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aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"overview","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-5c1d167ca2","type":"heading2","lines":["Présentation"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-f21bddc2b4","text":"\u003Cp\u003ELes agents d’IA révolutionnent les entreprises modernes en automatisant les tâches et en améliorant la prise de décision à l’aide d’algorithmes et de fonctionnalités de machine learning avancés. Ces systèmes sont conçus pour répondre aux besoins des entreprises en matière \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-analytics/\"\u003Ed’analyse de données\u003C/a\u003E. Ils les aident à gagner en efficacité et en productivité par la simplification de leurs processus. Cependant, leur influence va au‑delà de la simple automatisation : les agents d’IA peuvent gérer des tâches complexes et prendre des décisions, ce qui représente un bond en avant considérable par rapport aux logiciels de base actuels. À mesure que l’IA s’améliore, ces agents collaboreront à la planification et à l’exécution des tâches, ce qui aidera les entreprises à gagner en efficacité.\u003C/p\u003E\r\n\u003Cp\u003EAlors que les entreprises exploitent de plus en plus \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/ai-applications/\"\u003El’intelligence artificielle\u003C/a\u003E, il est essentiel de comprendre le rôle et l’impact des agents d’IA. Cet article examine la nature des agents d’IA, leurs fonctionnalités, leurs avantages, leurs types, leurs applications concrètes et leurs perspectives d’avenir.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"what-is","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-bca860c374","additionalClasses":"headline-decoration","type":"heading2","lines":["Qu’est‑ce qu’un agent d’IA ?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-1f7b2fe075","text":"\u003Cp\u003EUn agent d’IA est une forme d’intelligence artificielle conçue pour fonctionner de manière autonome. Il peut prendre des décisions, effectuer des tâches et s’adapter à son environnement sans intervention humaine constante. \u003Cb\u003EL’IA traditionnelle\u003C/b\u003E est un système basé sur des règles conçu pour des tâches spécifiques, qui s’appuie sur des algorithmes et une logique prédéfinis pour effectuer des fonctions telles que la prédiction et la classification. \u003Cb\u003EL’IA générative\u003C/b\u003E se concentre sur l’apprentissage de modèles à partir de vastes jeux de données pour créer du contenu tel que du texte, des images ou du son. \u003Cb\u003EL’IA agentique \u003C/b\u003Efranchit un nouveau cap en exploitant de grands modèles de langage (LLM) et des techniques avancées de machine learning pour résoudre des problèmes en plusieurs étapes grâce à un raisonnement sophistiqué et une planification itérative. Les agents d’IA peuvent passer au crible de manière indépendante différentes sources d’informations, juger de leur pertinence, de leur exactitude et de leur fiabilité, et utiliser leur capacité de jugement et leurs pouvoirs d’exécution améliorés pour atteindre l’objectif fixé par l’utilisateur. \u003C/p\u003E\n\u003Cp\u003EDans un système d’IA agentique, plusieurs agents d’IA peuvent gérer ensemble et de manière autonome des tâches complexes telles que le traitement de changements de compte, la résolution de problèmes de facturation et la mise en place de récompenses personnalisées pour renforcer la satisfaction des clients. Pour ce faire, ils analysent les informations disséminées dans différents formats et domaines d’une entreprise (bases de données, pages web, wikis internes, e‑mails, etc.) afin d’identifier les informations les plus fiables et à jour. Ensuite, ils appliquent le raisonnement en ayant recours à des algorithmes d’analyse, de détermination des tendances et de prise de décision pour générer des options en vue d’une solution. Après avoir choisi la meilleure option, ils coordonnent le plan et l’exécutent. Enfin, ils apprennent des résultats de leurs actions et s’améliorent pour la requête suivante. Cette capacité à gérer et exécuter des tâches complexes de manière autonome et à en tirer des enseignements marque une avancée significative par rapport aux précédentes itérations de l’IA. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_1767623498":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"how","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-4129ea0375","additionalClasses":"headline-decoration","type":"heading2","lines":["Fonctionnement des agents d’IA"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-98de0b08e1","text":"\u003Cp\u003ELes agents d’IA utilisent des technologies avancées telles que les algorithmes de machine learning, le traitement du langage naturel (NLP) et les réseaux neuronaux pour effectuer des tâches de manière autonome et efficace. Ces technologies permettent aux agents d’IA d’analyser d’importants volumes de données, de reconnaître des schémas complexes et de générer des réponses semblables à celles d’un être humain. En tirant un enseignement constant des données qu’ils traitent, les agents d’IA peuvent s’adapter à de nouveaux scénarios et améliorer leurs performances au fil du temps.