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aem-GridColumn--default--12"},":items":{"flexible_column_cont":{"id":"flexible-column-container-43189f848a","propertiesId":"hub-hero-breadcrumbs","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-9b698e9898",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"breadcrumb":{"id":"breadcrumb-0877fe764d","items":[{"id":"breadcrumb-0877fe764d-item-8fdfff6b6b","link":{"valid":true,"url":"/fr/artificial-intelligence/"},"active":false,"current":false,"title":"Intelligence artificielle",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"},{"id":"breadcrumb-0877fe764d-item-87b7628be6","link":{"valid":true,"url":"/fr/artificial-intelligence/ai-governance/"},"active":false,"current":false,"title":"Gouvernance de l’IA",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"},{"id":"breadcrumb-0877fe764d-item-ae3770e4f9","link":{"valid":true,"url":"/fr/artificial-intelligence/ai-governance/control-plane/"},"active":true,"current":true,"title":"Plan de contrôle agentique",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_939100716":{"id":"flexible-column-container-e25e492b96","propertiesId":"hub-hero","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-ca03924e06",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-669003366c","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Le plan de contrôle agentique : piloter les agents d'IA à grande échelle"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-e812ab0baf","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003ELes agents d’IA créent de nouvelles exigences en matière de coordination, de surveillance et de responsabilité. Un plan de contrôle agentique offre aux entreprises un moyen de gérer la façon dont les agents accèdent au contexte, utilisent les outils, respectent les politiques et agissent à travers les systèmes business.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-f747ff1e9a",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-7333d111f1","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?preferwebp=true&quality=85","alt":"Laurie MacPherson","lazyEnabled":true,"isLcpImage":true,"width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie MacPherson","Technical Writer, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_copy":{"id":"content-chip-fb87dd90b4","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/tracy-kabuya/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"709","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--9ad7a3c0-e23e-4370-bc36-f59a5fe02eff/img-1817.jpg?preferwebp=true&quality=85","alt":"Tracy Kabuya","lazyEnabled":true,"isLcpImage":false,"width":"709",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Tracy Kabuya","Contributrice locale, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip","content_chip_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["title_v2","text","container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-094cac8833",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-52f6bf974a","youtubeVideoId":"CtqKJV6gyGQ","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-e993c20e16","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-44e5052a0b",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-d9f3e0ef1a","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EThèmes liés à la gouvernance de l’IA :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-2e30d4e611","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/ai-compliance/\"\u003EConformité de l’IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/ai-ethics/\"\u003EÉthique de l’IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/ai-risk-management/\"\u003EGestion des risques liés à l’IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/ai-transparency/\"\u003ETransparence de l’IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003EBiais algorithmiques\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/eu-ai-act/\"\u003ERèglement européen sur l'IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/iso-42001/\"\u003EISO 42001\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/model-governance/\"\u003EGouvernance des modèles\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/responsible-ai/\"\u003EIA responsable\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-5fe2043601","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"callout__0":{"id":"text-e9c1e70b43","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EDÉFINITION DU PLAN DE CONTRÔLE AGENTIQUE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EUn plan de contrôle agentique est la couche de gouvernance et de coordination qui aide les entreprises à gérer la façon dont les agents d’IA accèdent au contexte, utilisent les outils, respectent les politiques et prennent des mesures autorisées à travers les systèmes.