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Questi sistemi sono progettati per gestire le esigenze di generazione di insight dai dati aziendali, aiutando le organizzazioni ad aumentare l’efficienza e la produttività semplificando i processi. Tuttavia, la loro influenza va al di là della semplice automazione. Gli agenti AI sono in grado di gestire attività complesse e prendere decisioni, un grande balzo in avanti rispetto al software di base attuale. Man mano che l’AI migliora, questi agenti collaboreranno tra loro per pianificare e svolgere le attività, aiutando le organizzazioni a diventare più efficienti.\u003C/p\u003E\n\u003Cp\u003EIn un momento in cui le aziende si affidano sempre di più all’intelligenza artificiale, è essenziale comprendere il ruolo e l’impatto degli agenti AI. Questo articolo esplora cosa sono gli agenti AI, le loro funzionalità, i vantaggi, i tipi di agenti, le applicazioni nel mondo reale e le prospettive future.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"what-is",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-30d3562c9c","additionalClasses":"headline-decoration","type":"heading2","lines":["Cos’è un agente AI?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-abb6e1e5d2","text":"\u003Cp\u003EUn agente AI è una forma di intelligenza artificiale progettata per funzionare in modo autonomo. Può prendere decisioni, eseguire attività e adattarsi all’ambiente specifico senza un intervento umano costante. L’\u003Cb\u003EAI tradizionale\u003C/b\u003E è un sistema basato su regole progettato per attività specifiche, che si basa su algoritmi e logica predefiniti per l’esecuzione di funzioni quali la previsione e la classificazione. L’\u003Cb\u003EAI generativa\u003C/b\u003E si concentra sull’apprendimento di schemi a partire da enormi data set per creare contenuti come testo, immagini o suoni. L’\u003Cb\u003EAgentic AI\u003C/b\u003E, anche detta intelligenza artificiale agentica o basata su agenti, fa un enorme balzo in avanti sfruttando large language model (LLM) e tecniche avanzate di machine learning (ML) per affrontare problemi in più fasi con ragionamento complesso e pianificazione iterativa. Gli agenti AI possono esaminare in modo indipendente fonti di informazioni diverse, giudicarne la pertinenza, l’accuratezza e l’affidabilità, e utilizzare capacità di giudizio e di esecuzione migliorate per raggiungere l’obiettivo dell’utente. \u003C/p\u003E\n\u003Cp\u003EIn un sistema di Agentic AI, più agenti AI possono collaborare per gestire in modo autonomo attività complesse come l’elaborazione delle modifiche agli account, la risoluzione dei problemi di fatturazione e la personalizzazione degli incentivi per migliorare la soddisfazione dei clienti. A tal fine, analizzano informazioni disperse in aree aziendali diverse e in più formati, come database, pagine web e wiki ed email interne, per identificare le informazioni più affidabili e aggiornate. Utilizzano poi il ragionamento, attraverso algoritmi di analisi, riconoscimento degli schemi e algoritmi decisionali, per generare varie soluzioni alternative. Dopo aver scelto l’alternativa migliore, coordinano il piano e lo eseguono. Infine, imparano dai risultati delle proprie azioni per migliorare i processi successivi. Questa capacità di gestire ed eseguire autonomamente attività complesse e imparare dai risultati rappresenta un progresso significativo rispetto alle precedenti iterazioni dell’intelligenza artificiale. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_1767623498":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-9ef1e596ba","additionalClasses":"headline-decoration","type":"heading2","lines":["Come funzionano gli agenti AI"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-dad9b850d3","text":"\u003Cp\u003EGli agenti AI utilizzano tecnologie avanzate come algoritmi di machine learning, elaborazione del linguaggio naturale (NLP) e reti neurali per eseguire le attività in modo autonomo ed efficiente. Queste tecnologie consentono agli agenti AI di analizzare grandi volumi di dati, riconoscere schemi complessi e generare risposte simili a quelle umane. Apprendendo costantemente dai dati che elaborano, gli agenti AI possono adattarsi ai nuovi scenari e migliorare le proprie prestazioni nel tempo.\u003C/p\u003E\n\u003Cp\u003EElaborazione dei dati e processi decisionali sono fondamentali per la funzionalità degli agenti AI, che recuperano e caricano i dati da varie fonti, li elaborano in tempo reale e utilizzano gli strumenti adeguati per orientare le proprie decisioni. Questa adattabilità consente agli agenti AI non solo di eseguire le attività richieste, ma anche di perfezionare le proprie azioni in base ai cambiamenti delle condizioni e alle interazioni con gli utenti. Questo processo li rende versatili in aree come il servizio clienti e l’analisi dei dati, in cui responsabilità dinamiche e adattabilità sono elementi cruciali.