\u003C/p\u003E\r\n\u003Cp\u003ELe \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-processing/\"\u003Etraitement des données\u003C/a\u003E et la prise de décision sont au cœur du fonctionnement des agents d’IA. Ils récupèrent et ingèrent des données issues de diverses sources, les traitent en temps réel et font appel aux outils appropriés pour guider leurs décisions. Cette adaptabilité permet non seulement aux agents d’IA d’accomplir des tâches, mais aussi d’affiner leurs actions en fonction de l’évolution des conditions et des interactions avec les utilisateurs. Elle leur confère une grande polyvalence dans des domaines tels que le service client et l’analyse de données, où la dynamique des responsabilités et l’adaptabilité sont cruciales.\u003C/p\u003E\r\n\u003Cp\u003EL’intégration des agents d’IA aux plateformes cloud et aux \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-lakes-and-data-lake-architecture/\"\u003Edata lakes\u003C/a\u003E étend encore leur potentiel. Les plateformes cloud fournissent l’évolutivité de calcul nécessaire pour traiter de vastes jeux de données au‑delà des limites de l’infrastructure locale. Cette connexion permet aux agents d’IA d’accéder à des données stockées dans de vastes data lakes et de les analyser pour en extraire des informations significatives. Cette intégration facilite les flux de travail et permet aux entreprises de davantage valoriser leurs données, ce qui favorise l’innovation et améliore les processus décisionnels.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_1241045613"]},"container_copy_copy_":{"columnClassNames":{"container_copy_copy__92287879":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__674278643":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__557343380":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__893034341":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__502120022":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1291069223":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"rel","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy_":{"columnClassNames":{"container_copy_copy__238270504":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"machine","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy_":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"challenges","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-0f500dcd86","additionalClasses":"headline-decoration","type":"heading2","lines":["Défis posés par les agents d’IA"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_1241045613":{"id":"text-f30ff834de","text":"\u003Cp\u003EPour évoluer efficacement, les agents d’IA ont besoin d’un accès sécurisé aux données d’entreprise et d’une gouvernance unifiée, ce qui garantit leur respect des politiques, la récupération d’informations précises et des résultats fiables. Cependant, le déploiement de systèmes d’IA fiables à grande échelle s’accompagne de défis :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPrécision : \u003C/b\u003Eles applications d’entreprise, en particulier dans les domaines de la finance et de l’ingénierie, ont une marge d’erreur très faible.\u003Cbr\u003E\r\n\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAccès aux données gouvernées : \u003C/b\u003Eles agents doivent exploiter des sources de données structurées (tables, vues) et non structurées (texte, audio), qui sont souvent silotées.\u003Cbr\u003E\r\n\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EConfiance et sécurité :\u003C/b\u003E à mesure que l’adoption de l’IA se généralise, assurer la conformité aux politiques de \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-governance/\"\u003Egouvernance des données\u003C/a\u003E devient de plus en plus complexe.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELes agents d’IA seront bientôt incontournables en entreprise, car ils permettront d’augmenter la productivité dans des domaines tels que l’assistance client, les opérations de terrain, l’analyse et l’ingénierie. Pour garantir une valeur et une évolutivité maximales à moindre risque, les flux de travail des agents d’IA devront garantir précision, efficacité et gouvernance à chaque étape. \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["title_v2","text_copy_1241045613"]},"container_copy_copy__238270504":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"solve","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-f6a15cbf6f","additionalClasses":"headline-decoration","type":"heading2","lines":["Comment les agents d’IA résolvent les problèmes : des informations exploitables à l’adaptation"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_1241045613":{"id":"text-f99db4d347","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EDétection :\u003C/b\u003E les agents définissent la tâche ou le problème à résoudre, puis vérifient et collectent des données auprès des sources disponibles les plus pertinentes, fiables, précises et à jour.