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Na","width":"512",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-7fcfbcb7e0","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/cindy-na/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Cindy Na"}},{"authorImage":{"id":"image-e07b06b1a8","height":"200","src":"/adobe/dynamicmedia/deliver/dm-aid--be5d59dc-5c8a-479f-94a2-38cd590a83f8/siddharth-dwivedi.jpg?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"alt":"Headshot-style photo of Siddharth Dwivedi","width":"200",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-5cc467cbf2","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/siddharth-dwivedi/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Siddharth Dwivedi"}}],"image":{"id":"image-1b2378b04c","height":"443","src":"/adobe/dynamicmedia/deliver/dm-aid--0f74f761-143b-41ac-b80a-582fe827880a/expedition-rollup-1-temp-blog-header.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"alt":"Image of Expedition mountain icon","width":"1114",":type":"snowflake-site/components/image"},"timeToRead":"9","publicationDate":"NOV 03, 2026","tag":{"tagText":"Prodotto e tecnologia","tagColor":"#3F808A"},"title":{"lines":["Sviluppa più velocemente e lavora in modo più intelligente con app e agenti AI ancorati ai dati"],"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 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aem-GridColumn--default--12","blog_text_1310573602":"aem-GridColumn aem-GridColumn--default--12","blog_title_830892397":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-6eeb433d52","text":"\u003Cp\u003ESolo l'11% delle organizzazioni ha agenti AI in produzione, nonostante il 38% li stia testando in fase pilota, secondo il \u003Ca href=\"https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html\" target=\"_blank\"\u003EDeloitte 2026 Tech Trends Report\u003C/a\u003E. Osserviamo questo scarto tra i progetti pilota e le implementazioni in produzione in settori e aziende di ogni dimensione: I team sviluppano una demo convincente e la implementano su dati reali, solo per vederla restituire con sicurezza un numero di fatturato errato o ignorare un vincolo di policy che un analista junior avrebbe individuato. \u003C/p\u003E\n\u003Cp\u003EIl problema è la mancanza di contesto. Gli agenti AI devono sapere quale definizione di metrica è quella autorevole, quali dati sono aggiornati, quali permessi detiene ciascun utente e cosa è accaduto nell’ultima conversazione. Senza questo ancoraggio, anche il modello più capace finisce per procedere a intuito. E anche quando gli agenti AI trovano la risposta corretta, può essere difficile condividere questi insight con il resto dell’azienda.\u003C/p\u003E\n\u003Cp\u003EA \u003Ca href=\"https://www.snowflake.com/en/expedition\" target=\"_blank\"\u003EExpedition 2026,\u003C/a\u003E Snowflake annuncia funzionalità lungo tre dimensioni che rafforzano il modo in cui agenti AI e app operano insieme ai tuoi dati, con governance e contesto completi:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EGrounding: \u003C/b\u003Ecollega gli agenti AI ai tuoi dati governati, alle metriche e alla business logic, così possono rispondere alle domande basandosi su definizioni autorevoli, non su supposizioni.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAzione: \u003C/b\u003Econsenti agli agenti AI di eseguire lavoro reale seguendo i permessi di ciascun utente, negli strumenti già utilizzati dai team, con piena verificabilità. Distribuisci rapidamente e in modo sicuro app Snowflake su dati governati, in modo che persone e agenti AI possano estrarre insight e agire.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EEffetto moltiplicatore: \u003C/b\u003Econdividi skill, profili e memoria testati in tutta l’organizzazione, così ogni nuovo agente AI parte meglio del precedente.\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-b5cb8fe57d","propertiesId":"grounding-dai-a-ogni-agente-ai-dati-rilevanti-e-contesto-di-business-dal-primo-giorno","type":"heading2","lines":["Grounding: dai a ogni agente AI dati rilevanti e contesto di business dal primo giorno"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_461565667":{"id":"blog-text-28fae70fa7","text":"\u003Cp\u003EI ricercatori di a16z lo hanno detto chiaramente nell’\u003Ca href=\"https://a16z.com/your-data-agents-need-context/\" target=\"_blank\"\u003Eanalisi di marzo 2026\u003C/a\u003E: “Una ragione fondamentale per cui gli agenti AI non funzionavano bene era la mancanza di un adeguato contesto sui dati.” Un agente AI che risponde alla domanda “Qual è stata la crescita del fatturato nell’ultimo trimestre?” può scrivere SQL con competenza su una tabella apparentemente plausibile, ma restituire numeri errati con piena sicurezza perché nessuno gli ha indicato quale campo contiene la definizione autorevole di “fatturato”.