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-f7c24eaac9","text":"\u003Cp\u003EPendant des décennies, les logiciels traditionnels ont été construits autour de workflows prédéfinis. L’IA agentique complexifie ce modèle en introduisant de l’autonomie.\u003C/p\u003E\n\u003Cp\u003EAu sein des systèmes d’entreprise soumis à des autorisations, un \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/agents/\"\u003Eagent d’IA\u003C/a\u003E se comporte comme un acteur doté d'une autorité déléguée. Il peut lire des données gouvernées, générer du SQL, mettre à jour un workflow, modifier un système d'enregistrement ou déclencher une action dans une autre application. Par conséquent, l’entreprise doit gouverner non seulement ce qu’une application peut faire, mais aussi les nombreux chemins qu’un agent peut emprunter à travers les données, les outils et les processus métier.\u003C/p\u003E\n\u003Cp\u003ESi ces contrôles sont dispersés entre les prompts, les paramètres d’application, les comptes de service et les intégrations individuelles, l’entreprise ne dispose d’aucun moyen constant de les visualiser ou de les appliquer.\u003C/p\u003E\n\u003Cp\u003EC’est là qu’intervient le plan de contrôle agentique : une couche de coordination et de \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/\"\u003Egouvernance\u003C/a\u003E pour les systèmes agentiques. Le CEO de Snowflake, \u003Ca href=\"https://www.snowflake.com/en/blog/agentic-enterprise-control-plane/\"\u003ESridhar Ramaswamy\u003C/a\u003E, a qualifié le plan de contrôle agentique de « couche manquante » pour l’entreprise, capable de « traduire l’intelligence en actions d’entreprise autorisées ».\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-an-agent-control-plane":{"id":"title-v2-cb4fc2c9ab","additionalClasses":"anchor-title anchor-title--what-is-an-agent-control-plane","type":"heading2","lines":["Qu’est-ce qu’un plan de contrôle agentique ?"],":type":"snowflake-site/components/title-v2"},"text_what-is-an-agent-control-plane_0":{"id":"text-49c924ed48","text":"\u003Cp\u003EUn plan de contrôle agentique est la couche centralisée qui déploie, exploite, surveille et gouverne les agents d’IA à l’échelle d’une entreprise. Il offre un moyen partagé de gérer l’identité des agents, d’appliquer les politiques d’exécution, d’observer les comportements, d’enregistrer les versions, de fournir un contexte gouverné et de contrôler l’accès aux outils.\u003C/p\u003E\n\u003Cp\u003ESans une couche de gouvernance partagée, les agents ont tendance à hériter des limites de l’environnement dans lequel ils ont été créés. Chacun peut avoir ses propres journaux, son propre modèle d’accès, sa méthode d’évaluation et ses autorisations d’outils. À mesure que l'adoption se répand, l'entreprise se retrouve avec de nombreux runtimes d'agents et aucun moyen cohérent de savoir quels agents existent, à quelles données ils ont accédé, quelles actions ils ont tentées ou quelles politiques ont gouverné ces actions.\u003C/p\u003E\n\u003Cp\u003EUn plan de contrôle agentique offre aux entreprises un moyen de gouverner ces agents en tant que couche d’exploitation connectée : en alignant l’identité, les politiques, le contexte, l’accès aux outils, l’exécution et l’auditabilité sur l’ensemble du parc d’agents.\u003C/p\u003E\n\u003Cp\u003ESridhar Ramaswamy explique l’importance du plan de contrôle agentique de la manière suivante : « Pour exploiter efficacement la technologie agentique, les entreprises ont besoin de plus que de simples modèles et applications. Elles ont besoin d’une couche de coordination, d’un plan de contrôle central qui aligne l’intelligence, les données d’entreprise, les politiques et l’exécution à l’échelle de l’organisation pour favoriser la cohésion agentique. »\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_what-is-an-agent-control-plane_0":{"id":"quote-item-ca647e0b2e","alignment":"left","layout":"simple","quoteSourceName":"Sridhar Ramaswamy","quoteSourceTitle":"CEO of Snowflake","showQuoteIcon":true,"quote":"Pour exploiter efficacement la technologie agentique, les entreprises ont besoin de plus que de simples modèles et applications. Elles ont besoin d’une couche de coordination, d’un plan de contrôle central qui aligne l’intelligence, les données d’entreprise, les politiques et l’exécution à l’échelle de l’organisation pour favoriser la cohésion agentique.