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ERaisonnement\u003C/b\u003E : les agents traitent et interprètent les données, en s’appuyant souvent sur un LLM, pour comprendre le contexte et les exigences et prendre des décisions éclairées.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPlanification\u003C/b\u003E : à partir des informations des agents de raisonnement, ils élaborent des plans d’action pour atteindre l’objectif.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECoordination\u003C/b\u003E : les agents partagent leurs plans avec les utilisateurs ou les systèmes pour assurer l’alignement et la prise de décision collaborative.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAction\u003C/b\u003E : les agents mettent en œuvre le plan et exécutent les actions nécessaires.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EApprentissage et adaptation \u003C/b\u003E: les agents évaluent les résultats et intègrent les feedbacks à prendre en compte, déterminent les réussites et les axes d’amélioration, et affinent les processus pour les tâches futures.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_1241045613"]}},":itemsOrder":["container_copy_copy_","container_copy_copy__238270504"]},"container_copy_copy__502120022":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"benefits","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-e0de72749e","additionalClasses":"headline-decoration","type":"heading2","lines":["Avantages de l’utilisation d’agents d’IA"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-43049c1802","text":"\u003Cp\u003ELes entreprises qui adoptent rapidement l’IA agentique sont en passe d’en tirer des avantages considérables. En voici quelques exemples : \u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EEfficacité et productivité accrues\u003C/b\u003E : les agents d’IA excellent dans l’automatisation des tâches répétitives et l’optimisation des flux de travail. Ils permettent ainsi aux collaborateurs de se consacrer à des projets stratégiques et à la résolution créative de problèmes. Cela se traduit par une amélioration des performances de toutes les équipes et par des gains de temps en faveur de l’innovation et de la croissance.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAmélioration de la prise de décision et des informations\u003C/b\u003E : ces systèmes intelligents peuvent traiter et analyser de grandes quantités de données en temps réel, afin de livrer des informations exploitables qui permettent aux entreprises de prendre rapidement des décisions éclairées. Grâce à leur capacité à identifier des modèles et des tendances qui ne sont pas facilement perceptibles par les analystes humains, les agents d’IA aident les entreprises à réagir avec agilité aux évolutions du marché.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EÉconomies et optimisation des ressources\u003C/b\u003E : réduire les interventions manuelles dans les processus de données et les tâches opérationnelles permet aux entreprises de diminuer leurs frais opérationnels et d’allouer plus efficacement leurs ressources. Les entreprises peuvent ainsi non seulement améliorer leur efficacité opérationnelle, mais aussi augmenter leur rentabilité globale, ce qui leur permet de réinvestir dans la croissance et l’innovation.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EDélai de commercialisation plus court\u003C/b\u003E : grâce à leur capacité à automatiser des processus complexes et à fournir des analyses en temps réel, les agents d’IA accélèrent les cycles de développement et de déploiement des produits. Avec la simplification des opérations et la réduction des goulots d’étranglement, les entreprises accélèrent la mise sur le marché de leurs offres, gagnent en compétitivité et répondent plus vite aux urgences.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ESatisfaction et fidélisation accrues des clients\u003C/b\u003E : les agents d’IA révolutionnent la manière dont les entreprises interagissent avec leurs clients à travers des expériences personnalisées et des réponses rapides aux demandes. Ces agents analysent le comportement et les commentaires des clients afin d’adapter les stratégies et de proposer des solutions qui répondent à leurs attentes. Cette réactivité renforce la fidélisation, améliore la satisfaction et contribue à établir des relations clients plus profondes et plus durables.