\u003C/p\u003E\n\u003Cp\u003EL’integrazione degli agenti AI con piattaforme cloud e data lake ne estende ulteriormente il potenziale. Le piattaforme cloud offrono la scalabilità computazionale necessaria per elaborare data set estesi al di là delle limitazioni dell’infrastruttura locale. Grazie a questa connessione, gli agenti AI possono accedere a dati archiviati in vasti data lake e analizzarli, ricavandone insight significativi. L’integrazione supporta flussi di lavoro più fluidi e offre alle organizzazioni gli strumenti necessari per estrarre più valore dai propri dati, promuovendo l’innovazione e migliorando i processi decisionali.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_1241045613"]},"container_copy_copy_":{"columnClassNames":{"container_copy_copy__92287879":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__674278643":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__557343380":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__893034341":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__502120022":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1291069223":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy_":{"columnClassNames":{"container_copy_copy__238270504":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"machine",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy_":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"challenges",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-056f7bee05","additionalClasses":"headline-decoration","type":"heading2","lines":["Le sfide degli agenti AI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_1241045613":{"id":"text-78bb75f6a1","text":"\u003Cp\u003EPer scalare efficacemente gli agenti AI è necessario offrire loro un accesso sicuro ai dati aziendali e una governance unificata, per garantire che seguano le policy, recuperino informazioni accurate e forniscano risultati affidabili. Tuttavia, l’implementazione di sistemi AI affidabili su vasta scala comporta alcune sfide.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAccuratezza: \u003C/b\u003Ele applicazioni aziendali, soprattutto in ambito finance e engineering, hanno un margine di errore molto ridotto.\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAccesso ai dati governati: \u003C/b\u003Egli agenti devono operare su sorgenti dati strutturate (tabelle, viste) e non strutturate (testo, audio), spesso isolate in silos.\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAffidabilità e sicurezza:\u003C/b\u003E con la crescente diffusione dell’AI, garantire la conformità alle policy di data governance diventa sempre più complesso.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EGli agenti AI diventeranno presto essenziali in azienda, migliorando la produttività nell’assistenza clienti, nelle operazioni sul campo, per l’analisi dei dati e l’engineering. Per garantire il massimo valore e la massima scalabilità riducendo al contempo i rischi, i flussi di lavoro degli agenti AI dovranno garantire precisione, efficienza e governance in ogni fase. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["title_v2","text_copy_1241045613"]},"container_copy_copy__238270504":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1341773628":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"solve",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-e5bc71eb85","additionalClasses":"headline-decoration","type":"heading2","lines":["In che modo gli agenti AI risolvono i problemi: dagli insight all’adattamento"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_1241045613_1341773628":{"id":"text-696157a899","text":"\u003Cp\u003E\u003Cb\u003E1. Percezione:\u003C/b\u003E gli agenti definiscono il compito o il problema da risolvere, quindi individuano e raccolgono i dati dalle fonti più pertinenti, affidabili, accurate e aggiornate disponibili.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E2. Ragionamento:\u003C/b\u003E gli agenti elaborano e interpretano i dati, spesso utilizzando un LLM, per comprendere il contesto e i requisiti e prendere decisioni informate.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\u003E3. Pianificazione:\u003C/b\u003E  in base agli insight derivati dal ragionamento, gli agenti sviluppano piani di azione per raggiungere l’obiettivo.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E4. Coordinamento: \u003C/b\u003Egli agenti condividono i propri piani con utenti o sistemi per garantire allineamento e processi decisionali collaborativi.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E5. Azione:\u003C/b\u003E gli agenti implementano il piano ed eseguono le azioni necessarie.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\u003E6. Apprendimento e adattamento:\u003C/b\u003E gli agenti valutano i risultati e incorporano il feedback per riflettere, determinare i risultati positivi e le opportunità di miglioramento e perfezionare i processi per le attività future. \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_1241045613_1341773628"]}},":itemsOrder":["container_copy_copy_","container_copy_copy__238270504"]},"container_copy_copy__502120022":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-d57e60d45e","additionalClasses":"headline-decoration","type":"heading2","lines":["I vantaggi degli agenti AI"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-8273e0111b","text":"\u003Cp\u003ELe organizzazioni che adotteranno l’Agentic AI per prime sono destinate a beneficiare di sostanziali vantaggi.  \u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EEfficienza e produttività migliorate\u003C/b\u003E: gli agenti AI eccellono nell’automazione delle attività ripetitive e nell’ottimizzazione dei flussi di lavoro. I dipendenti potranno quindi concentrarsi su progetti strategici e sulla risoluzione creativa dei problemi. Il risultato è un miglioramento delle prestazioni di tutti i team e più tempo dedicato all’innovazione e alla crescita.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EMiglioramento di processi decisionali e insight\u003C/b\u003E: i sistemi intelligenti sono in grado di elaborare e analizzare enormi quantità di dati in tempo reale, fornendo insight pratici che consentono alle aziende di prendere rapidamente decisioni meglio informate. Grazie alla capacità di individuare schemi e trend che potrebbero non essere immediatamente evidenti ad analisti umani, gli agenti AI consentono alle organizzazioni di rispondere agilmente ai cambiamenti del mercato.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ERisparmio sui costi e ottimizzazione delle risorse\u003C/b\u003E: riducendo la necessità di interventi manuali nei processi sui dati e nelle attività operative, le organizzazioni possono ridurre al minimo i costi operativi generali e allocare le risorse in modo più efficace. Questo non solo migliora l’efficienza operativa, ma incrementa anche la redditività complessiva, consentendo alle aziende di reinvestire nella crescita e nell’innovazione.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ETime-to-market più rapido\u003C/b\u003E: grazie alla capacità di automatizzare i processi complessi e di fornire analisi dei dati in tempo reale, gli agenti AI possono accelerare i cicli di sviluppo e distribuzione dei prodotti. Semplificando le operazioni e riducendo i colli di bottiglia, le aziende possono lanciare nuovi prodotti, servizi o aggiornamenti più rapidamente, ottenendo un vantaggio competitivo e soddisfacendo richieste sensibili al fattore tempo.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EMaggiore soddisfazione e fidelizzazione dei clienti\u003C/b\u003E: gli agenti AI possono rivoluzionare il modo in cui le aziende interagiscono con i clienti offrendo esperienze personalizzate e rispondendo rapidamente alle richieste. Gli agenti possono analizzare comportamenti e feedback dei clienti per adattare le strategie e fornire soluzioni allineate alle aspettative dei clienti. Questa reattività promuove la fidelizzazione, migliora la soddisfazione e contribuisce a relazioni più profonde e durature con i clienti.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"flexible_column_cont":{"id":"flexible-column-container-0f411f5c7c","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-626df788d4",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"card_v2":{"id":"card-v2-7a0046f915","modalId":"video","configurationStatus":{"configured":true,"message":""},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--78ff4463-7422-4cc3-9146-b3f422f5f2d5/ai-agents-and-data.jpg?quality=85&preferwebp=true","alt":"Texas Health and Human Services","lazyEnabled":true,"width":"1280","height":"720",":type":"snowflake-site/components/image"},"type":"content-card","layoutStyle":"vertical","title":{"id":"title","type":"heading4","lines":["Andrew Ng di Landing AI esplora l’ascesa degli agenti AI e dell’Agentic Reasoning"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"#"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Guarda il video"},":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-328c4bad15",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{},":itemsOrder":[]},"isActiveTOC":false,"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["title_v2","text_copy_1241045613","flexible_column_cont"]},"container_copy_copy__893034341":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_649454223":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1791265668":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1479724203":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_1710778900":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1365519898":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"types",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-f673ffeef5","additionalClasses":"headline-decoration","type":"heading2","lines":["Tipi di agenti AI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613":{"id":"text-a70689a3af","text":"\u003Cp\u003EGli agenti AI possono essere classificati in vari tipi, in base alle funzionalità e applicazioni. Comprendere queste distinzioni è essenziale per utilizzarne con efficacia le capacità.