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Janardanan"}},{"authorImage":{"id":"image-8ab740bc42","height":"200","src":"/adobe/dynamicmedia/deliver/dm-aid--c1cf5112-d351-40f4-9f2a-aa77a5228f6d/danielle-kucera.jpg?quality=85&preferwebp=true","alt":"Headshot-style photo of Danielle Kucera of Snowflake","lazyEnabled":true,"isLcpImage":false,"width":"200",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-a40fc683d8","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/danielle-kucera/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Danielle Kucera"}},{"authorImage":{"id":"image-c75d61549a","height":"400","src":"/adobe/dynamicmedia/deliver/dm-aid--8abfe0e1-f452-4f0c-85cb-2c21dbcc9bef/tehzeeb-versey-patel.png?quality=85&preferwebp=true","alt":"Avatar headshot of Tehzeeb Versey-Patel of Snowflake","lazyEnabled":true,"isLcpImage":false,"width":"400",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-e0cbfdbe8b","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/tehzeeb-versey-patel/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Tehzeeb Versey-Patel"}}],"image":{"id":"image-51faaaf034","height":"608","src":"/adobe/dynamicmedia/deliver/dm-aid--8cd70e66-3b6b-44ea-8a7d-7c765cd2601b/enterprise-data-context-blog-header.png?quality=85&preferwebp=true","alt":"Graphic for Expedition showing AI Data Cloud and Enterprise Data and Context callout","lazyEnabled":true,"isLcpImage":false,"width":"1413",":type":"snowflake-site/components/image"},"timeToRead":"10","publicationDate":"NOV 03, 2026","tag":{"tagText":"Prodotto e tecnologia","tagColor":"#3F808A"},"title":{"lines":["Creare fiducia negli agenti AI con contesto di business governato e sicurezza AI"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"},"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-41df648f0a",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[]},"button":{"id":"button-ad67cc285c","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"UNKNOWN"}},":itemsOrder":["blog_hero","container","button"]},"responsivegrid_content":{"columnCount":12,"columnClassNames":{"blog_title_707169141":"aem-GridColumn aem-GridColumn--default--12","image":"aem-GridColumn aem-GridColumn--default--12","wistia_video":"aem-GridColumn aem-GridColumn--default--12","blog_text_219485443":"aem-GridColumn aem-GridColumn--default--12","blog_title_2102433236":"aem-GridColumn aem-GridColumn--default--12","blog_text_1267904540":"aem-GridColumn 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aem-Grid--default--12","appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-bc84a1f716","text":"\u003Cp\u003ELe aziende hanno superato il punto in cui si chiedevano se gli agenti AI fossero in grado di svolgere un lavoro utile. La domanda più urgente ora è se i team possano garantire ciò che questi agenti dicono e fanno. L’agente AI risponde a una domanda basandosi sulla definizione di fatturato effettivamente approvata dalla finanza? La sicurezza può individuare una chiamata a tool rischiosa prima che venga eseguita, e un limite di budget può fermare un agente AI fuori controllo prima che compaia nella previsione trimestrale? \u003C/p\u003E\n\u003Cp\u003ESe la governance e il contesto non vengono integrati negli agenti AI fin dall’inizio, il rischio di risposte contrastanti e agenti AI fuori controllo aumenta con ogni nuovo agente AI, senza contare il rischio di AI ombra e di spesa AI fuori controllo. \u003C/p\u003E\n\u003Cp\u003EOggi, a \u003Ca href=\"https://www.snowflake.com/en/expedition/\" target=\"_blank\"\u003EExpedition 2026\u003C/a\u003E, Snowflake annuncia funzionalità e miglioramenti che rafforzano la fiducia in ogni livello della piattaforma:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003ECreare una comprensione condivisa e governata del business\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EEstendere il control plane di Snowflake a ogni azione, connessione e dollaro spesi degli agenti AI\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EFondare queste funzionalità su una piattaforma dati