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"flexible_column_cont":{"id":"flexible-column-container-e1de1a71a6","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-bf09e72f3c","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"card_v2":{"id":"card-v2-eed4e38c66","modalId":"video","configurationStatus":{"configured":true,"message":""},"layoutStyle":"vertical","title":{"id":"title","type":"heading4","lines":["Andrew Ng, de Landing AI, explore l’essor des agents d’IA et du raisonnement agentique"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"#"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Regarder la vidéo"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--78ff4463-7422-4cc3-9146-b3f422f5f2d5/ai-agents-and-data.jpg?quality=85&preferwebp=true","alt":"Texas Health and Human Services","lazyEnabled":true,"height":"720","width":"1280",":type":"snowflake-site/components/image"},"type":"content-card",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2"]},"flexible_column_content_container_2":{"id":"container-71fea081fe","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{},":itemsOrder":[]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["title_v2","text_copy_1241045613","flexible_column_cont"]},"container_copy_copy__893034341":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_649454223":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1791265668":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1479724203":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_1710778900":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1365519898":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"types","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-242b730204","additionalClasses":"headline-decoration","type":"heading2","lines":["Types d’agents d’IA"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613":{"id":"text-86c9da3ed1","text":"\u003Cp\u003ELes agents d’IA se répartissent en plusieurs types en fonction de leurs fonctionnalités et de leurs applications. Il est essentiel de comprendre ces distinctions pour tirer efficacement parti de leurs fonctionnalités.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy":{"id":"title-v2-0019a44ddd","additionalClasses":"headline-decoration","type":"heading4","lines":["Agents réactifs et agents proactifs"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_1479724203":{"id":"text-d9a9c40c95","text":"\u003Cp\u003ECette classification fondamentale différencie les agents en fonction de leur façon de réagir à leur environnement. Les \u003Cb\u003Eagents réactifs\u003C/b\u003E agissent instantanément, en réponse aux stimuli environnementaux à l’aide de règles prédéfinies ou de comportements acquis. Ils excellent dans les environnements qui nécessitent des réponses immédiates, tels que les chatbots du service client qui traitent les requêtes des utilisateurs en temps réel. En revanche, les \u003Cb\u003Eagents proactifs\u003C/b\u003E anticipent les besoins futurs et agissent avant qu’on ne leur demande. Par exemple, un moteur de recommandation basé sur l’IA peut suggérer des produits en fonction du comportement passé d’un utilisateur, ce qui améliore l’engagement de ce dernier.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_1710778900":{"id":"title-v2-f0c05d1c06","additionalClasses":"headline-decoration","type":"heading4","lines":["Agents autonomes ou semi‑autonomes"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_1791265668":{"id":"text-e68f7ea4fb","text":"\u003Cp\u003EUne autre distinction importante concerne le degré d’indépendance avec lequel les agents d’IA fonctionnent. Les \u003Cb\u003Eagents autonomes\u003C/b\u003E agissent de manière indépendante et prennent des décisions sans intervention humaine. Conçus pour une automatisation à grande échelle, ces agents sont souvent déployés dans des applications à haut risque et à forte intensité de données. Par exemple, les systèmes de trading automatisés sur les marchés financiers fonctionnent à des vitesses inégalées pour exécuter des transactions optimales basées sur des données en temps réel, sans attendre de contribution humaine. En revanche, les \u003Cb\u003Eagents semi‑autonomes \u003C/b\u003Etravaillent aux côtés d’opérateurs humains, pour améliorer les processus décisionnels par le biais d’informations et de suggestions, sous supervision humaine. Par exemple, les systèmes de gestion de la supply chain utilisent des agents semi‑autonomes pour repérer les perturbations ou les besoins en stocks, ce qui permet aux équipes de prendre plus rapidement des décisions éclairées.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_649454223":{"id":"title-v2-a895794314","additionalClasses":"headline-decoration","type":"heading4","lines":["Agents spécifiques à un domaine "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_1365519898":{"id":"text-2644230e8c","text":"\u003Cp\u003ELes agents d’IA adaptés à des cas d’usage spécifiques à un secteur prouvent à quel point la spécialisation favorise l’innovation et la résolution de problèmes à grande échelle. Par exemple, dans le secteur de la santé, les agents d’IA contribuent au diagnostic des maladies par l’analyse en temps réel des dossiers des patients, des données d’imagerie et des tendances. Ils permettent des diagnostics plus rapides et plus précis, ce qui améliore le pronostic des patients. Dans le secteur financier, les agents de détection des fraudes surveillent et analysent les données transactionnelles afin d’identifier les anomalies, en vue de protéger les entreprises et les consommateurs contre les menaces financières. Dans le secteur de l’industrie, les agents de maintenance prédictive analysent les données des machines, prévoient les pannes éventuelles et recommandent des mesures préventives pour éviter les temps d’arrêt.