Intégration d'Agents IA & Model Context Protocol (MCP) : Orchestrer vos LLM en 2026

Generative AI as Core System Infrastructure

In 2026, AI integration moves beyond simple chat widgets. Enterprises demand autonomous AI Agents operating on business databases, invoking APIs, and executing background automations securely.

1. RAG (Retrieval-Augmented Generation) Architecture

RAG injects real-time corporate knowledge into foundational LLMs without costly model fine-tuning:

Data Embedding: Indexing documents via high-dimensional vectors.

Pgvector Search: Running sub-10ms cosine similarity queries inside Postgres.

Dynamic Context Injection: Feeding targeted snippets into system prompts.

typescript

// Vector similarity lookup with Pgvector
import { db } from './db';

export async function searchContext(queryEmbedding: number[], tenantId: string) {
  return await db.query(`
    SELECT content, similarity
    FROM document_embeddings
    WHERE tenant_id = $1
    ORDER BY embedding <=> $2::vector
    LIMIT 5
  `, [tenantId, JSON.stringify(queryEmbedding)]);
}

Summary

AI Agents transform business speed when coupled with robust backend architecture.

Integrating AI Agents & Model Context Protocol (MCP): Orchestrating LLMs in 2026

Mohamed Yassine Ben Yaala

Generative AI as Core System Infrastructure

1. RAG (Retrieval-Augmented Generation) Architecture

Summary

Let's build something exceptional.