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IA agentique : des chatbots aux agents qui font le travail
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IA agentique : des chatbots aux agents qui font le travail

Udemy Instructor
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About this course

Ce cours utilise l'intelligence artificielle. Tout le monde vend des « agents » en ce moment. Certains de ces produits planifient vraiment, appellent des outils et mènent à bien un travail en plusieurs étapes.

Beaucoup sont des chatbots ou des workflows figés sous une nouvelle étiquette. Ce cours vous donne une méthode claire et indépendante des éditeurs pour faire la différence, puis vous mène de la compréhension des agents à la construction, au test et au chiffrage de l'un d'eux. C'est une introduction large pour les professionnels et les nouveaux créateurs.

Aucune formation en apprentissage automatique n'est nécessaire. Si vous utilisez régulièrement ChatGPT, Claude, Gemini ou Copilot et voulez savoir ce qui change quand le modèle commence à agir, c'est le bon point de départ. Une entreprise, un pilote, du début à la finNous suivons Harborline Logistics, un transitaire fictif submergé par les e-mails de suivi d'expédition et en retard sur sa paperasse douanière.

Son équipe opérations mène un pilote d'agent de 90 jours appelé Relay. Chaque concept du cours est appliqué à ce pilote : vous voyez le même agent passer d'une première boucle à un système testé, supervisé et chiffré. Contenu du coursSection 1, L'IA agentique, vraiment : chatbot, copilote, workflow et agent ; la boucle de l'agent ; cinq niveaux d'autonomie ; repérer l'agent washingSection 2, La pensée des agents : modèle, outils et instructions ; ReAct, planification et réflexion ; cinq schémas de workflow et quand éviter un agentSection 3, Outils, MCP et Computer Use : appel d'outils, Model Context Protocol, utilisation du navigateur et de l'ordinateur, et modes agent de ChatGPT, Claude et GeminiSection 4, Mémoire, connaissances et contexte : ingénierie du contexte, récupération (RAG), compétences, et ancrage d'un agent dans les procédures de l'entrepriseSection 5, Construire des agents sans code : le paysage no-code, la spécification d'agent d'abord, et un agent de suivi d'expédition dans n8nSection 6, Des agents en Python : choisir un SDK sans dépendance, un agent qui fonctionne en une cinquantaine de lignes, sa connexion à un serveur MCP, et la lecture des tracesSection 7, Systèmes multi-agents : quand un agent ne suffit plus, orchestrateurs et passations, le protocole A2A, et un bureau douane à deux agentsSection 8, Confiance, valeur, adoption : évaluation, modes d'échec courants et garde-fous, validation humaine, coût par tâche, KPI, et une feuille de route d'adoption sur 90 joursDes labs pratiques sur votre propre ordinateurLes labs de construction tournent en local avec des outils gratuits : Python, Ollama pour les modèles locaux, et n8n dans Docker.

Aucune clé d'API payante n'est nécessaire. Le dossier de départ comprend une API de suivi d'expédition simulée, un serveur MCP, douze procédures de Harborline, ainsi que les e-mails de test et les cas douaniers utilisés dans les vidéos. Lab 1 : lancer la même tâche dans les modes agent de ChatGPT, Claude et Gemini et lire leurs journaux d'actionsLab 2 : indexer les procédures de Harborline et comparer les réponses avec et sans récupérationLab 3 : construire un agent de suivi no-code dans n8n et le confronter à dix e-mails de testLab 4 : reconstruire cet agent en Python, placer son outil derrière un serveur MCP, et trouver un bug placé grâce à la traceLab 5 : ajouter un second agent avec une passation et comparer résultats et consommation de tokens avec l'agent seulQuelques précisions honnêtes.

Le lab 1 utilise le mode agent d'un assistant de chat dans votre navigateur, et les modes agent disponibles dépendent de votre abonnement et de votre région. Si le vôtre n'en propose pas, vous pouvez suivre les sessions enregistrées. Les modèles locaux ne sont pas déterministes : vos réponses et vos taux de réussite aux évals différeront un peu des vidéos.

Le modèle local des labs demande environ 6 Go de mémoire libre. La section Python est facultative si vous ne voulez que les concepts et le parcours no-code. Pratique et évaluationChacune des huit sections comporte un quiz et un exercice pratique avec un corrigé détaillé.

Le projet final vous demande de choisir une tâche de votre propre travail et de produire une spécification d'agent, un niveau d'autonomie avec ses points de validation, un jeu d'évaluation de dix cas, et une estimation de coût avec une feuille de route sur 90 jours. Un examen d'entraînement final de 60 questions couvre les huit sections. Ce que ce cours n'est pasCe n'est pas un cours d'ingénierie approfondi sur un seul framework, et il ne promet ni emploi, ni certification, ni résultat particulier.

C'est une carte du domaine, avec assez de pratique pour juger des propositions d'agents, mener un petit pilote et décider quoi apprendre ensuite. Les outils de ce domaine évoluent vite : le cours enseigne donc des éléments portables (spécification, prompts, outils et évaluations) qui restent valables quand une plateforme change.

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