aperta e interoperabile by design\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title":{"id":"blog-title-c00f511f5e","propertiesId":"sviluppare-una-comprensione-condivisa-e-governata-del-business","type":"heading2","lines":["Sviluppare una comprensione condivisa e governata del business"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_275396429":{"id":"blog-text-947100a88b","text":"\u003Cp\u003EUna dashboard riporta una cifra per il fatturato netto, un analista ne riporta un’altra e un agente AI ne restituisce una terza. Quando ogni team calcola il “fatturato netto” in modo diverso e non esiste una registrazione di quale versione sia quella ufficiale, anche un agente AI perfettamente governato può restituire una risposta su cui nessuno concorda.\u003C/p\u003E\n\u003Cp\u003E\u003Ca target=\"_blank\" href=\"http://www.snowflake.com/it/blog/business-ontology-context\"\u003E\u003Cb\u003EBusiness Ontology\u003C/b\u003E\u003C/a\u003E (presto in public preview) è la novità più recente di \u003Ca target=\"_blank\" href=\"https://www.snowflake.com/en/product/features/horizon-context/\"\u003ESnowflake Horizon Context\u003C/a\u003E. Offre alle organizzazioni un unico posto governato in cui registrare il significato dei loro concetti di business principali, indipendentemente da qualsiasi struttura dati specifica. Business Ontology fa anche parte di un approccio unificato alla governance del contesto: \u003Ca target=\"_blank\" href=\"https://www.snowflake.com/it/blog/enterprise-ai-agents-grounded-context/\"\u003E\u003Cb\u003ESnowflake Cortex Sense\u003C/b\u003E\u003C/a\u003E (presto in public preview) individua il contesto nei tuoi dati, Business Ontology lo rende autorevole e \u003Cb\u003E\u003Ca target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/views-semantic/overview\"\u003ESnowflake Semantic Views\u003C/a\u003E\u003C/b\u003E lo trasforma in metriche governate e coerenti. \u003C/p\u003E\n\u003Cp\u003EGrazie a Cortex Sense, puoi inizializzare Business Ontology a partire dal contesto già esistente in Snowflake, offrendo agli analisti un punto di partenza per definire i concetti e le loro relazioni tra i team. Una volta creati, le persone responsabili dei concetti possono approvarne le definizioni. Con l’evoluzione del contesto di business, Cortex Sense può suggerire definizioni, ma è il responsabile umano a controllare il processo di ufficializzazione. \u003C/p\u003E\n\u003Cp\u003EInoltre, non è necessario ricostruire un glossario da zero per Business Ontology. Le organizzazioni che conservano le definizioni in un catalogo di terze parti o in un foglio di calcolo possono migrarle con un’\u003Cb\u003Eimportazione guidata di glossario\u003C/b\u003E in un’unica sessione con \u003Ca href=\"https://www.snowflake.com/it/product/snowflake-coco/\"\u003ESnowflake CoCo\u003C/a\u003E, l’agente AI per il coding di Snowflake. L’importazione estrae domini, termini candidati, relazioni e asset di dati collegati, contrassegnando ciascuno come bozza da approvare da parte del relativo responsabile tramite il \u003Cb\u003Eworkflow “steward approval”\u003C/b\u003E. \u003C/p\u003E\n\u003Cp\u003EOgni definizione approvata si collega a viste semantiche, tabelle e viste tra team e domini, in modo che gli agenti AI di finance, vendite e supporto lavorino tutti sulla base dello stesso significato. Quando una differenza è intenzionale, come una metrica definita diversamente in base alla regione, Business Ontology mantiene ogni versione come definizione approvata, etichettata con dove e quando si applica. Nel frattempo, le Semantic Views eseguono la logica approvata quando arriva una domanda. \u003C/p\u003E\n\u003Cp\u003EOltre ad aiutare a mantenere il contesto governato coerente e aggiornato, Cortex Sense porta quel contesto anche nell’interazione stessa con l’agente AI, rendendo gli agenti AI utili dal momento del lancio. Quando Cortex Sense deduce qualcosa in conflitto con una definizione approvata, gli agenti AI utilizzano la definizione approvata e Cortex Sense apprende dall’esperienza. \u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EHorizon Context \u003C/b\u003Ecrea una data foundation connessa per Business Ontology, raccogliendo, arricchendo e attivando il contesto in tutto il patrimonio di dati. I suoi connettori pronti all’uso raccolgono schemi, log delle query, definizioni di dashboard e segnali di utilizzo da fonti come Tableau, Power BI, dbt, SQL Server e PostgreSQL. Horizon Context arricchisce questi metadati con lineage end‑to‑end, tag, descrizioni generate dall’AI e segnali di popolarità.