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EL’approccio di Snowflake parte dal principio che gli agenti AI dovrebbero ereditare il contesto che la tua organizzazione ha già creato:\u003C/b\u003E gli schemi, le metriche, la business logic, le policy di governance e i permessi che definiscono come funzionano i tuoi dati. Diverse funzionalità nuove e potenziate rendono tutto questo concreto.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/it/blog/enterprise-ai-agents-grounded-context/\" target=\"_blank\"\u003E\u003Cb\u003ESnowflake Cortex Sense\u003C/b\u003E\u003C/a\u003E (presto in public preview) crea automaticamente il contesto a partire dai tuoi schemi esistenti, dalle definizioni delle metriche e dalla business logic, così non devi passare settimane a configurare e definire connessioni. Cortex Sense funziona sui tuoi dati, sui dati condivisi e sui dati acquistati tramite il Marketplace Snowflake, così gli agenti AI comprendono il quadro completo, non solo la porzione di un singolo team.\u003C/p\u003E\r\n\u003Cp\u003EQuando gli agenti AI devono ragionare su grandi volumi di informazioni non strutturate, \u003Cb\u003EAnalytical Search\u003C/b\u003E (public preview) permette loro di contare, confrontare e individuare tendenze contemporaneamente in migliaia di documenti, dai contratti ai ticket di supporto fino ai report interni. Non si tratta di una ricerca per parole chiave; Analytical Search applica funzioni AI a intere raccolte di documenti per rispondere a domande come “Quanti contratti contengono una clausola di escalation dei prezzi?” o “Quali ticket di supporto menzionano la stessa causa principale?” E poiché \u003Cb\u003ECortex Search\u003C/b\u003E ora applica policy di row access e masking sulle tabelle di origine (private preview), i documenti visibili a un agente AI seguono le stesse regole dei dati strutturati che interroga. \u003C/p\u003E\r\n\u003Cp\u003EMan mano che gli agenti AI passano dal trovare documenti al comprenderli, il nuovo modello di embedding di Snowflake \u003Cb\u003EArctic Embed 3S\u003C/b\u003E (presto in GA) \u003Cb\u003Emigliora la qualità del retrieval rispetto ad Arctic Embed L-v2.0 del 3% in inglese, del 6% nel retrieval multilingue e del 23% per tabelle e colonne strutturate\u003C/b\u003E, mantenendo la stessa classe dimensionale compatta.\u003C/p\u003E\r\n\u003Cp\u003EMan mano che i modelli frontier diventano una commodity, il vantaggio competitivo si sposta sulla capacità di un modello di comprendere a fondo il tuo business specifico. Le organizzazioni si stanno orientando verso modelli foundation open-weight personalizzati per il proprio dominio specifico, utilizzando metodi di post-training come il fine‑tuning supervisionato e il reinforcement learning. \u003Cb\u003E\u003Ca href=\"http://www.snowflake.com/it/blog/cortex-training-fine-tune-open-weight-llms/\"\u003ESnowflake Cortex Training\u003C/a\u003E \u003C/b\u003E(public preview) rende semplice il post-training e il deployment di mode Gemma e altri) su dati proprietari utilizzando GPU completamente gestite. \u003Cb\u003EI benchmark mostrano un costo fino al 61% inferiore rispetto alle soluzioni serverless concorrenti.\u003Csup\u003E1\u003C/sup\u003E\u003C/b\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_177032528":{"id":"blog-text-1bf49abb00","text":"\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003ECon Cortex Training, i team possono addestrare, sperimentare, valutare e distribuire modelli personalizzati sulla stessa piattaforma dei loro dati governati. Cortex Training integra anche \u003Ca target=\"_blank\" href=\"https://www.snowflake.com/it/blog/cortex-ai-gateway-control-plane-enterprise-ai/\"\u003ECortex AI Gateway\u003C/a\u003E (public preview) per catturare trace e log degli agenti AI, creando un ciclo di feedback continuo per la personalizzazione e il miglioramento dei modelli.