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_what-is-an-agent-control-plane_1":{"id":"text-4d8ad43008","text":"\u003Cp\u003ELe terme « plan de contrôle » vient de l’architecture système, où une couche gère la façon dont le travail est configuré et gouverné, tandis qu’une autre couche effectue le travail.\u003C/p\u003E\n\u003Cp\u003EDans les systèmes distribués, le plan de données est l’endroit où le travail s’effectue : les requêtes sont traitées, les données se déplacent, les fonctions s’exécutent et les applications répondent. Le plan de contrôle gouverne la façon dont ce travail est configuré et géré. Pour les agents d’IA, le plan de données est l’endroit où chaque agent raisonne, appelle des outils, récupère du contexte, écrit des résultats et accomplit des tâches. Le plan de contrôle se situe au-dessus de ces environnements d’exécution d’agents, en appliquant des contrôles d’identité, de politique, de monitoring et de cycle de vie à l’ensemble d’entre eux.\u003C/p\u003E\n\u003Cp\u003ECette séparation est d’autant plus importante lorsque les entreprises commencent à utiliser différents modèles, frameworks et approches de déploiement. Un plan de contrôle indépendant du prestataire offre à l’entreprise une couche de gouvernance constante à travers ces systèmes, plutôt que de lier la politique et l’auditabilité à un seul prestataire de modèles ou framework d’agents.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_what-is-an-agent-control-plane_0":{"id":"card-v2-cdd4fc1d13","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Simon AI"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire l’étude de cas complète"},"image":{"id":"image","height":"360","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c6933908-8a65-4f02-b6df-48566e9c4633/simon-ai-logo-nav.png?preferwebp=true&quality=85","alt":"Simon AI logo","lazyEnabled":true,"isLcpImage":false,"width":"840",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ESimon AI utilise Snowflake Cortex AI et Snowpark Container Services pour alimenter des agents d’IA composables qui aident les spécialistes du marketing à offrir une personnalisation contextuelle à grande échelle, sans déplacer les données ni compromettre la gouvernance. S’exécutant directement dans l’AI Data Cloud de Snowflake, les agents de Simon offrent aux spécialistes du marketing un accès gouverné et en temps réel aux données structurées et non structurées, aidant ainsi les clients à créer des audiences contextuellement pertinentes 90 % plus rapidement et à identifier plus de 100 nouvelles cohortes de clients à forte valeur ajoutée.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_what-an-agent-control-plane-does-core-functions":{"id":"title-v2-19d788a163","additionalClasses":"anchor-title anchor-title--what-an-agent-control-plane-does-core-functions","type":"heading2","lines":["Ce que fait un plan de contrôle agentique : fonctions principales"],":type":"snowflake-site/components/title-v2"},"text_what-an-agent-control-plane-does-core-functions_0":{"id":"text-599ca79481","text":"\u003Cp\u003EParce que les agents combinent raisonnement, récupération et utilisation d'outils, le plan de contrôle doit coordonner ce qui se passe avant qu'une action soit exécutée : qui ou quoi agit, quel contexte est disponible, quelles politiques s'appliquent, quand un jugement humain est requis et comment le résultat est enregistré par la suite.\u003C/p\u003E\n\u003Ch3\u003EAppliquer la gouvernance et les politiques à l’exécution\u003C/h3\u003E\n\u003Cp\u003EUne politique d’agent a une valeur limitée si elle n’existe que dans un document de conception ou un modèle de prompt. Au moment de l’exécution, le plan de contrôle doit évaluer si un agent doit récupérer une table, appeler un outil externe, envoyer un message, mettre à jour un enregistrement ou faire appel à un réviseur humain.\u003C/p\u003E\n\u003Cp\u003ECes politiques peuvent dépendre de l’utilisateur, de l’agent, de la classification des données, de l’outil appelé, de l’action demandée et du niveau de risque du workflow. Par exemple, un agent du service client peut être autorisé à résumer l’historique d’un dossier sans approbation, mais un remboursement, une modification de compte ou la divulgation d’informations sensibles nécessitent des vérifications supplémentaires. La gouvernance à l’exécution offre à l’entreprise un moyen de contraindre le comportement de l’agent lorsque celui-ci passe de l’analyse à l’action.