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"http://www.snowflake.com/it/blog/business-ontology-context\" target=\"_blank\"\u003EPer maggiori dettagli, leggi il nostro blog post su Business Ontology.\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_707169141":{"id":"blog-title-84e7493d10","propertiesId":"governare-osservare-e-ottimizzare-ogni-azione-degli-agenti-ai","type":"heading2","lines":["Governare, osservare e ottimizzare ogni azione degli agenti AI"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_883134437":{"id":"blog-text-f9fe66b9e9","text":"\u003Cp\u003ESpesso, i team di ogni funzione aziendale collegano gli agenti AI a modelli, server MCP e sistemi aziendali più velocemente di quanto IT e sicurezza possano monitorarli. Questo lascia ai leader la scelta tra scalare rapidamente, accettando potenziali rischi di sicurezza, conformità e costi, oppure rallentare le implementazioni finché le funzionalità di sicurezza e controllo dei costi non si allineano.\u003Cbr\u003E\n\u003Cbr\u003E\nCon Snowflake, i leader non devono fare questa scelta, perché collega dati governati, funzionalità di sicurezza per gli agenti AI, controlli di costo AI e funzionalità di observability per gli agenti AI, tutto in un unico sistema. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-62499187de","height":"856","src":"/adobe/dynamicmedia/deliver/dm-aid--7e78d6b4-546d-4138-aedf-2c2147e04611/expedition-cortex-ai-gateway-solution.png?quality=85&preferwebp=true","alt":"Diagram showing relationship between Cortex AI Gateway, cost governance, dynamic model routing, observability and cortex sandbox","lazyEnabled":true,"isLcpImage":true,"width":"2000",":type":"snowflake-site/components/image"},"blog_title_2102433236":{"id":"blog-title-6c2d14c91c","propertiesId":"portare-il-control-plane-di-snowflake-a-ogni-agente-ai-con-cortexai-gateway","type":"heading3","lines":["Portare il control plane di Snowflake a ogni agente AI con Cortex AI Gateway"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_948856335":{"id":"blog-text-56fedd2a0d","text":"\u003Cp\u003E\u003Cb\u003E\u003Ca target=\"_blank\" href=\"https://www.snowflake.com/it/blog/cortex-ai-gateway-control-plane-enterprise-ai/\"\u003ECortex AI Gateway\u003C/a\u003E\u003C/b\u003E (public preview) è un punto di accesso centralizzato e governato che consente agli agenti AI di accedere ai modelli e ai dati su cui agiscono. Applica un insieme coerente di policy alle connessioni degli agenti AI su cloud, framework e provider AI supportati. Poiché governa direttamente da dove risiedono i dati, ogni agente AI eredita i controlli di identità e accesso già esistenti della tua organizzazione, così come ogni nuovo controllo man mano che viene applicato. I team non devono più creare e gestire un secondo set di credenziali per gli agenti AI.\u003C/p\u003E\n\u003Cp\u003ELa governance è il punto di partenza, e \u003Cb\u003ESnowflake Cortex Sandboxes\u003C/b\u003E (private preview) dimostra l’impegno di Snowflake nell’abilitare azioni autonome e governate degli agenti AI. Cortex Sandboxes offre il controllo sul codice eseguito da un agente AI: gli agenti AI hanno uno spazio sicuro e privato per eseguire codice personalizzato insieme ai dati aziendali in Snowflake, mantenendo e seguendo i permessi della persona che esegue l’agente AI. Non è necessario spostare i dati fuori da Snowflake e questi spazi si avviano in pochi secondi e si arrestano automaticamente al termine, evitando manutenzione aggiuntiva e riducendo il rischio di esposizioni accidentali.