\u003C/p\u003E\r\n\u003Cp\u003ENuove funzionalità rendono agentici anche i dati condivisi ed esterni. \u003Ca target=\"_blank\" href=\"https://app.snowflake.com/marketplace/listing/GZTSZ290BW2O9/snowflake-curated-data-products-private-preview-snowflake-ai-data-package-%E2%80%94-industry-data-semantic-models-ai-agent\"\u003E\u003Cb\u003EAI Data Package\u003C/b\u003E\u003C/a\u003E (public preview), con oltre 60 data set esterni curati nei settori chiave, viene fornito completo di semantic view e di un agente AI Snowflake CoWork pronto all’uso, come servizio gestito. \u003Cb\u003EAgentic Marketplace Discovery\u003C/b\u003E (public preview), basato su Snowflake CoCo, consente agli utenti di reperire dati, app e AI tramite conversazione. \u003Ca target=\"_blank\" href=\"https://www.snowflake.com/en/developers/guides/creating-cortex-agents-for-marketplace-data-providers/\"\u003E\u003Cb\u003EAgents for Shared Data\u003C/b\u003E \u003C/a\u003E(public preview) genera automaticamente un Cortex Agent e una semantic view per qualsiasi dato condiviso, così i destinatari possono utilizzarlo in modo conversazionale. Insieme, queste funzionalità permettono agli utenti di scoprire, interrogare e utilizzare dati condivisi ed esterni interamente tramite linguaggio naturale.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_1785283666":{"id":"blog-title-5caa993e16","propertiesId":"azione-affida-agli-agenti-ai-lavoro-reale-ovunque-i-team-lavorino","type":"heading2","lines":["Azione: affida agli agenti AI lavoro reale, ovunque i team lavorino"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1310573602":{"id":"blog-text-6aba40399a","text":"\u003Cp\u003EAncorare gli agenti AI ai dati giusti è il punto di partenza. La prossima domanda è se riescono a svolgere lavoro reale, in autonomia, con i permessi giusti, nei luoghi in cui i team passano il loro tempo. \u003Ca href=\"https://www.capgemini.com/insights/research-library/ai-agents/\" target=\"_blank\"\u003EUna ricerca di Capgemini\u003C/a\u003E ha rilevato che \u003Cb\u003Ela fiducia negli agenti AI completamente autonomi è scesa dal 43% al 27% in un solo anno\u003C/b\u003E, a causa di preoccupazioni legate alla privacy dei dati, all’opacità della “AI black box” e alla limitata maturità dei dati.  \u003C/p\u003E\n\u003Cp\u003EL’approccio integrato e governato di Snowflake all’AI è pensato per prevenire questa situazione, e i nostri ultimi miglioramenti contribuiscono a sviluppare la fiducia negli agenti AI attraverso trasparenza, applicazione dei permessi e verificabilità.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_100862755":{"id":"blog-title-c72c8789b8","propertiesId":"snowflake-coco-lagente-ai-di-coding-che-continua-a-lavorare-anche-dopo-il-logout","type":"heading3","lines":["Snowflake CoCo: l’agente AI di coding che continua a lavorare anche dopo il logout"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_727236657":{"id":"blog-text-8e7befadb2","text":"\u003Cp\u003E\u003Ca target=\"_blank\" href=\"https://www.snowflake.com/it/product/snowflake-coco/\"\u003ESnowflake CoCo\u003C/a\u003E, l’agente AI di coding per i team dati, ora può eseguire lavoro reale in autonomia su sessioni, pianificazioni e strumenti diversi.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ECoCo Automations \u003C/b\u003E(in GA nell’interfaccia Snowsight e nella CLI; in public preview in CoCo Desktop) eliminano il lavoro ricorrente dalla tua to-do list trasformandolo in task Snowflake pianificati. Un data engineer descrive un processo in linguaggio semplice, come “controlla lo stato della pipeline prima dello standup” o “ricostruisci il report del lunedì”, e ne imposta la cadenza. Ogni esecuzione produce una trascrizione, opera nei limiti dei permessi di chi l’ha creata e rientra in quote preimpostate che limitano la spesa di un agente AI. I team possono condividere automazioni e le skill CoCo che hanno creato tramite un \u003Cb\u003Ecatalogo di skill e plugin\u003C/b\u003E a livello di account, così un workflow creato da un ingegnere diventa disponibile per tutto il team.