\u003C/p\u003E\n\u003Ch3\u003EDonner aux agents une identité vérifiable\u003C/h3\u003E\n\u003Cp\u003EUn agent d’entreprise a besoin d’une identité distincte de celle de l’utilisateur, du modèle et de l’application qui en assure l’hébergement. Sans cette identité, il est difficile de répondre aux questions d’audit de base : Quel agent a accédé aux données ? Quel utilisateur a autorisé la session ? Quel compte de service a appelé l’outil ? Quelle politique a autorisé l’action ?\u003C/p\u003E\n\u003Cp\u003EL’identité de l’agent prend en charge les approches \u003Ca href=\"https://www.cisa.gov/topics/cybersecurity-best-practices/zero-trust\" target=\"_blank\"\u003EZero Trust\u003C/a\u003E pour les systèmes d’IA. Plutôt que d'accorder un accès permanent aux outils ou aux jeux de données, l'entreprise peut exiger un accès juste-à-temps fondé sur le rôle de l'agent, les droits de l'utilisateur, la tâche et l'état actuel des politiques. Un agent d'approvisionnement, par exemple, peut être autorisé à consulter les données des fournisseurs approuvés lors d'un workflow de sourcing, mais pas à accéder à des référentiels de contrats sans rapport ni à envoyer des bons de commande sans approbation.\u003C/p\u003E\n\u003Cp\u003EL’attestation dépend également de l’identité. Lorsqu’un agent possède une identité vérifiable, le système peut créer un enregistrement lié à cette identité. Cela est essentiel pour l’examen des incidents, les rapports de \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/ai-compliance/\"\u003Econformité\u003C/a\u003E et l’évaluation continue.\u003C/p\u003E\n\u003Ch3\u003EStandardiser l’observabilité tout au long du cycle de vie de l’agent\u003C/h3\u003E\n\u003Cp\u003EL’observabilité des agents fournit aux équipes les traces, les métriques et les évaluations nécessaires pour inspecter le comportement des agents. Le plan de contrôle consomme cette télémétrie et l’applique aux agents, aux workflows et aux environnements.\u003C/p\u003E\n\u003Cp\u003EPour un workflow agentique, une trace peut inclure des étapes de récupération, des appels d'outils, des artefacts intermédiaires de raisonnement, des contrôles de politiques, des approbations, des nouvelles tentatives, des échecs et des résultats finaux. Au fil du temps, les traces montrent si l’agent utilise trop d’outils, récupère un contexte non pertinent, fait trop souvent appel à un humain ou produit des sorties qui échouent à l’évaluation.\u003C/p\u003E\n\u003Cp\u003EUn plan de contrôle standardise la façon dont ces informations sont collectées et examinées. Au lieu que chaque framework d’agent produise une vue de surveillance différente, l’entreprise obtient un enregistrement constant du comportement tout au long du cycle de vie de l’agent : développement, tests, déploiement, surveillance à l’exécution et retrait.\u003C/p\u003E\n\u003Ch3\u003EGérer le cycle de vie, les versions et le contexte gouverné\u003C/h3\u003E\n\u003Cp\u003ELe comportement de l’agent évolue au fil du temps à mesure que les schémas d’outils, les fournisseurs de modèles, les règles business ou les sources de données changent autour de lui. Sans gestion du cycle de vie, les équipes peinent davantage à retracer quelle version de l'agent s'est exécutée, quel contexte a été utilisé et si les contrôles ont été appliqués, notamment lorsque les agents opèrent à l'échelle de plusieurs équipes.\u003C/p\u003E\n\u003Cp\u003ELe plan de contrôle offre un espace pour enregistrer les agents, suivre les versions, affecter des propriétaires et gérer le statut de déploiement. Il aide également à gouverner le contexte utilisé par les agents. Dans de nombreux workflows d’entreprise, l’entrée la plus importante est le contexte métier gouverné qui définit les clients, les produits, les comptes, les politiques, les métriques et l’état des processus.\u003C/p\u003E\n\u003Cp\u003EUn agent commercial qui répond à des questions sur la santé du pipeline, par exemple, a besoin de la définition actuelle d’un pipeline qualifié, d’un accès aux données de compte approuvées et d’une visibilité sur les autorisations de l’utilisateur. Lorsque le contexte est fourni via le plan de contrôle, les agents peuvent travailler à partir de sources gouvernées plutôt que de copies improvisées, d’extraits obsolètes ou de fichiers fournis par l’utilisateur.