\u003C/p\u003E\n\u003Cp\u003ESnowflake continua inoltre a migliorare la governance nei workflow AI. Con la governance MCP tramite \u003Cb\u003ECortex AI Gateway Tools\u003C/b\u003E (public preview), le chiamate a strumenti degli agenti AI vengono autenticate con l’identità dell’utente finale e verificate rispetto alle policy prima dell’esecuzione. Il sistema può bloccare chiamate API non approvate, azioni non autorizzate e violazioni di conformità prima che un agente AI raggiunga un sistema sensibile. \u003Cb\u003ECortex AI Guardrails\u003C/b\u003E (public preview) aggiunge una protezione runtime a livello di account contro prompt injection, jailbreak e nuovi tipi di attacco, per ogni agente AI instradato tramite Cortex AI Gateway, inclusi gli agenti AI di terze parti.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"wistia_video":{"id":"wistia-video-c11f26fa79","title":{"id":"title","type":"heading1","lines":["Creare fiducia negli agenti AI con contesto di business governato e sicurezza AI"],":type":"snowflake-site/components/title"},"wistiaVideoId":"1uveq6nh86","showVideoLength":true,"showRewatchButton":true,":type":"snowflake-site/components/wistia-video"},"blog_text_588005512":{"id":"blog-text-8f469ef917","text":"\u003Cp\u003ELa visibilità viene dopo, perché non puoi gestire ciò che non puoi vedere. \u003Cb\u003EObservability\u003C/b\u003E è in GA e registra ogni chiamata ai modelli, invocazione di strumenti, passaggio di reasoning e decisione dei guardrail in tabelle governate all’interno dell’account Snowflake del cliente. Queste trace possono essere interrogate con SQL standard e unite al resto dei dati aziendali. \u003Cb\u003EAgent Trace Intelligence\u003C/b\u003E (private preview) utilizza questo join per individuare gli agenti AI che sprecano denaro in loop, gli strumenti che continuano a fallire e il costo reale di ogni attività completata.\u003C/p\u003E\n\u003Cp\u003EIl costo è l’ultimo elemento del quadro. Le funzionalità di \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/governance-and-availability/ai-cost-management-and-governance\" target=\"_blank\"\u003Egovernance dei costi\u003C/a\u003E\u003C/b\u003E sono in GA e attribuiscono la spesa AI a un team, dipartimento, agente AI o applicazione, applicando limiti rigidi quasi in tempo reale e aiutando i manager a identificare e bloccare gli agenti AI fuori controllo prima che superino i budget. \u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/it/blog/dynamic-model-routing-open-models-cortex-ai/\" target=\"_blank\"\u003EDynamic Model Routing\u003C/a\u003E\u003C/b\u003E (private preview) abbina ogni richiesta a un modello approvato dall’organizzazione in base a costo, accuratezza e latenza, tenendo il lavoro di routine lontano dai modelli più costosi. In \u003Ca href=\"https://www.snowflake.com/it/blog/dynamic-model-routing-open-models-cortex-ai/\" target=\"_blank\"\u003Efase di test interni\u003C/a\u003E, il \u003Cb\u003Erouting dinamico tra modelli ha garantito un’efficienza dei token fino a 3 volte superiore\u003C/b\u003E rispetto a un percorso basato solo su modelli frontier, mantenendo la stessa qualità. \u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/it/blog/cortex-ai-gateway-control-plane-enterprise-ai/\" target=\"_blank\"\u003EScopri di più su Cortex AI Gateway nel nostro blog.\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_1818581894":{"id":"blog-title-60d1f4bf57","propertiesId":"portare-lobservability-in-tempo-reale-agli-agenti-ai-e-alle-applicazioni-con-observe-by-snowflake","type":"heading3","lines":["Portare l’observability in tempo reale agli agenti AI e alle applicazioni con Observe by Snowflake"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1730056153":{"id":"blog-text-ba434a73e1","text":"\u003Cp\u003ECortex AI Gateway offre un punto di accesso governato per gli agenti AI, ma eseguire l’AI in modo affidabile in produzione richiede anche di capire come si comportano gli agenti AI e come si comportano le applicazioni e l’infrastruttura