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"youtube":{"id":"embed-5e8eb60413","youtubeVideoId":"mmb5Li_B_As","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","isLcpImage":true,"type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"blog_text_286102237":{"id":"blog-text-fe9f3abf9f","text":"\u003Cp\u003ESnowsight ora dispone di una \u003Cb\u003Emodalità CoCo\u003C/b\u003E, uno spazio di lavoro a schermo intero supportato da un proprio container cloud con shell, file system, Git e live preview. Avvia una migrazione lunga, un’analisi multistep o la creazione di una pipeline, chiudi il laptop e torna a trovare i risultati già pronti. Con \u003Cb\u003ECloud Agents\u003C/b\u003E (in GA), i team possono eseguire CoCo in un ambiente isolato e gestito da Snowflake, che supporta task di lunga durata. Ogni sessione utilizza i permessi Snowflake dell’utente che l’ha creata, e \u003Cb\u003Erestricted session scope\u003C/b\u003E (in GA) permette agli amministratori di limitare ulteriormente cosa può fare ciascun agente AI.\u003C/p\u003E\r\n\u003Cp\u003ECoCo ora raggiunge i team anche dove già lavorano. \u003Cb\u003ECoCo Slackbot Integration\u003C/b\u003E (public preview) porta CoCo nei canali Slack per domande, trigger di workflow e revisione dei risultati. \u003Cb\u003ECoCo GitHub Integration \u003C/b\u003E(public preview) indirizza le modifiche di codice proposte attraverso il processo di pull request già in uso dal team. \u003Cb\u003ECoCo in Snowflake Mobile\u003C/b\u003E (su iOS è in GA; su Android in public preview) permette agli utenti di controllare una sessione cloud, approvare una tool call o inviare un follow-up dal proprio telefono. E con \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code-agent-sdk/cortex-code-agent-sdk\" target=\"_blank\"\u003ECortex Agent SDK\u003C/a\u003E \u003C/b\u003E(public preview), gli sviluppatori possono integrare le funzionalità di coding di CoCo in qualsiasi applicazione che rilasciano.\u003C/p\u003E\r\n\u003Cp\u003EPer maggiori dettagli, \u003Ca href=\"https://www.snowflake.com/it/blog/snowflake-coco-cloud-agents-automations/\" target=\"_blank\"\u003Eleggi il nostro blog post sui nuovi miglioramenti di CoCo.\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_1062003545":{"id":"blog-title-b6f9753de6","propertiesId":"snowflake-cowork-un-agente-ai-personale-per-ogni-team","type":"heading3","lines":["Snowflake CoWork: un agente AI personale per ogni team"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_268868741":{"id":"blog-text-3942594a25","text":"\u003Cp\u003E\u003Ca target=\"_blank\" href=\"https://www.snowflake.com/en/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E è l’agente di lavoro personale per i knowledge worker di un’intera organizzazione. A Expedition, stiamo aggiungendo la possibilità di personalizzare CoWork in base al lavoro specifico del tuo team.\u003C/p\u003E\n\u003Cp\u003EInizia con \u003Cb\u003ECoWork Profiles\u003C/b\u003E (public preview), che permette agli amministratori di configurare quali agenti AI, skill, connettori e istruzioni ciascun team vede. L’esperienza CoWork del team finance può indirizzare le domande a un agente AI di pianificazione finanziaria, mentre quella del team marketing le indirizza a un agente AI di analisi delle campagne, tutto senza ampliare gli accessi. La stessa domanda raggiunge automaticamente la competenza giusta, così gli utenti fanno domande in modo naturale invece di scegliere da un menu di agenti AI che non riconoscono.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-4ad2958a05","height":"1077","src":"/adobe/dynamicmedia/deliver/dm-aid--940b69ec-ab1d-4d25-9144-97a5f3c70d3d/admin-enabling-profile.gif?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"alt":"Gif of Snowflake CoWork profiles feature","width":"1920",":type":"snowflake-site/components/image"},"blog_text_97780628":{"id":"blog-text-a015262cae","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EInoltre, \u003Cb\u003ESnowflake Dashboards \u003C/b\u003E(public preview) permette a chiunque nel team di dialogare con la propria dashboard tramite CoWork: riassumerla, chiedere cosa ha determinato un cambiamento o filtrarla su un segmento. Gli analisti sviluppano le dashboard descrivendole a CoCo e condividendole con i ruoli corretti. I dati dietro la dashboard restano in Snowflake, governati dalle policy di row access e masking già esistenti.\u003C/p\u003E\n\u003Cp\u003ELe dashboard creano una fonte di verità condivisa che permette ai team di avere conversazioni basate sull’AI che attingono a dati governati e a un contesto condiviso. Snowflake accoglie il team \u003Ca target=\"_blank\" href=\"https://mora.com/\"\u003EMora\u003C/a\u003E per contribuire a rafforzare questi sforzi, \u003Cb\u003Eintegrando l’approccio innovativo di Mora all’AI per la BI nella piattaforma Snowflake.