More robust AI agents thanks to Model Context Protocol 

In recent years, the performance of language models (LLMs) has undergone spectacular advances. They are now capable of analyzing documents and images, generating code and holding natural, nuanced conversations. 

In this dynamic, a new generation of interfaces is emerging: AI agents. Capable of acting autonomously and relying on external tools, these agents push back the limits of what could be expected from conversational AI. 

In this article, you'll discover why AI agents represent a major evolution in the use of LLMs, what technical challenges they pose, and how the Model Context Protocol (MCP) proposes to address them by providing a standardized framework for their design. 

The rise of AI agents: promise and complexity 

Rather than simply generating text in response to a query, an AI agent is able to perform actions, relying on external tools such as APIs, databases, business applications or scripts. It thus becomes active rather than simply reactive. 

TheReAct architecture - one of the most popular design models - illustrates this principle: the model reasons about a task, chooses a tool, uses it, observes the result and adapts its strategy. The result is a particularly powerful perception/action/reflection loop. The results can be astonishing: booking meetings, analyzing logs, sending e-mails, calling CRMs, etc. 

But in practice, creating a robust and reliable AI agent is a real technical challenge. 

The challenges of implementing an AI agent 

Despite their potential, AI agents face several obstacles: 

• Contextual access to the right data: an LLM doesn't spontaneously know the user, his role, or the system in which he operates. 

• Tool coordination: to act effectively, the agent needs to understand which tools to use, in which order, and with what constraints. 

• Security compliance: the agent must only access information or functions authorized by the user's profile. 

• Standardization and maintainability: without a clear framework, each agent becomes a unique project, difficult to replicate or maintain. 

This is precisely where the Model Context Protocol comes in. 

Qu’est-ce que le Model Context Protocol (MCP) ?

The Model Context Protocol (MCP) is a standardized specification, proposed by Anthropic, designed to structure exchanges between an LLM and its environment to facilitate the implementation of intelligent agents. 

The MCP provides the model with the essential contextual elements it needs to understand : 

• Its role, 

• Features and tools available 

• The various resources to which it has access. 

All in a format that is clear, readable and actionable by the language model. 

Pourquoi le MCP est une avancée pour les agents IA ? 

Contrairement à des approches comme le RAG ou le prompt engineering qui visent à enrichir le modèle en données textuelles ponctuelles, le MCP fournit une infrastructure stable et générique pour concevoir des agents IA. Ses avantages sont nombreux : 

• Standardization: facilitates reuse and interoperability between projects. 

• Easy integration: smooth connections between LLM, business tools and data. 

• Robustness: the model is guided by a precise, usable framework, limiting any drift or error. 

• Scalability: makes it possible to generalize the creation of AI agents in different departments or corporate contexts. 

In other words, the MCP doesn't solve all the problems of an AI agent, but it does create the basis for their industrialization. 

Use cases for AI agents supported by the MCP 

When equipped with the right tools, an AI agent can provide much more targeted and specific responses. These tools enable it to : 

• Understand the user (role, preferences, history), 

• Take into account specific business requirements (rules, documentation, local constraints), 

• Access dynamic data (system status, weather, calendar, etc.), 

• Respecter des règles de sécurité (droits d’accès, confidentialité, auditabilité). 

Here are a few concrete examples of AI agents whose efficiency can be multiplied thanks to the Model Context Protocol. 

Here are a few concrete use cases: 

Personalized technical support 

An AI agent can adapt its responses according to the user's workstation, the hardware he or she is using, or the company's internal procedures. 

Exemple : Un technicien reçoit une réponse adaptée à son système d’exploitation et à son niveau d’accès réseau. 

HR or legal assistance 

L’agent comprend les droits et le statut de l’utilisateur (manager, collaborateur, RH), ainsi que les règles internes propres à son entité. 

Example: An HR answer that takes into account the specific collective agreement of the agency where the user works. 

Intelligent customer service 

En accédant à l’historique d’un client, son profil et son activité récente, l’agent de support client anticipe les besoins et personnalise ses réponses. 

Example: "I see you ordered this product yesterday, would you like to cancel or change the order?" 

Le Model Context Protocol : où en est-on aujourd’hui ? 

The MCP is still in an emerging phase, but has already been adopted by several major AI players. It is generating a great deal of interest in the community, as it meets a key expectation: to have a standard for designing truly useful, safe and maintainable AI agents. 

At Wikit, our R&D team is actively experimenting with MCP to evaluate its potential on concrete customer cases. The gradual integration of this protocol into our Wikit Semantics platform is a strategic direction for offering ever more personalized, robust and secure AI agents. 

Conclusion 

The Model Context Protocol should not be seen as a simple "contextualization" tool. It is an essential building block in the industrialization of AI agents, a lever for moving from simple prototypes to reliable, sustainable and always impressive solutions. 

By structuring the interactions between an LLM and its environment, the MCP facilitates the creation of agents capable of acting, learning and adapting. It paves the way for a new generation of professional assistants: powerful, responsible and aligned with your challenges. 

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