che li supportano. \u003Ca href=\"https://www.snowflake.com/en/product/observe/\" target=\"_blank\"\u003EObserve by Snowflake\u003C/a\u003E riunisce questi sistemi in un’unica piattaforma di observability basata sull’AI, aiutando i team di engineering a investigare i guasti, migliorare la qualità degli agenti AI e ottimizzare i costi. \u003C/p\u003E\n\u003Cp\u003EObserve ha ricostruito la sua pipeline di ingestion su Snowpipe Streaming (private preview), scrivendo log, metriche e trace direttamente nelle tabelle Snowflake, così gli ingegneri che rispondono a un incidente vedono i dati più aggiornati. \u003Cb\u003ESnowflake Data Share into Observe \u003C/b\u003E(private preview) trasforma i dati che i clienti già archiviano nei propri account Snowflake in data set Observe zero-copy, eliminando la necessità di effettuare l’ingestion o l’archiviazione due volte. \u003Cb\u003EO11y Agent in Slack\u003C/b\u003E (private preview) consente ai team di automatizzare la prima risposta a nuovi problemi e visualizzare l’analisi delle cause radice direttamente nel canale Slack del team.\u003C/p\u003E\n\u003Cp\u003EPer i team che sviluppano i propri agenti AI e applicazioni LLM, \u003Ca href=\"https://www.snowflake.com/it/blog/ai-agent-observability-monitor-debug-optimize-llm-applications/\"\u003E\u003Cb\u003EAgent Observability\u003C/b\u003E\u003C/a\u003E (private preview) monitora le prestazioni, gli errori, l’utilizzo dei token e i costi. Supporta inoltre la ricerca e il debug su trace, sessioni e conversazioni, ed esegue valutazioni online LLM-as-judge sul traffico in produzione. La telemetria viene raccolta tramite OpenTelemetry e conservata in tabelle Apache Iceberg™, su un’architettura progettata per mantenere economicamente accessibile il contesto completo degli agenti AI, così i team possono collegare il comportamento degli agenti AI ai dati di business su Snowflake e valutarne il valore.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_82120626":{"id":"blog-title-b9b61ef583","propertiesId":"estendere-la-governance-su-un-open-lakehouse","type":"heading2","lines":["Estendere la governance su un open lakehouse"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_984852262":{"id":"blog-text-4511dfe2db","text":"\u003Cp\u003ELa maggior parte delle aziende dispone di più di una piattaforma dati. Eseguono più motori su più cataloghi, regioni e cloud. Con ogni silo, gli agenti AI devono ragionare su più copie di dati (a volte in conflitto tra loro), policy di governance e livelli di contesto di business. Questo si traduce in risposte difficili da considerare attendibili, basate su dati a cui gli agenti AI forse non dovrebbero avere accesso. \u003C/p\u003E\n\u003Cp\u003EIn Snowflake, crediamo che esista un modo migliore. Alla base di Business Ontology e Cortex AI Gateway c’è l’architettura open by design di Snowflake. Si fonda su community open source come Apache Iceberg per lo storage, Apache Polaris™ per la governance e \u003Ca href=\"https://www.snowflake.com/it/blog/apache-ossie-open-semantic-interchange-incubator/\" target=\"_blank\"\u003EApache Ossie (incubating)\u003C/a\u003E per la semantica. Insieme, forniscono i meccanismi per connettere tutti i tuoi dati su più piattaforme e sviluppare una data foundation open e interoperabile, con governance e contesto universali, per potenziare la tua impresa agentica. \u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\" target=\"_blank\"\u003EHorizon Catalog\u003C/a\u003E, basato su Apache Polaris, contribuisce a semplificare le architetture multicatalog e multiengine offrendo un catalog universale senza lock-in. Definisci un unico insieme di policy di governance e applicale in qualsiasi engine compatibile con Iceberg REST Catalog, indipendentemente dal catalogo e senza migrazione, con accesso da engine esterni alle tabelle esterne (in GA). \u003C/p\u003E\n\u003Cp\u003EApplicare la sicurezza è solo metà dell’equazione: è necessario anche verificare ogni interazione con gli engine. Questo è il motivo per cui \u003Cb\u003Ela cronologia di audit ora registra sia le interazioni di Snowflake che quelle degli engine esterni\u003C/b\u003E con le Iceberg Tables gestite da Snowflake e gestite esternamente, quando Horizon Catalog funge da livello di governance universale. I team di sicurezza e piattaforma possono analizzare gli accessi, monitorare l’adozione e validare le integrazioni da un unico posto, senza dover creare un’infrastruttura di observability separata.  \u003C/p\u003E\n\u003Cp\u003EI silo all’interno dei tuoi team sono solo una parte della sfida. Cosa succede quando un cliente o un partner ha bisogno di accedere ai dati? È qui che entra in gioco \u003Cb\u003ESnowflake AI and Data Sharing\u003C/b\u003E, che consente ai producer di dati di \u003Ca href=\"https://www.snowflake.com/en/product/features/zero-etl-data-sharing/\" target=\"_blank\"\u003Econdividere rapidamente e facilmente i propri prodotti con i consumer\u003C/a\u003E. Ora supporta \u003Ca href=\"https://docs.snowflake.com/en/collaboration/open-format-sharing\" target=\"_blank\"\u003E\u003Cb\u003EOpen Data Sharing\u003C/b\u003E\u003C/a\u003E, basato su Apache Polaris (in GA), che consente di condividere dati con qualsiasi engine compatibile con Iceberg REST.\u003C/p\u003E\n\u003Cp\u003ESnowflake porta inoltre altri due miglioramenti di AI and Data Sharing in private preview. Lo \u003Cb\u003Esharing cross-cloud senza replica\u003C/b\u003E rende disponibili i listing dei provider tra più regioni senza costi di replica e storage anticipati, consentendo ai consumer di accedere ai dati tramite query remote sulle Iceberg Tables. Il \u003Cb\u003Erilevamento della deriva dello schema e Copy Grants per la condivisione dei dati\u003C/b\u003E aiutano producer e consumer a identificare modifiche agli schema o alle grant che potrebbero interrompere la condivisione, preservando al tempo stesso le copy grant quando le view vengono modificate.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_1130657084":{"id":"blog-title-74a36a2b56","propertiesId":"gestire-la-spesa-e-lamministrazione-ai-tra-team-e-account","type":"heading2","lines":["Gestire la spesa e l’amministrazione AI tra team e account"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_219485443":{"id":"blog-text-627f823b82","text":"\u003Cp\u003EPer evitare sforamenti nella spesa per l’AI i dirigenti tengono sotto stretto controllo crediti e spesa. Allo stesso tempo, gestire account e prestazioni è una componente importante per ottenere un ROI dall’AI. \u003C/p\u003E\n\u003Cp\u003EI nuovi aggiornamenti per \u003Ca href=\"https://www.snowflake.com/en/pricing-options/cost-and-performance-optimization/\" target=\"_blank\"\u003EFinOps\u003C/a\u003E offrono alle aziende maggiore controllo sulla governance dei costi e maggiore flessibilità tra gli account. Gli amministratori possono applicare \u003Ca href=\"https://www.snowflake.com/it/blog/per-user-quotas-ai-generally-available/\" target=\"_blank\"\u003Equote per utente\u003C/a\u003E, impostare limiti settimanali per utente e applicare quote all’utilizzo di Cortex AI Gateway (tutte in GA). Il \u003Cb\u003Esupporto per budget a livello di organizzazione\u003C/b\u003E (in GA) consente di impostare target di spesa in dollari o crediti su più account utilizzando i tag. Il \u003Cb\u003Erilevamento delle anomalie di costo in base ai tag\u003C/b\u003E (in GA) fornisce insight sui potenziali picchi e supporta fino a 50 coorti di spesa personalizzate per account, con avvisi giornalieri e una modalità sandbox.\u003C/p\u003E\n\u003Cp\u003ECon la crescita dell’adozione dell’AI, cresce anche il numero di account Snowflake gestiti da un’azienda. Nelle aziende che gestiscono 10 o più account Snowflake, gli amministratori spesso devono effettuare il log in ogni singolo account o scrivere script personalizzati per implementare modifiche a livello organizzativo.