\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EPer esigenze che vanno oltre le dashboard, come l’esplorazione personalizzata, visualizzazioni avanzate o workflow multistep, i team possono creare app \u003Ca target=\"_blank\" href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E oppure app React e \u003Ca target=\"_blank\" href=\"http://next.js\"\u003ENext.js\u003C/a\u003E su \u003Ca target=\"_blank\" href=\"https://docs.snowflake.com/en/developer-guide/snowflake-app-runtime/about-snowflake-app-runtime\"\u003ESnowflake App Runtime\u003C/a\u003E con AI conversazionale integrata. \u003C/p\u003E\n\u003Cp\u003EAnche le funzionalità più ampie di CoWork si stanno espandendo a Expedition. Le \u003Cb\u003EAutomations\u003C/b\u003E eseguono lavoro ricorrente secondo una pianificazione, con gli stessi permessi di una domanda live. La \u003Cb\u003EUser Memory\u003C/b\u003E (in GA) e \u003Cb\u003EUser Skills \u003C/b\u003Etrasportano il contesto da una sessione all’altra, così nessuno deve rispiegare come lavora il team. \u003Cb\u003EMCP connectors, code execution e generazione di documenti\u003C/b\u003E (PDF, PPTX, DOCX, HTML) trasformano le risposte in prodotti di lavoro condivisibili, e \u003Cb\u003EHTML Reports\u003C/b\u003E (public preview) permette ai team di pubblicare report curati e governati direttamente da una conversazione CoWork. \u003C/p\u003E\n\u003Cp\u003E\u003Ca target=\"_blank\" href=\"https://www.snowflake.com/it/blog/snowflake-cowork-profiles-automations-dashboards\"\u003EPer maggiori dettagli, leggi il nostro blog sui nuovi miglioramenti di CoWork.\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_830892397":{"id":"blog-title-7dc8b09cc0","propertiesId":"snowflake-bots-sempre-attivi-sempre-sul-pezzo","type":"heading3","lines":["Snowflake Bots: sempre attivi, sempre sul pezzo"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_516133111":{"id":"blog-text-7dd786c19f","text":"\u003Cp\u003E\u003Cb\u003ESnowflake Bots\u003C/b\u003E (presto in private preview) portano in CoCo e CoWork agenti AI proattivi e orientati agli obiettivi. Assegna a un bot una responsabilità, come mantenere in salute le pipeline critiche o monitorare il customer churn, e lui si farà carico dell’obiettivo dall’inizio alla fine: suggerisce quali segnali monitorare, pianifica il lavoro e porta a termine il task nell’arco di giorni o settimane sui tuoi dati governati, nel rispetto dei permessi esistenti. \u003C/p\u003E\n\u003Cp\u003EIl bot ti coinvolge solo quando una decisione richiede l’intervento umano e ti aggiorna quando il processo è completato, così il tuo team può dedicare meno tempo a monitorare il lavoro e più tempo al lavoro strategico e creativo che solo le persone possono svolgere.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_2117682272":{"id":"blog-title-9986fbf87e","propertiesId":"app-raggiungibili-dagli-agenti-ai-e-integrabili-oltre-snowflake","type":"heading3","lines":["App raggiungibili dagli agenti AI e integrabili oltre Snowflake"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1914023450":{"id":"blog-text-20401a821a","text":"\u003Cp\u003ENuovi miglioramenti offrono alle app Snowflake, sia app aziendali interne che app orientate ai clienti, una portata più ampia, maggiore flessibilità e supporto nativo per i workflow agentici. \u003C/p\u003E\n\u003Cp\u003ELe app \u003Ca href=\"https://www.snowflake.com/en/product/features/streamlit-in-snowflake/\" target=\"_blank\"\u003EStreamlit in Snowflake\u003C/a\u003E acquisiscono diverse funzionalità che ampliano la loro portata e le loro capacità conversazionali: \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/features/sharing-streamlit-apps\" target=\"_blank\"\u003EUser-based sharing\u003C/a\u003E (in GA): \u003C/b\u003EI team possono condividere le app con singoli utenti, oltre che con i ruoli. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESupporto per l’embedding (in GA): \u003C/b\u003ELe app Streamlit sono ora \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/features/embedding/overview\" target=\"_blank\"\u003Eintegrabili\u003C/a\u003E in portali e strumenti di terze parti come Salesforce, senza richiedere un login Snowflake e preservando l’accesso governato. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EIndividuabilità e visibilità: \u003C/b\u003ELe app possono essere mostrate come CoWork Artifacts in CoWork (private preview).\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EFunzionalità conversazionali: \u003C/b\u003EGli utenti possono avere conversazioni affiancate con CoCo all’interno di Snowsight (in GA) e CoWork (private preview) sui dati delle proprie app. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EEsposizione automatica MCP (public preview): \u003C/b\u003EOgni app si trasforma in un insieme di strumenti richiamabili dagli agenti AI, invocando direttamente dati e azioni dell’app.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"wistia_video":{"id":"wistia-video-a82a5db25c","title":{"id":"title","type":"heading1","lines":["Sviluppa più velocemente e lavora in modo più intelligente con app e agenti AI ancorati ai dati"],":type":"snowflake-site/components/title"},"wistiaVideoId":"7dok3l7q40","showVideoLength":true,"showRewatchButton":true,":type":"snowflake-site/components/wistia-video"},"blog_text_1901730580":{"id":"blog-text-6c273ebfcf","text":"\u003Cp\u003EPer le web app full-stack, \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-app-runtime/about-snowflake-app-runtime\" target=\"_blank\"\u003EApp Runtime\u003C/a\u003E\u003C/b\u003E è una soluzione end‑to‑end per passare dal prompt alla web app distribuita all’interno del tuo account Snowflake. Descrivi la tua idea di app a Snowflake CoCo, che può svilupparla e pubblicarla all’interno del tuo account Snowflake, con SSO, controllo degli accessi basato sui ruoli (RBAC) e audit logging già attivi. \u003Cb\u003EServerless compute \u003C/b\u003E(presto in public preview) consente alle app di scalare a zero quando sono inattive e di riattivarsi in pochi secondi quando qualcuno le apre, così i team pagano in base all’utilizzo effettivo invece che per capacità di calcolo sempre attiva. \u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EWorkspaces integration\u003C/b\u003E (presto in public preview) consente ai team di sviluppare e distribuire interamente all’interno di Snowsight quando gli strumenti di sviluppo locali sono limitati. Con \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code-agent-sdk/cortex-code-agent-sdk\" target=\"_blank\"\u003ECortex Agent SDK\u003C/a\u003E, app.yml v2, i target di deployment e lo scaling orizzontale, App Runtime può offrire un percorso chiaro dal prototipo generato da CoCo alle operazioni di produzione.\u003C/p\u003E\n\u003Cp\u003EI miglioramenti a \u003Ca href=\"https://www.snowflake.com/en/product/features/native-apps/\" target=\"_blank\"\u003ESnowflake Native Apps\u003C/a\u003E ampliano anche ciò che i partner ISV e i fornitori di app possono sviluppare. Utilizzare \u003Cb\u003EApp Runtime come frontend per Snowflake Native Apps \u003C/b\u003E(presto in public preview) offre ai fornitori un framework web full-stack per l’esplorazione avanzata dei dati e workflow complessi. Snowflake Native Apps ora \u003Cb\u003Ehanno accesso anche a Snowflake Postgres\u003C/b\u003E (presto in public preview), quindi i fornitori possono creare e interrogare istanze Postgres direttamente nell’account senza soluzioni alternative di integrazione per l’accesso esterno. \u003C/p\u003E\n\u003Cp\u003ETutte queste funzionalità si combinano per rendere le app Snowflake una parte cruciale dell’impresa agentica. Sia le app interne che quelle rivolte all’esterno sono ora raggiungibili dagli agenti AI, integrabili in ambienti oltre Snowflake e basate su una data foundation di dati ben governati.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_187672774":{"id":"blog-title-f74a5c3ad6","propertiesId":"effetto-moltiplicatore-trasforma-i-successi-individuali-degli-agenti-ai-in-un-vantaggio-organizzativo","type":"heading2","lines":["Effetto moltiplicatore: trasforma i successi individuali degli agenti AI in un vantaggio organizzativo"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_420861939":{"id":"blog-text-05963e429c","text":"\u003Cp\u003EIl vantaggio organizzativo di agenti AI ben ancorati ai dati si realizza quando il successivo agente AI si basa su tutto ciò che il precedente ha imparato, e workflow comprovati, contesto di team e instradamento dei modelli efficiente in termini di costi vengono condivisi di default, invece di essere ricreati per ogni progetto.