[{"data":1,"prerenderedAt":972},["ShallowReactive",2],{"/fr-fr/blog/ci-expert-and-data-analyst-ai-agents-target-development-gaps":3,"navigation-fr-fr":187,"banner-fr-fr":622,"footer-fr-fr":630,"blog-post-authors-fr-fr-Corinne Dent":869,"blog-related-posts-fr-fr-ci-expert-and-data-analyst-ai-agents-target-development-gaps":885,"blog-promotions-fr-fr":910,"next-steps-fr-fr":963},{"id":4,"title":5,"authors":6,"body":8,"category":166,"date":167,"description":168,"extension":169,"externalUrl":170,"faq":170,"featured":171,"heroImage":172,"meta":173,"navigation":171,"path":174,"seo":175,"slug":180,"stem":181,"tags":182,"template":185,"updatedDate":170,"__hash__":186},"blogPosts/fr-fr/blog/ci-expert-and-data-analyst-ai-agents-target-development-gaps.md","GitLab 18.11 : les agents CI Expert et Data Analyst comblent les lacunes du développement",[7],"Corinne Dent",{"type":9,"value":10,"toc":159},"minimark",[11,15,18,28,31,36,44,47,50,53,56,70,73,82,86,89,91,94,98,101,104,107,110,124,127,131,133,135,137,140,144,147],[12,13,14],"p",{},"Le code généré par l'IA progresse plus vite que les systèmes qui l'entourent ne peuvent suivre. Plus de code signifie plus de merge requests en attente, plus de pipelines à configurer, plus de questions sur la livraison auxquelles personne n'a le temps de répondre — et la plupart des outils sur lesquels les équipes s'appuient n'ont pas été conçus pour ce rythme.",[12,16,17],{},"Dans GitLab 18.11, deux nouveaux agents fondamentaux pour Duo Agent Platform s'attaquent à des lacunes spécifiques du cycle de développement que l'IA a largement laissées de côté :",[19,20,21,25],"ul",{},[22,23,24],"li",{},"L'agent CI Expert (désormais en version bêta) comble le fossé entre l'écriture\ndu code et son intégration dans un pipeline opérationnel.",[22,26,27],{},"L'agent Data Analyst (désormais en disponibilité générale) comble le fossé entre\nla livraison du code et la capacité à répondre à des questions fondamentales sur\nle déroulement réel de cette livraison.",[12,29,30],{},"Ces problématiques ne pouvaient pas être résolues par un assistant généraliste. Un outil fonctionnant en dehors de GitLab peut générer un fichier YAML ou répondre à une question, mais il n'a aucune connaissance des performances historiques de vos pipelines, des zones de concentration des échecs, ni de vos temps de cycle de merge request réels. Ce contexte réside dans GitLab. Ces agents aussi.",[32,33,35],"h2",{"id":34},"configurer-rapidement-la-ci-avec-lagent-ci-expert","Configurer rapidement la CI avec l'agent CI Expert",[12,37,38,39,43],{},"L'IA a facilité l'écriture du code comme jamais auparavant. Intégrer ce code dans un pipeline opérationnel reste pourtant quelque chose que la plupart des équipes font des jours, voire des semaines plus tard — si tant est qu'elles le fassent. Le problème de la page blanche n'est plus dans l'éditeur. La page blanche, c'est désormais ",[40,41,42],"code",{},".gitlab-ci.yml",".",[12,45,46],{},"Les développeurs qui n'ont jamais configuré de CI ne savent pas à quoi ressemble la détection de langage en YAML, quelles commandes de test utiliser, ni comment valider le résultat avant de pousser leurs modifications. Les équipes copient généralement une configuration d'un projet précédent qui ne correspond pas forcément, assemblent des exemples tirés de la documentation, ou attendent la seule personne qui l'a déjà fait. Si cette personne n'est pas disponible, la CI devient quelque chose qu'on « fera plus tard ». Plus tard ne vient jamais.",[12,48,49],{},"Quand la CI n'est jamais mise en place, les conséquences se font sentir partout. Les modifications sont livrées sans filet de sécurité fiable, les régressions apparaissent en production plutôt qu'en pipeline, et le travail s'accumule en lots plus importants et plus risqués, car personne ne veut être celui qui « casse le build ». Avec le temps, les équipes s'habituent à travailler dans l'incertitude, en s'appuyant souvent sur des connaissances institutionnelles non documentées et des tests ad hoc, plutôt que sur une boucle de retour rapide et prévisible intégrée à chaque modification.",[12,51,52],{},"L'agent CI Expert, désormais disponible en version bêta, supprime ces frictions. Il inspecte votre dépôt, identifie votre langage et votre framework, et propose un pipeline de build et de test opérationnel, adapté à ce qui s'y trouve réellement — en expliquant chaque décision en langage clair. L'objectif : un pipeline fonctionnel en quelques minutes, sans écrire une seule ligne de YAML à la main.",[12,54,55],{},"Ce que fait l'agent CI Expert :",[19,57,58,61,64,67],{},[22,59,60],{},"La génération de pipeline tenant compte du dépôt détecte le langage, le\nframework et la configuration des tests.",[22,62,63],{},"Il génère des configurations de build et de test valides et exécutables.",[22,65,66],{},"Un flux guidé pour le premier pipeline, avec une explication en langage clair\nde chaque étape dans Agentic Chat.",[22,68,69],{},"Une sémantique GitLab CI native, sans traduction de configuration requise.",[12,71,72],{},"Parce qu'il s'exécute dans GitLab et observe le comportement réel des pipelines au