\u003C/p\u003E\n\u003Cp\u003ESnowflake sta cambiando le cose con \u003Ca href=\"https://www.snowflake.com/en/product/use-cases/organizations/\" target=\"_blank\"\u003ESnowflake Organizations\u003C/a\u003E. Offre ai GlobalOrgAdmin un’unica interfaccia Snowsight per gestire utenti e gruppi utenti dell’organizzazione, configurare le regole per i domini di terze parti e i tipi di tenant, e monitorare le violazioni. Possono inoltre distribuire i pacchetti scanner di Trust Center su tutti gli account contemporaneamente, semplificando gli aggiornamenti (public preview). Le nuove \u003Cb\u003EOrg Usage Views cross-account\u003C/b\u003E (in GA), che includono la spesa AI a livello organizzativo per account e servizio, contribuiscono a rendere la governance misurabile e verificabile, e gli amministratori ottengono una vista unificata, basata sugli insight, sullo stato di salute della loro organizzazione.\u003C/p\u003E\n\u003Cp\u003EAnche il tuning delle prestazioni si avvicina sempre più a una manutenzione quasi nulla. Con la \u003Cb\u003Efunzionalità Automatic Key Selection\u003C/b\u003E di Optima Clustering (in GA), Snowflake rende il clustering autonomo. Invece di far scegliere manualmente le chiavi di clustering a un amministratore, Snowflake ora le seleziona in base ai pattern storici delle query e di accesso alle tabelle, aggiornandole automaticamente quando questi pattern cambiano. Optima raggruppa nuovamente le parti della tabella effettivamente interrogate in base alle heat map dei workload, mentre la fatturazione basata sul valore, legata all’ingest, riduce la probabilità di picchi di costo. Abbinata alla capacità di \u003Ca href=\"https://www.snowflake.com/it/blog/adaptive-compute-ga-azure-google-cloud/\" target=\"_blank\"\u003ESnowflake Adaptive Compute\u003C/a\u003E di adattare la capacità di calcolo in base all’andamento della domanda, questa funzionalità contribuisce a semplificare la manutenzione delle prestazioni in tutta l’azienda.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_490306541":{"id":"blog-title-82f59c3743","propertiesId":"sviluppa-agenti-ai-su-cui-la-tua-azienda-puo-contare","type":"heading2","lines":["Sviluppa agenti AI su cui la tua azienda può contare"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1335257127":{"id":"blog-text-ba723b3a7f","text":"\u003Cp\u003EPerché gli utenti possano fidarsi di un agente AI, l’agente deve operare sulla base di un significato aziendale approvato, agire nel rispetto delle policy, rimanere visibile alle persone responsabili e rispettare i limiti di spesa concordati. Snowflake rende tutto questo possibile collegando in un unico sistema il contesto aziendale governato, la sicurezza degli agenti AI e i controlli dei costi, ed estende tale governance e contesto su una data foundation open. Con questo approccio integrato e interoperabile, le aziende possono distribuire più agenti AI con sicurezza, migliorarli continuamente e ottenere più valore dai loro investimenti in AI.\u003C/p\u003E\n\u003Cp\u003EScopri altri annunci di Snowflake Expedition su \u003Ca href=\"http://snowflake.com/en/blog/expedition-26/\" target=\"_blank\"\u003Esnowflake.com/en/blog/expedition-26/\u003C/a\u003E, oppure partecipa all’evento virtuale su \u003Ca href=\"http://snowflake.com/en/expedition/\" target=\"_blank\"\u003Esnowflake.com/en/expedition/\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EDichiarazioni previsionali: Questo articolo potrebbe contenere dichiarazioni previsionali, incluse quelle relative a future offerte di prodotto, e non costituisce un impegno a fornire alcuna offerta di prodotto. I risultati e le offerte effettivi potrebbero essere diversi. Fai riferimento al nostro più recente modulo 10‑Q per ulteriori informazioni.\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_789439352":{"id":"blog-title-9ce44d46d2","propertiesId":"domande-frequenti","type":"heading2","lines":["Domande frequenti"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1267904540":{"id":"blog-text-b89b87e816","text":"\u003Ch4\u003ECos’è Business Ontology?