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy":{"id":"title-v2-e5e77d95a4","additionalClasses":"headline-decoration","type":"heading4","lines":["Agenti reattivi e agenti proattivi"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_1479724203":{"id":"text-1101590aba","text":"\u003Cp\u003EQuesta classificazione fondamentale differenzia gli agenti in base al modo in cui reagiscono all’ambiente. Gli \u003Cb\u003Eagenti reattivi\u003C/b\u003E agiscono sul momento, rispondendo agli stimoli ambientali in base a regole predefinite o comportamenti appresi. Eccellono negli ambienti in cui sono necessarie risposte immediate, come i chatbot del servizio clienti che rispondono alle domande degli utenti in tempo reale. Gli \u003Cb\u003Eagenti proattivi\u003C/b\u003E, invece, anticipano le esigenze future e agiscono prima di essere sollecitati. Ad esempio, un sistema di raccomandazione basato su AI può suggerire prodotti in base al comportamento passato dell’utente, migliorandone l’engagement.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_1710778900":{"id":"title-v2-f161a84f06","additionalClasses":"headline-decoration","type":"heading4","lines":["Agenti autonomi e agenti semi-autonomi"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_1791265668":{"id":"text-64c892fb30","text":"\u003Cp\u003EUn’altra distinzione chiave riguarda il grado di indipendenza con cui operano gli agenti AI. Gli \u003Cb\u003Eagenti autonomi\u003C/b\u003E operano in modo indipendente, prendendo decisioni senza intervento umano. Progettati per l’automazione su vasta scala, questi agenti sono spesso utilizzati in applicazioni strategiche e ad alta intensità di dati, ad esempio nei sistemi di trading automatizzati dei mercati finanziari, che operano a velocità senza precedenti per eseguire transazioni ottimali basate su dati in tempo reale, senza attendere l’input umano. Al contrario, gli \u003Cb\u003Eagenti semi-autonomi\u003C/b\u003E affiancano gli operatori umani, migliorando i processi decisionali con insight e suggerimenti, ma richiedendo sempre la supervisione umana. 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Ad esempio, in ambito sanitario gli agenti AI aiutano nella diagnosi delle patologie analizzando in tempo reale cartelle cliniche dei pazienti, schemi e dati di imaging. Consentono diagnosi più rapide e accurate, migliorando gli esiti per i pazienti. In ambito finanziario gli agenti dedicati al rilevamento delle frodi monitorano e analizzano i dati delle transazioni per individuare anomalie, proteggendo aziende e consumatori. Nel settore manifatturiero gli agenti per la manutenzione predittiva analizzano i dati dei macchinari, prevedendo possibili guasti e consigliando azioni preventive per evitare fermi macchina.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy","text_copy_1241045613_1479724203","title_v2_copy_1710778900","text_copy_1241045613_1791265668","title_v2_copy_649454223","text_copy_1241045613_1365519898"]},"container_copy_copy__557343380":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1479724203":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"what-are",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-4456a9a644","additionalClasses":"headline-decoration","type":"heading2","lines":["Cosa sono i data agent?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613":{"id":"text-42cff47d47","text":"\u003Cp\u003EUn \u003Cb\u003EAI data agent\u003C/b\u003E, o \u003Cb\u003Edata agent basato su LLM\u003C/b\u003E, è un sistema intelligente che combina un large language model (LLM) con flussi di lavoro di data engineering e analisi dei dati per automatizzare e semplificare le attività sui dati. A differenza dei data agent tradizionali che seguono regole o script predefiniti, gli AI data agent possono interpretare istruzioni in linguaggio naturale, interagire con database, generare o modificare query ed eseguire attività come discovery, trasformazione, arricchimento e controllo della qualità dei dati, il tutto mediante l’utilizzo di interfacce conversazionali o processi decisionali autonomi. Questi agenti agiscono da intermediari tra gli esseri umani e i sistemi di dati complessi, riducendo le barriere tecniche e accelerando il time-to-insight.\u003C/p\u003E\n\u003Cp\u003ESfruttando le capacità di ragionamento e comprensione contestuale degli LLM, gli AI data agent possono gestire richieste di dati più dettagliate, dedurre l’intento e adattarsi dinamicamente a nuovi requisiti. Possono assistere in attività come generare query SQL da prompt in linguaggio naturale, identificare anomalie dei dati, consigliare trasformazioni o persino orchestrazioni di parti di pipeline di dati. Queste caratteristiche li rendono strumenti potenti nei moderni ecosistemi dati, in particolare per i team che vogliono democratizzare l’accesso ai dati, incrementare la produttività e migliorare la collaborazione tra utenti sia tecnici che non tecnici.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-top-padding-extra-small"},"title_v2_copy":{"id":"title-v2-6adf8f9e3f","additionalClasses":"headline-decoration","type":"heading3","lines":["Agenti AI e data agent: le differenze chiave"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_1479724203":{"id":"text-3db08c11e4","text":"\u003Cp\u003EUn\u003Cb\u003E data agent basato su LLM\u003C/b\u003E è un tipo specializzato di tool AI il cui scopo è aiutare le persone a lavorare con i dati. Utilizza LLM di provider come OpenAI (GPT), Anthropic, Claude e altri per comprendere i comandi in linguaggio naturale e trasformarli in attività sui dati, come scrivere query SQL, riassumere data set, scoprire anomalie nei dati o assistere nella creazione di pipeline di dati. Sono agenti progettati per semplificare e automatizzare il \u003Cb\u003Elavoro sui dati\u003C/b\u003E consentendo agli utenti non tecnici di interagire più facilmente con sistemi di dati complessi.