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy","text_copy_1241045613_1479724203","title_v2_copy_1710778900","text_copy_1241045613_1791265668","title_v2_copy_649454223","text_copy_1241045613_1365519898"]},"container_copy_copy__557343380":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1479724203":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"what-are","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-0bcb290c0f","additionalClasses":"headline-decoration","type":"heading2","lines":["Que sont les agents de données ?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613":{"id":"text-cdb4979595","text":"\u003Cp\u003EUn \u003Cb\u003Eagent de données d’IA\u003C/b\u003E, ou \u003Cb\u003Eagent de données alimenté par des LLM\u003C/b\u003E, est un système intelligent qui combine de grands modèles de langage (LLM) avec des flux de travail de data engineering et d’analyse pour automatiser et simplifier des tâches liées aux données. Contrairement aux agents de données traditionnels, qui suivent des règles ou des scripts prédéfinis, les agents de données d’IA peuvent interpréter des instructions en langage naturel, interagir avec des bases de données, générer ou modifier des requêtes. Ils sont également capables d’effectuer des tâches comme la découverte, la transformation, l’enrichissement et les contrôles de qualité des données, le tout via des interfaces conversationnelles ou une prise de décision autonome. Ces agents servent d’intermédiaires entre les humains et les systèmes de données complexes, ce qui réduit les barrières techniques et accélère l’obtention d’informations.\u003C/p\u003E\n\u003Cp\u003EGrâce aux capacités de raisonnement et de compréhension contextuelle des LLM, les agents de données d’IA traitent des demandes plus nuancées, déduisent les intentions et s’adaptent de manière dynamique aux besoins. Ils peuvent aider à générer des requêtes SQL à partir de prompts en langage naturel, détecter des anomalies dans les données, recommander des transformations ou même orchestrer certaines parties d’un pipeline de données. Ils constituent ainsi des outils performants dans les écosystèmes de données modernes, en particulier pour les équipes qui souhaitent démocratiser l’accès aux données, améliorer la productivité et renforcer la collaboration entre les utilisateurs techniques et non techniques.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-top-padding-extra-small"},"title_v2_copy":{"id":"title-v2-ce3b027cf5","additionalClasses":"headline-decoration","type":"heading3","lines":["Agents d’IA et agents de données : principales différences"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_1479724203":{"id":"text-f27cd83ae2","text":"\u003Cp\u003EUn \u003Cb\u003Eagent de données\u003C/b\u003E alimenté par des LLM est un type spécialisé d’outil d’IA spécialement conçu pour aider les utilisateurs à exploiter les données. Il utilise de grands modèles de langage (LLM) de fournisseurs tels qu’OpenAI (GPT), Anthropic, Claude et d’autres pour comprendre les commandes en langage naturel et les transformer en tâches de données : rédaction de requêtes SQL, synthèse de jeux de données, recherche d’anomalies dans les données ou soutien à la création de pipelines de données. Ces agents sont conçus pour simplifier et automatiser les \u003Cb\u003Etâches liées aux données\u003C/b\u003E afin de faciliter l’interaction des utilisateurs non techniques avec des systèmes de données complexes.\u003C/p\u003E\n\u003Cp\u003EUn \u003Cb\u003Eagent d’IA\u003C/b\u003E plus généraliste, en revanche, peut effectuer un éventail de tâches beaucoup plus large, au‑delà des données, comme piloter un robot, jouer à un jeu vidéo, planifier des réunions ou interagir dans un chatbot de service client. Les agents d’IA peuvent utiliser différents types d’IA (pas seulement des LLM) et se spécialisent souvent dans la prise de décision, la planification, l’apprentissage de l’environnement ou l’interaction entre les systèmes. En résumé : les agents de données alimentés par des LLM sont spécifiques à une tâche et axés sur les données, tandis que les agents d’IA sont des systèmes plus généralistes, orientés sur des objectifs, qui peuvent intervenir dans de nombreux domaines plus ou moins complexes.