\u003C/h4\u003E\n\u003Cp\u003EBusiness Ontology è un livello di modellazione e governance aziendale di Snowflake Horizon Context. Registra il significato ufficiale dei termini aziendali, chi li ha approvati e quale data asset li calcola. Le definizioni approvate si collegano a Semantic Views, tabelle e view e vengono fornite a Snowflake CoCo e Snowflake CoWork, così gli agenti AI possono rispondere basandosi sulla definizione ufficiale.\u003C/p\u003E\n\u003Ch4\u003ECosa fa Cortex AI Gateway?\u003C/h4\u003E\n\u003Cp\u003ECortex AI Gateway estende il control plane di Snowflake agli agenti AI di terze parti. Verifica le chiamate agli strumenti MCP rispetto alle policy prima dell’esecuzione, registra le chiamate ai modelli e i passaggi di ragionamento in un piano di observability lato server, attribuisce la spesa AI e applica limiti di budget rigorosi in tempo quasi reale. Instrada inoltre ogni richiesta verso il modello più efficiente.\u003C/p\u003E\n\u003Ch4\u003EIn che modo Snowflake aiuta a controllare i costi degli agenti AI?\u003C/h4\u003E\n\u003Cp\u003ESnowflake aiuta a controllare i costi degli agenti AI su più livelli. Cortex AI Gateway attribuisce la spesa a team, agenti AI e applicazioni e applica limiti in tempo quasi reale, mentre Dynamic Model Routing consente di scegliere il modello migliore per la propria richiesta in base a costo, precisione e latenza. Budget a livello di organizzazione, quote per utente e rilevamento delle anomalie basato su tag estendono la governance dei costi a tutti gli account.\u003C/p\u003E\n\u003Ch4\u003ESnowflake può governare le tabelle Apache Iceberg gestite in altri catalog?\u003C/h4\u003E\n\u003Cp\u003ESì. Horizon Catalog, basato su Apache Polaris, può accedere alle Iceberg Tables gestite esternamente tramite Horizon Iceberg REST Catalog e governarle sul posto, senza migrazione. Open Data Sharing, ora in GA, fornisce dati governati a qualsiasi client Iceberg REST Catalog.\u003C/p\u003E\n\u003Ch4\u003EIn che modo Observe by Snowflake supporta l’observability degli agenti AI?\u003C/h4\u003E\n\u003Cp\u003EAgent Observability, in private preview, raccoglie la telemetria degli agenti AI tramite OpenTelemetry e monitora prestazioni, errori, utilizzo dei token e costi. Supporta il debug su trace, sessioni e conversazioni ed esegue valutazioni LLM-as-judge sul traffico di produzione. La telemetria viene conservata in Iceberg Tables, in modo che i team possano correlare il comportamento degli agenti AI con i dati di business.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","blog_title","blog_text_275396429","blog_title_707169141","blog_text_883134437","image","blog_title_2102433236","blog_text_948856335","wistia_video","blog_text_588005512","blog_title_1818581894","blog_text_1730056153","blog_title_82120626","blog_text_984852262","blog_title_1130657084","blog_text_219485443","blog_title_490306541","blog_text_1335257127","blog_title_789439352","blog_text_1267904540"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 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Nai","lazyEnabled":true,"isLcpImage":false,"width":"450",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-15d75c144d","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/carlos-nai/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Carlos Nai"},"authorTitle":"Product Marketing Lead, Data Lakes at Snowflake"},{"authorImage":{"id":"image-7263380525","height":"1470","src":"/adobe/dynamicmedia/deliver/dm-aid--d30f2a6f-d4ac-4a3f-9032-4b75527fced0/radhika-janardanan.jpg?quality=85&preferwebp=true","alt":"Headshot style photo of Radhika Janardanan","lazyEnabled":true,"isLcpImage":false,"width":"1753",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-d574c248e2","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/radhika-janardanan/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Radhika Janardanan"},"authorTitle":"Senior Product Marketing Manager, Snowflake"},{"authorImage":{"id":"image-8ab740bc42","height":"200","src":"/adobe/dynamicmedia/deliver/dm-aid--c1cf5112-d351-40f4-9f2a-aa77a5228f6d/danielle-kucera.jpg?quality=85&preferwebp=true","alt":"Headshot-style photo of Danielle Kucera of 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