\u003C/p\u003E\n\u003Cp\u003ELeo Rodriguez, Principal Product Marketing Manager, IA/ML, chez Snowflake, pose clairement le problème du contexte : « Les modèles d’IA sont intelligents. Le problème est qu’ils n’ont pas automatiquement le contexte stratégique nécessaire pour que l’entreprise leur fasse confiance. Pour obtenir des réponses précises, vous devez fournir au modèle les définitions, les relations, les autorisations et les sources de confiance dont il a besoin pour raisonner sur les données de l’entreprise. »\u003C/p\u003E\n\u003Ch3\u003EContrôler l’accès aux modèles et aux outils via des passerelles\u003C/h3\u003E\n\u003Cp\u003EÀ mesure que les agents interagissent avec les modèles et les outils, les passerelles deviennent un élément pratique du plan de contrôle.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EUne passerelle LLM gère l’accès aux modèles :\u003C/strong\u003E Une passerelle LLM peut acheminer les requêtes entre les fournisseurs, appliquer des contrôles de clés et de budget, mettre en place des garde-fous, capturer l’utilisation et standardiser la journalisation. Pour les organisations utilisant plusieurs modèles, cette couche évite que l’accès aux modèles ne devienne un ensemble d’identifiants déconnectés intégrés dans diverses applications.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EUne passerelle MCP gère l’accès des agents aux outils.\u003C/strong\u003E Le \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-snowflake-mcp-server/\"\u003EModel Context Protocol\u003C/a\u003E (MCP) offre aux agents un moyen structuré de se connecter aux outils et aux systèmes externes, mais cette connectivité élargit également la surface d’application des politiques. Une passerelle peut réguler l'accès aux outils, inspecter les actions demandées, appliquer les droits et enregistrer ce que l'agent a tenté de faire.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EEnsemble, ces passerelles aident le plan de contrôle à gouverner les deux aspects de l’exécution des agents : les appels de modèles qui produisent le raisonnement et les appels d’outils qui transforment ce raisonnement en action.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003ERegardez Andrew Ng, chercheur de premier plan en IA, explorer l’essor des agents d’IA et du raisonnement agentique :\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-an-agent-control-plane-does-core-functions_0":{"id":"embed-597aefa4bc","youtubeVideoId":"KrRD7r7y7NY","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_control-plane-vs-orchestration-vs-observability":{"id":"title-v2-8ea647593b","additionalClasses":"anchor-title anchor-title--control-plane-vs-orchestration-vs-observability","type":"heading2","lines":["Plan de contrôle, orchestration et observabilité"],":type":"snowflake-site/components/title-v2"},"text_control-plane-vs-orchestration-vs-observability_0":{"id":"text-18830fef6b","text":"\u003Cp\u003ELe plan de contrôle, l’orchestration et l’\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/observability/\"\u003Eobservabilité\u003C/a\u003E sont étroitement liés, mais ils résolvent des problèmes différents.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EL’orchestration coordonne l’exécution :\u003C/b\u003E Dans un workflow d’agent, l’orchestration détermine quels agents ou outils s’exécutent, dans quel ordre, avec quelles entrées et selon quelle logique de branchement. Un système multi-agent pour l’intégration des clients peut assigner un agent à la validation des données de compte, un autre à la vérification des exigences de conformité et un troisième à la rédaction d’un plan de lancement. La couche d’orchestration gère la séquence et les transferts.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EL’observabilité enregistre ce qui s’est passé :\u003C/b\u003E Elle capture les traces, les métriques, les évaluations, les appels d’outils et les résultats afin que les équipes puissent inspecter le comportement des agents. Lorsqu’un workflow \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/rag/\"\u003ERAG\u003C/a\u003E renvoie une réponse non prise en charge ou qu’un agent tourne en boucle sur des appels d’outils répétés, l’observabilité fournit aux développeurs et aux opérateurs les preuves nécessaires pour diagnostiquer le problème.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ELe plan de contrôle gouverne ces deux couches :\u003C/b\u003E Il gère l’identité, les politiques, l’auditabilité, l’accès et les contrôles de cycle de vie, quel que soit le framework d’orchestration qui coordonne le workflow ou le système d’observabilité qui collecte la télémétrie. En pratique, le plan de contrôle peut utiliser les données d’observabilité pour appliquer des décisions de gouvernance, et il peut appliquer des politiques aux workflows orchestrés avant, pendant et après l’exécution.