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-skill-plugin-sharing\"\u003ESkills and Plugins Sharing\u003C/a\u003E (in GA) trasforma il workflow di un esperto in un asset riutilizzabile e governato in tutto il tuo account Snowflake. Sviluppa una skill per la revisione delle pipeline o una skill per la qualità dei dati una sola volta, quindi condividila come link snow:// oppure rendila individuabile nel catalogo ricercabile di Snowsight. I colleghi che dispongono dell’accesso possono installarla in CoCo Desktop, Snowsight CoCo o nella CoCo CLI. Gli amministratori certificano le versioni attendibili, fissano quelle utilizzate dagli agenti AI e monitorano l’adozione. I tuoi team consumano meno token per risolvere di nuovo lo stesso problema e ottengono output più coerenti, mentre i tuoi agenti AI seguono playbook verificati invece di improvvisare.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAgent Profiles\u003C/b\u003E portano impostazioni predefinite specifiche per team (come istruzioni, connettori e sorgenti dati preferite) in ogni sessione. Insieme a \u003Cb\u003EMemory\u003C/b\u003E, che mantiene il contesto personale tra le sessioni, gli agenti AI iniziano ogni conversazione con piena consapevolezza di come lavora il team, di cosa è stato discusso l’ultima volta e di quali convenzioni contano. Nessuno deve ripetere lo stack tecnologico o le convenzioni di naming ogni lunedì mattina.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EModel Tiers\u003C/b\u003E con instradamento automatico assegnano ogni task al modello giusto per il processo. I task di riepilogo e formattazione di routine vengono eseguiti su modelli efficienti in termini di costi, mentre i task di reasoning complesso vengono instradati verso modelli più capaci. I team ottengono la qualità di cui hanno bisogno senza dover selezionare manualmente i modelli o spendere troppo per task semplici.\u003C/p\u003E\n\u003Cp\u003EInfine, \u003Cb\u003EAgentic Marketplace Discovery\u003C/b\u003E (public preview) porta CoCo nel Marketplace Snowflake all’interno di Snowsight. Gli utenti possono descrivere ciò di cui hanno bisogno, come “dati firmografici aziendali per account EMEA”, e perfezionarlo attraverso la conversazione mentre CoCo individua e confronta dati, app e prodotti AI corrispondenti. Dalla stessa conversazione, gli utenti possono esplorare un listing, installarlo, avviare una prova o richiedere un preventivo, quando il listing lo consente e il loro ruolo lo permette. La discovery diventa il primo passo per mettere a frutto i nuovi dati.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_1396145136":{"id":"blog-title-3adc95e8a5","propertiesId":"sviluppa-e-utilizza-agenti-ai-che-conoscono-il-tuo-business-e-svolgono-un-lavoro-reale","type":"heading2","lines":["Sviluppa e utilizza agenti AI che conoscono il tuo business e svolgono un lavoro reale"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_2048836720":{"id":"blog-text-19dd0e39dc","text":"\u003Cp\u003EQuesti annunci dimostrano l’impegno Snowflake verso un principio di design comune: Gli agenti AI dovrebbero funzionare sulla stessa piattaforma dei dati di cui hanno bisogno, ereditare la governance già esistente e migliorare con l’uso. Quando gli agenti AI sono ancorati ai tuoi dati e dispongono dei guardrail appropriati per agire con permessi reali, possono fornire risultati e risposte affidabili senza aggiungere attrito ai tuoi workflow AI.\u003C/p\u003E\n\u003Cp\u003EScopri altri annunci di Snowflake Expedition su \u003Ca target=\"_blank\" href=\"http://snowflake.com/en/blog/expedition-26/\"\u003Esnowflake.com/en/blog/expedition-26/\u003C/a\u003E, oppure partecipa all’evento virtuale su \u003Ca target=\"_blank\" href=\"http://snowflake.com/en/expedition/\"\u003Esnowflake.com/en/expedition/\u003C/a\u003E.\u003Cbr\u003E\n\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EDichiarazioni previsionali: Questo articolo può 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 differire. Fai riferimento al nostro più recente modulo 10‑Q per ulteriori informazioni.\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E\u003Csup\u003E1 \u003C/sup\u003EMetodologia di confronto dei costi: Il pricing di Fireworks e Tinker si basa sui prezzi pubblicati il 22 ottobre 2026 sulle rispettive pagine web. Abbiamo eseguito workload di training LoRA SFT su Cortex Training con diverse lunghezze di contesto e misurato il throughput per GPU H200. Abbiamo utilizzato il throughput (toks/sec) per calcolare il prezzo per milione di token per Cortex Training usando la formula \u003Ci\u003E(price_per_second / throughput) x 1M.