fil du temps, chaque amélioration peut s'appuyer sur la façon dont les équipes travaillent réellement, et non sur de simples exemples statiques.",[74,75],"iframe",{"src":76,"frameBorder":77,"allow":78,"referrerPolicy":79,"style":80,"title":81},"https://player.vimeo.com/video/1183458036?badge=0&autopause=0&player_id=0&app_id=58479","0","autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share","strict-origin-when-cross-origin","position:absolute;top:0;left:0;width:100%;height:100%;","CI/CD Expert Agent",[83,84],"script",{"src":85},"https://player.vimeo.com/api/player.js",[87,88],"br",{},[87,90],{},[12,92,93],{},"L'agent CI Expert est disponible sur GitLab.com, Self-Managed et Dedicated, dans les éditions Free, Premium et Ultimate avec GitLab Duo Agent Platform activé.",[32,95,97],{"id":96},"interroger-les-données-gitlab-en-langage-naturel-avec-lagent-data-analyst","Interroger les données GitLab en langage naturel avec l'agent Data Analyst",[12,99,100],{},"L'IA a accéléré la cadence de livraison des équipes. Répondre à des questions fondamentales sur l'avancement de ce travail est devenu plus difficile, pas plus simple.",[12,102,103],{},"Combien de temps les merge requests restent-elles en revue ? Quels pipelines ralentissent les équipes ? Les objectifs de déploiement sont-ils réellement atteints ? Ces questions trouvaient autrefois une réponse en consultant un tableau de bord. Aujourd'hui, avec davantage de code, davantage d'équipes et une complexité accrue, les données existent — elles sont dans GitLab — mais y accéder implique encore d'attendre une équipe analytique, de soumettre une demande de tableau de bord, ou d'apprendre le GLQL.",[12,105,106],{},"L'agent Data Analyst comble ce fossé. Posez une question en langage naturel et obtenez une visualisation instantanée dans Agentic Chat. Aucun langage de requête, aucune demande de tableau de bord, aucune attente que quelqu'un d'autre assemble les réponses.",[12,108,109],{},"Par exemple, l'agent peut répondre aux questions portant sur les sujets suivants, selon les rôles :",[19,111,112,115,118,121],{},[22,113,114],{},"Responsables ingénierie : temps de cycle des merge requests, débit par projet,\npoints de blocage dans les revues.",[22,116,117],{},"Développeurs : tendances de contribution, tests instables bloquant leurs merge\nrequests, évolution de la vitesse des pipelines.",[22,119,120],{},"Ingénieurs DevOps et plateforme : taux de succès/échec des pipelines,\nutilisation des runners, fréquence de déploiement.",[22,122,123],{},"Direction ingénierie : fréquence de déploiement multi-portefeuille, métriques\nde santé des projets, comparaisons des délais de livraison.",[12,125,126],{},"Désormais en disponibilité générale dans la version 18.11, l'agent couvre les merge requests, les tickets, les projets, les pipelines et les jobs — une couverture complète du cycle de vie du développement logiciel, étendue par rapport au périmètre de la version bêta. Parce que l'agent Data Analyst interroge ce qui se trouve déjà dans GitLab, le contexte est toujours à jour, sans pipeline à maintenir ni outil tiers à synchroniser. Les requêtes générées en GitLab Query Language peuvent être copiées et utilisées partout où le Markdown GitLab est pris en charge, avec une exportation directe vers les éléments de travail et les tableaux de bord prévue dans la roadmap.",[74,128],{"src":129,"frameBorder":77,"allow":78,"referrerPolicy":79,"style":80,"title":130},"https://player.vimeo.com/video/1183094817?badge=0&autopause=0&player_id=0&app_id=58479","Data Analyst agent demo",[83,132],{"src":85},[87,134],{},[87,136],{},[12,138,139],{},"L'agent Data Analyst est disponible sur GitLab.com, Self-Managed et Dedicated, dans les éditions Free, Premium et Ultimate avec GitLab Duo Agent Platform activé.",[32,141,143],{"id":142},"une-plateforme-unique-un-contexte-connecté","Une plateforme unique, un contexte connecté",[12,145,146],{},"Les deux agents s'exécutent dans GitLab, avec accès au code, aux pipelines, aux tickets et aux merge requests déjà présents. C'est ce qui distingue une IA native à la plateforme d'un assistant déconnecté : le contexte est toujours à jour et ne fait que gagner en pertinence avec le temps. L'agent CI Expert et l'agent Data Analyst représentent deux avancées concrètes vers une plateforme où l'IA ne se contente pas de vous aider à écrire du code plus vite, mais vous aide à comprendre, livrer et maintenir ce qui est construit.",[148,149,150],"blockquote",{},[12,151,152,158],{},[153,154,157],"a",{"href":155,"rel":156},"https://about.gitlab.com/fr-fr/gitlab-duo-agent-platform/",[],"Commencer un essai gratuit de GitLab Duo Agent Platform","\npour découvrir ces agents IA fondamentaux.",{"title":160,"searchDepth":161,"depth":161,"links":162},"",2,[163,164,165],{"id":34,"depth":161,"text":35},{"id":96,"depth":161,"text":97},{"id":142,"depth":161,"text":143},"product","2026-04-16","Configurez votre CI et interrogez vos données de cycle de vie du développement logiciel grâce à deux nouveaux agents fondamentaux de GitLab Duo Agent Platform, disponibles dans GitLab 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