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy","text_copy_1241045613_1479724203"]},"container_copy_copy__92287879":{"columnClassNames":{"text_copy_1241045613_384307662":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1479724203":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_671876296":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"uses","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-912a65a272","additionalClasses":"headline-decoration","type":"heading2","lines":["Utilisations pratiques des agents d’IA en entreprise "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613":{"id":"text-71933320ba","text":"\u003Cp\u003ELes agents d’IA se sont rapidement imposés comme des outils révolutionnaires dans de nombreux secteurs. Ils exploitent la puissance des données pour fournir des informations exploitables et améliorer l’efficacité opérationnelle. Ces agents, alimentés par des algorithmes avancés, accélèrent et améliorent la précision des processus complexes. Voici comment les agents d’IA redéfinissent des secteurs clés de l’entreprise.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy":{"id":"title-v2-3c7b02d53b","additionalClasses":"headline-decoration","type":"heading4","lines":["Analyse de données et Business Intelligence"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_1479724203":{"id":"text-b7ac577605","text":"\u003Cp\u003ELes agents d’IA représentent des atouts essentiels dans les domaines de \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-analytics/\"\u003El’analyse des données\u003C/a\u003E et de la \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/ai-for-business-intelligence/\"\u003EBusiness Intelligence\u003C/a\u003E. Ils permettent aux entreprises de traiter de grandes quantités d’informations avec une efficacité inégalée. En passant rapidement au crible de grands jeux de données, ces agents identifient des schémas et des tendances dont la découverte prendrait beaucoup plus de temps à des analystes humains. Par exemple, dans le retail, les agents peuvent analyser l’historique des ventes ainsi que les comportements des clients en temps réel pour prédire les tendances d’achat futures et optimiser les niveaux de stock. Intégrés aux plateformes data, les agents fournissent également aux entreprises des informations en temps réel pour éclairer leur prise de décision et s’adapter aux évolutions du marché. Par exemple, une institution financière peut utiliser l’analyse basée sur l’IA pour détecter les anomalies du marché et orienter ses stratégies d’investissement.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy":{"id":"title-v2-9c9f1065d5","additionalClasses":"headline-decoration","type":"heading4","lines":["Service et assistance à la clientèle "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_384307662":{"id":"text-2a8321ae96","text":"\u003Cp\u003ELes agents d’IA des plateformes de service client ont révolutionné la façon dont les entreprises interagissent avec leurs clients. Ces agents excellent dans le traitement simultané d’un volume élevé de requêtes et fournissent des réponses instantanées et précises 24 heures sur 24. L’automatisation des requêtes courantes permet aux agents de libérer les représentants du service client afin qu’ils puissent se consacrer à des situations plus complexes ou plus délicates sur le plan émotionnel. Cette approche hybride améliore non seulement l’efficacité du service, mais aussi la satisfaction et la fidélité des clients. Par ailleurs, l’analyse des interactions avec les clients permet à ces agents de mettre en évidence les points faibles récurrents. Les entreprises sont donc en mesure de répondre de manière proactive aux préoccupations et d’affiner leurs stratégies en matière d’expérience client.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-176924335d","additionalClasses":"headline-decoration","type":"heading4","lines":["Automatisation et amélioration des processus "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_671876296":{"id":"text-ec85d93108","text":"\u003Cp\u003EL’automatisation, alimentée par les agents d’IA, est devenue un moteur de transformation opérationnelle. Ces agents simplifient les tâches répétitives et chronophages, de la saisie des données aux contrôles de conformité, et assurent la constance et la précision. Résultat : les collaborateurs peuvent se consacrer à des initiatives à plus forte valeur ajoutée. Les agents d’IA accélèrent également l’innovation car ils réduisent le délai de mise sur le marché des nouveaux produits ou services. Pour une entreprise du secteur de l’industrie, cela peut se traduire par l’utilisation d’agents pour optimiser les calendriers de production ou identifier les goulots d’étranglement dans les flux de travail, afin de garantir une livraison plus rapide sans compromettre la qualité.