\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_1940305924":{"id":"blog-title-62b5f65028","propertiesId":"domande-frequenti","type":"heading2","lines":["Domande frequenti"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_502359727":{"id":"blog-text-1b6520ed76","text":"\u003Ch4\u003ECosa avete annunciato riguardo agli agenti AI a Snowflake Expedition 2026?\u003C/h4\u003E\r\n\u003Cp\u003ETra gli annunci principali figurano la modalità CoCo in Snowsight, i Cloud Agents per i workflow di coding autonomi, i CoWork Profiles per l’instradamento degli agenti AI specifico per team, gli Snowflake Dashboards in CoWork, Cortex Training per la personalizzazione di modelli open-weight, App Runtime con serverless compute, Skills and Plugins Sharing per il riutilizzo organizzativo e Agentic Marketplace Discovery per il sourcing conversazionale dei dati.\u003C/p\u003E\r\n\u003Ch4\u003ECome fa Snowflake a rendere più affidabili gli agenti AI?\u003C/h4\u003E\r\n\u003Cp\u003ESnowflake ancora gli agenti AI a dati governati attraverso Cortex Sense, che crea automaticamente il contesto a partire da schemi, metriche e business logic. Gli agenti AI ereditano i permessi esistenti, le policy di accesso righe e le regole di masking, così possono rispondere basandosi su definizioni autorevoli invece di indovinare. Le azioni degli agenti AI vengono eseguite con i permessi Snowflake dell’utente o del proprietario e sono verificabili.\u003C/p\u003E\r\n\u003Ch4\u003ECos’è Cortex Sense?\u003C/h4\u003E\r\n\u003Cp\u003ECortex Sense (presto in public preview) crea automaticamente il contesto di business a partire dai tuoi schemi, dalle definizioni delle metriche e dalla business logic esistenti. È progettato per eliminare le settimane di prompt engineering manuale generalmente necessarie per far comprendere i tuoi dati a un agente AI, e funziona sui tuoi dati, sui dati condivisi e sui dati del Marketplace.\u003C/p\u003E\r\n\u003Ch4\u003ECos’è CoCo Agent Mode? \u003C/h4\u003E\r\n\u003Cp\u003ECoCo Agent Mode offre all’agente AI di coding di Snowflake un ambiente di lavoro a schermo intero basato su un container cloud. I data engineer possono avviare migrazioni, build o analisi di lunga durata, chiudere il laptop e tornare a risultati già pronti. Le sessioni vengono eseguite con i permessi Snowflake dell’utente, con un ambito di sessione limitato per controlli aggiuntivi.\u003C/p\u003E\r\n\u003Ch4\u003ECosa sono i CoWork Profiles?\u003C/h4\u003E\r\n\u003Cp\u003EI CoWork Profiles (public preview) consentono agli amministratori di configurare un’esperienza degli agenti AI su misura per ogni team, specificando quali agenti AI, skill, connettori e istruzioni sono disponibili. La stessa domanda viene instradata automaticamente verso la competenza giusta, senza ampliare l’accesso ai dati.\u003C/p\u003E\r\n\u003Ch4\u003ESnowflake offre una piattaforma per lo sviluppo di app?\u003C/h4\u003E\r\n\u003Cp\u003ESì. Puoi sviluppare e distribuire app di livello enterprise su Snowflake, tra cui app di esplorazione dei dati, web app full-stack e data product destinati all’esterno. Streamlit in Snowflake viene utilizzato per sviluppare app di dati interne basate su Python, come dashboard e visualizzazioni di dati, analisi dei dati esplorativa e workflow leggeri. Snowflake App Runtime ti consente di sviluppare app full-stack con UI personalizzate, tra cui app avanzate di esplorazione dei dati, app per workflow complessi e app/agenti AI. Snowflake Native App Framework serve per sviluppare data product monetizzabili destinati all’esterno, che possono essere inseriti e distribuiti nel Marketplace Snowflake.\u003C/p\u003E\r\n\u003Ch4\u003EIn cosa differisce App Runtime da Streamlit in Snowflake?\u003C/h4\u003E\r\n\u003Cp\u003EStreamlit in Snowflake è Python-first e progettato per dashboard e visualizzazioni di dati, analisi dei dati esplorativa e workflow leggeri. App Runtime è pensato per le web app full-stack: esplorazione avanzata dei dati, workflow complessi e app e agenti AI che richiedono UI e UX personalizzate, librerie JavaScript specializzate, navigazione su misura o endpoint API. 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