform could use a user's data (history, preferences) to enable its agent to suggest specific movies to the user's agent.
d. Adopt business models based on automation¶
Why? AI agents reduce operational costs by automating customer interactions, sales and support.
How can this be achieved? Develop web or mobile applications that integrate with AI agents to automate processes such as order management, customer support or marketing campaigns.
Example: An SME could use a web application connected to an AI agent to automatically manage quote requests and contracts.
e. Participate in the open ecosystem¶
Why should you do this? The Open Agentic Web is based on collaboration and interoperability. Companies that adopt open standards will gain in visibility and efficiency.
How can we help? Contribute to open-source initiatives or collaborate with consortia to develop protocols for interaction between agents. This includes using CMS like WordPress with agent-compatible extensions.
Example: A company could join a consortium to standardize e-commerce APIs, enabling its agent to integrate easily with other platforms.
f. Train teams and invest in skills¶
Why should you do this? The Open Agentic Web requires technical and strategic skills to design, manage and optimize AI agents.
How can we achieve this? Train teams in web development, AI, digital security and UX to create agent-compatible sites. Web agencies can play a key role in supporting this transition.
Example: A company could work with a web agency to train its developers to integrate APIs compatible with AI agents.
4. Strategic recommendations for companies¶
Invest in AI and interoperability: Allocate resources to develop or integrate AI agents and standardized APIs. Explore solutions such as the xAI API for advanced capabilities.
Rethink site architecture: Move from human-user-centric to agent-centric logic, with structured data, robust APIs and conversational interfaces.
Prioritize security and ethics: Implement protocols to protect user data and guarantee transparency in interactions with agents. Collaborate with experts: Work with web agencies specializing in AI and web application development to accelerate adaptation.
Experiment progressively: Launch pilot projects (for example, an AI agent for customer service) before rolling out functionality across the entire site.
5. Conclusion¶
The Open Agentic Web marks a transition to a proactive web, where autonomous AI agents simplify interactions, reduce friction and personalize experiences. To adapt, websites must become open platforms, with APIs, structured data and conversational capabilities.
Companies, meanwhile, need to invest in proprietary agents, leverage data for personalization, and participate in an interoperable ecosystem. By embracing these changes, they can transform their sites into dynamic tools, capable of meeting the expectations of an evolving web while remaining competitive in a changing digital landscape.
If you'd like to find out more about a specific aspect (for example, the technical aspects of APIs or concrete use cases), please don't hesitate to contact us!