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy","text_copy_1241045613_1479724203","title_v2_copy_copy","text_copy_1241045613_384307662","title_v2_copy_copy_c","text_copy_1241045613_671876296"]},"container_copy_copy__674278643":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1479724203":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"industry","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-f2d4766ddd","additionalClasses":"headline-decoration","type":"heading2","lines":["Applications des agents d’IA par secteur d’activité"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613":{"id":"text-022843a23c","text":"\u003Cp\u003ELes agents d’IA remodèlent les secteurs d’activité et résolvent des défis complexes par l’automatisation des processus, la prévision des tendances et la personnalisation des expériences utilisateurs. Voici quelques exemples d’applications d’agents d’IA dans différents secteurs pour favoriser la résolution des problèmes et l’excellence opérationnelle.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_1241045613_1479724203":{"id":"text-b4c299ea19","text":"\u003Ctable\u003E\n\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ESECTEUR\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ELES AGENTS D’IA PEUVENT…\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EMédias\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EIdentifier les performances publicitaires, attribuer les dépenses en conséquence et tester de nouveaux messages pour améliorer le ROI\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ERecommander du contenu aux utilisateurs en fonction de leurs habitudes de consommation et donner des idées de programmation future basées sur des analyses d’audience \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EServices financiers\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EExaminer le marché et ajuster la répartition du portefeuille d’un client afin d’optimiser les tendances et d’atténuer les risques \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EIdentifier les signes précurseurs de défaut de paiement chez un emprunteur et mettre en œuvre un plan de remboursement sur mesure pour éviter ce problème\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003ESanté et sciences de la vie\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003ESurveiller et analyser à distance les données des patients en temps réel grâce à des appareils portables, afin d’alerter les prestataires de soins en cas de changements ou d’éventuels futurs problèmes\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EConcevoir et tester de nouvelles molécules pour des maladies afin d’identifier de potentiels médicaments\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EIndustrie\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003ESurveiller les performances des équipements, prévoir les défaillances et répartir les équipes de maintenance en temps réel\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EOptimiser les stocks en temps réel en réponse aux fluctuations de la demande ou aux perturbations météorologiques\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003ERetail\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EPrédire les tendances de la demande, ajuster les niveaux de stock et les prix en temps réel\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EProposer en temps voulu des recommandations de produits personnalisées et une résolution plus rapide des problèmes pour les acheteurs \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003ETechnologie\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003ESurveiller activement les réseaux pour identifier les activités inhabituelles, afin de détecter et d’atténuer les menaces en temps réel\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EPrototyper des modèles ou des codes, en les itérant pour améliorer les résultats\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003ETélécommunications\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EPrédire les charges de trafic et gérer l’allocation de bande passante en conséquence\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EIdentifier les clients susceptibles de partir et mettre en place des stratégies de fidélisation\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_1241045613","text_copy_1241045613_1479724203"]},"container_copy_copy__1291069223":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn 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Mais ce potentiel immense s’accompagne de responsabilités tout aussi importantes. Les questions relatives à la confidentialité des données, aux biais algorithmiques et à l’impact sur la main‑d’œuvre exigent une approche équilibrée et réfléchie. La transparence et la confiance sont non négociables : il s’agit de créer des systèmes qui apportent de la valeur tout en respectant les principes sociétaux. Grâce à des directives éthiques claires et des frameworks réglementaires évolutifs, nous pouvons faire de l’IA un moteur, capable non seulement de s’adapter à notre monde, mais aussi de le faire progresser. 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