\u003C/p\u003E\n\u003Cp\u003EUn \u003Cb\u003Eagente AI\u003C/b\u003E più generico può invece svolgere una gamma molto più ampia di attività al di là dei dati, come guidare un robot, giocare a un videogame, programmare riunioni o interagire in un chatbot dell’assistenza clienti. Gli agenti AI possono utilizzare diversi tipi di AI (non solo sistemi LLM) e spesso si concentrano sui processi decisionali, sulla pianificazione, sull’apprendimento dall’ambiente o sull’interazione tra sistemi. In breve: i data agent basati su LLM sono specifici per l’attività e incentrati sui dati, mentre i data agent basati su AI sono sistemi più generici e orientati a obiettivi che possono operare su molti domini con complessità variabile.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy","text_copy_1241045613_1479724203"]},"container_copy_copy__92287879":{"columnClassNames":{"text_copy_1241045613_384307662":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1479724203":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_671876296":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"uses",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-1bb506cb27","additionalClasses":"headline-decoration","type":"heading2","lines":["Utilizzi pratici degli agenti AI in azienda "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613":{"id":"text-fc527c6e9d","text":"\u003Cp\u003EGli agenti AI sono rapidamente emersi come strumenti trasformativi in diversi settori, sfruttando la potenza dei dati per fornire insight pratici e promuovere l’efficienza operativa. Questi agenti, basati su algoritmi avanzati, apportano velocità e precisione a processi complessi. Ecco come gli agenti AI stanno trasformando le aree chiave dell’azienda.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy":{"id":"title-v2-9044011e74","additionalClasses":"headline-decoration","type":"heading4","lines":["Analisi dei dati e business intelligence"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_1479724203":{"id":"text-f4c42f0580","text":"\u003Cp\u003EGli agenti AI sono risorse vitali nell’analisi dei dati e nella business intelligence e consentono alle organizzazioni di elaborare enormi quantità di informazioni con un’efficienza senza pari. Esaminando rapidamente enormi data set, questi agenti individuano schemi e trend la cui scoperta da parte di analisti umani richiederebbe molto più tempo. Ad esempio, nel settore retail, gli agenti possono analizzare i dati storici di vendita insieme ai comportamenti in tempo reale dei clienti per prevedere i trend di acquisto futuri e ottimizzare i livelli di inventario. Se integrati nelle piattaforme dati aziendali, gli agenti forniscono inoltre insight in tempo reale per supportare processi decisionali strategici e adattarsi ai cambiamenti del mercato. Ad esempio, un istituto finanziario può utilizzare l’analisi dei dati basata su AI per rilevare le anomalie del mercato e definire strategie di investimento informate.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy":{"id":"title-v2-427e028355","additionalClasses":"headline-decoration","type":"heading4","lines":["Assistenza e servizio clienti "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-none"},"text_copy_1241045613_384307662":{"id":"text-25e2d226f1","text":"\u003Cp\u003EGli agenti AI nelle piattaforme di customer service hanno rivoluzionato il modo in cui le aziende interagiscono con i clienti. Gli agenti AI eccellono nella gestione di grandi volumi di richieste simultanee e forniscono risposte istantanee e precise 24 ore su 24. Automatizzando le query di routine, gli agenti consentono alle persone di concentrarsi su situazioni più complesse o che richiedono un intervento umano. Questo approccio ibrido non solo migliora l’efficienza del servizio, ma aumenta anche la soddisfazione e la fidelizzazione dei clienti. 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Di seguito sono riportati alcuni esempi di applicazione degli agenti AI in diversi settori per promuovere la risoluzione dei problemi e l’eccellenza operativa.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_1241045613_1479724203":{"id":"text-583dfd9815","text":"\u003Ctable\u003E\n\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ESETTORE\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EGLI AGENTI AI POSSONO...\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EMedia\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EIdentificare la performance degli annunci, aggiustare di conseguenza l’allocazione della spesa e testare nuovi contenuti creativi per migliorare il ROI\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EConsigliare contenuti agli utenti in 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