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-agent-control-planes-matter-for-enterprise-ai":{"id":"title-v2-6b4d94f29c","additionalClasses":"anchor-title anchor-title--why-agent-control-planes-matter-for-enterprise-ai","type":"heading2","lines":["Pourquoi les plans de contrôle des agents sont essentiels pour l’IA d’entreprise"],":type":"snowflake-site/components/title-v2"},"text_why-agent-control-planes-matter-for-enterprise-ai_0":{"id":"text-41422b9afd","text":"\u003Cp\u003ELes agents d’IA d’entreprise opèrent dans des environnements où l’accès aux données, les processus stratégiques et les obligations de conformité existent déjà, et ils doivent respecter ces conditions.\u003C/p\u003E\n\u003Cp\u003ESi les agents agissent au sein des systèmes d’entreprise, ils doivent être gouvernés comme des acteurs de ces systèmes, et non comme des interfaces passives superposées. Par exemple, un analyste humain peut avoir l’autorisation de consulter les données de ventes régionales, mais pas les conditions financières au niveau du client, et un data engineer peut être autorisé à modifier un pipeline en développement, mais pas à le déployer en production sans révision. Lorsque les agents commencent à assister ces mêmes workflows, le modèle de contrôle doit suivre l’action.\u003C/p\u003E\n\u003Cp\u003ELe risque principal n’est pas que les agents deviennent soudainement incontrôlables. Dans de nombreux cas, le risque immédiat est l’incohérence. Un agent consigne des traces complètes, tandis qu’un autre ne stocke que les résultats finaux. Un workflow utilise des autorisations basées sur les rôles, tandis qu’un autre stocke des identifiants d’outils à large portée. Au fil du temps, ces différences rendent plus difficile de faire évoluer l’IA agentique en toute confiance.\u003C/p\u003E\n\u003Cp\u003EPour Rodriguez, le plan de contrôle est d’autant plus important lorsque les agents franchissent les frontières que les entreprises ont traditionnellement gouvernées séparément : les données, les modèles et les outils tiers :\u003C/p\u003E\n\u003Cp\u003E« La capacité la plus sous-estimée du plan de contrôle est de réunir la gouvernance des données et de l’IA. De nombreuses entreprises traitent le plan de contrôle des données et le plan de contrôle de l’IA comme des périmètres distincts. Mais à mesure que les agents commencent à utiliser les données d’entreprise, les modèles et les outils tiers dans le même workflow, ces contrôles doivent converger. »\u003C/p\u003E\n\u003Cp\u003EUn plan de contrôle offre aux organisations une architecture plus gouvernable. Les nouveaux agents peuvent être enregistrés avec leurs propriétaires et leurs versions. L’accès aux outils peut être médiatisé plutôt qu’intégré. Les politiques peuvent être appliquées à l’exécution au lieu d’être copiées dans les prompts. Les enregistrements d’audit peuvent suivre l’activité des agents à travers les systèmes. Lorsqu’un incident ou une question de conformité survient, l’entreprise dispose d’un moyen de reconstituer ce qui s’est passé.\u003C/p\u003E\n\u003Cp\u003EPour les secteurs réglementés, l’enregistrement peut devenir aussi important que le résultat lui-même. Avant qu’une organisation réglementée puisse bénéficier de ce que les agents d’IA peuvent faire, elle a besoin de preuves de contexte, d’autorisation, d’application des politiques et d’examen humain le cas échéant.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-agent-control-planes-matter-for-enterprise-ai_0":{"id":"text-12082b04a3","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EPIÈGE COURANT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EUne erreur courante consiste à traiter le plan de contrôle comme un simple tableau de bord d’observabilité supplémentaire. L’observabilité montre ce qui s’est passé, mais le plan de contrôle aide à déterminer ce que les agents sont autorisés à faire avant, pendant et après l’exécution.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_snowflake-the-control-plane-for-the-agentic-enterprise":{"id":"title-v2-60ae2202bc","additionalClasses":"anchor-title anchor-title--snowflake-the-control-plane-for-the-agentic-enterprise","type":"heading2","lines":["Snowflake : le plan de contrôle pour l’entreprise agentique"],":type":"snowflake-site/components/title-v2"},"text_snowflake-the-control-plane-for-the-agentic-enterprise_0":{"id":"text-40b000d2f5","text":"\u003Cp\u003ELe plan de contrôle doit être proche des \u003Ca href=\"https://www.snowflake.com/fr/data-governance/\"\u003Edonnées gouvernées\u003C/a\u003E, du contexte et des politiques. La prochaine phase de l'IA d'entreprise repose sur la connexion de l'intelligence à des données d'entreprise de confiance et sur la traduction de cette intelligence en actions multi-étapes dans les systèmes métier où le travail s'effectue.\u003C/p\u003E\n\u003Cp\u003EDe nombreuses entreprises disposent déjà de contrôles d’accès, de politiques, d’une traçabilité et d’un contexte partagé rattachés au socle data. À mesure que les agents commencent à agir sur des informations métier, le plan de contrôle doit préserver ces contrôles plutôt que d'acheminer du contexte sensible via des flux non gouvernés ou des couches applicatives déconnectées.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://ai.snowflake.com/\"\u003ESnowflake CoWork\u003C/a\u003E offre un socle de plan de contrôle aux utilisateurs métier et aux travailleurs du savoir, via des questions-réponses gouvernées, un travail multi-étapes et des actions fondées sur des outils. \u003Ca href=\"https://www.snowflake.com/en/product/snowflake-coco/\"\u003ESnowflake CoCo\u003C/a\u003E étend cette même base aux développeurs en tant qu’agent de codage d’IA natif des données pour le data engineering, l’analytique et les workflows d’IA. Pour les développeurs, les data engineers et les équipes d’IA, cela signifie que l’assistance agentique peut fonctionner en ayant connaissance des données, des métadonnées, du contexte de compte et de la gouvernance Snowflake, plutôt que de se situer en dehors de l’environnement où le travail gouverné a déjà lieu.\u003C/p\u003E\n\u003Cp\u003EÀ mesure que les agents se connectent à des outils et systèmes externes, la gouvernance doit s’étendre au-delà des prompts et des autorisations de données pour englober les actions que les agents tentent d’entreprendre. Les passerelles MCP et autres points de contrôle similaires peuvent réguler l'accès aux outils, appliquer les droits et enregistrer ce que l'agent a tenté de faire\u003C/p\u003E\n\u003Cp\u003EL'IA d'entreprise ne pourra se déployer à grande échelle que si les agents peuvent être traités comme des acteurs gouvernés dans les processus métier. Le plan de contrôle devient la couche de connexion entre l’intelligence et l’exécution, offrant aux agents un cheminement homogène du contexte stratégique de confiance vers l’action autorisée.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_governing-the-path-from-context-to-action":{"id":"title-v2-525fa0ff60","additionalClasses":"anchor-title anchor-title--governing-the-path-from-context-to-action","type":"heading2","lines":["Gouverner le cheminement du contexte à l’action"],":type":"snowflake-site/components/title-v2"},"text_governing-the-path-from-context-to-action_0":{"id":"text-9c42d48742","text":"\u003Cp\u003EL’IA agentique modifie la portée de la gouvernance d’entreprise.  Un agent peut partir d'une demande d'utilisateur, mais le travail peut rapidement transiter par des données gouvernées, des appels d'outils, des modifications de workflows et des processus métier où les droits et l'auditabilité jouent déjà un rôle essentiel.\u003C/p\u003E\n\u003Cp\u003ELe plan de contrôle offre aux entreprises un moyen de coordonner l'activité des agents d'IA via le même socle qui gouverne les données, le contexte et les actions. L’identité, les politiques, l’observabilité et les contrôles de cycle de vie font partie intégrante du fonctionnement du travail agentique, plutôt que d’être des vérifications distinctes recréées pour chaque expérience d’agent.\u003C/p\u003E\n\u003Cp\u003ECette base devient encore plus importante à mesure que les agents se multiplient. Un assistant unique peut être traité comme une application locale, mais un parc d'agents requiert un moyen commun de gouverner ce que chacun peut utiliser, ce qu'il peut faire et comment ses actions sont enregistrées.\u003C/p\u003E\n\u003Cp\u003EAvec une architecture de plan de contrôle en place, les organisations peuvent traiter les agents comme des participants gouvernés dans les processus stratégiques, avec des limites claires concernant ce à quoi ils peuvent accéder, ce qu’ils peuvent faire et comment leur travail est tracé.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_governing-the-path-from-context-to-action_0":{"id":"text-8fa0906465","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EÀ RETENIR\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EÀ mesure que les agents d'IA endossent le rôle d'acteurs non humains, les entreprises ont besoin d'un moyen commun de gérer l'identité, le contexte, les politiques, l'accès aux outils et l'auditabilité. 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