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Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/ja/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-e835d32807","type":"heading2","lines":["概要"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-d1b38372a7","text":"\u003Cp\u003EエンタープライズAIは急速に進化しています。最初の波は、インテリジェンスの拡張に焦点を当てたものでした。組織は、チャットインターフェイス、生産性ツール、アナリティクスプラットフォーム、社内システムに\u003Ca href=\"https://www.snowflake.com/ja/fundamentals/large-language-model/\"\u003E大規模言語モデル\u003C/a\u003E（LLM）を組み込みました。こうしたシステムは、要約、下書き、分析、推奨を行えるため、ナレッジワーク全体にわたって個人の生産性が加速しました。\u003C/p\u003E\n\u003Cp\u003Eこの初期段階は測定可能な成果をもたらしましたが、基本的にはタスク指向でした。ユーザーが質問し、システムが応答を生成して、人間が次のアクションを決定するというものでした。\u003C/p\u003E\n\u003Cp\u003E自律型AIエージェントの登場により、組織は成果にAIの責任を割り当てるようになっています。自律型エージェントは、オンデマンドで出力を生成するのではなく、目標の解釈、計画の構築、ツールへのアクセス、アクションの実行、結果の評価、イテレーションを行います。多くの場合、限定的な監督下で実行されます。\u003C/p\u003E\n\u003Cp\u003EAIはタスクサポートの役割からワークフローオーナーの役割へと移行しています。自律型AIは、ガバナンスの下で複数のシステムで横断的に継続的に動作するデジタル労働力です。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-9eaa729257","additionalClasses":"headline-decoration","type":"heading2","lines":["AIエージェントとは"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-3982fe900b","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E自律型AIエージェントは、限られた継続的な監視下で、環境を知覚し、定義された目標を推論し、アクションを実行できるシステムです。実際には、コンテキストを認識し、利用可能なツールから選択し、新しい情報が出てきたらアプローチを調整します。\u003C/p\u003E\n\u003Cp\u003Eシングルタスク\u003Ca href=\"https://www.snowflake.com/ja/fundamentals/what-are-ai-agents-understanding-their-role-and-impact/\"\u003EAIエージェント\u003C/a\u003Eは、ドキュメントの分類、情報の抽出、特定のプロンプトへの応答の生成などの個別のアクティビティに焦点を当てます。対照的に、自律型エージェントは、1つのアクションを完了して次の指示を待つのではなく、進捗を追跡し、時間をかけて適応していくことが期待されます。\u003C/p\u003E\n\u003Cp\u003Eエンタープライズ設定では、この自律性は明示的な境界内に存在します。システムへのアクセスは範囲指定されており、エスカレーションパスも明確です。システムの独立性は、ポリシー制御と監査要件とのバランスがとれています。 \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_2060034519":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"work",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-8c53f44c1f","additionalClasses":"headline-decoration","type":"heading2","lines":["自律型AIを実現するコア機能"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-2f6d0f5709","text":"\u003Cp\u003E自律システムは、孤立した支援からワークフローオーナーシップへと移行するために、いくつかの主要なアーキテクチャ機能に依存しています。 \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E永続的なコンテキストとメモリ\u003C/h3\u003E\n\u003Cp\u003E自律型エージェントは、すでに実行したこと、適用される制約、時間の経過に伴う結果の変化を常に把握する必要があります。構造化メモリがなければ、各ステップはゼロからスタートします。目標が数日から数週間に及ぶ環境では、正確な状態を維持することが有意義な自律の絶対条件です。コンテキストはログに記録され、構造化され、取得可能でなければなりません。システムは、以前のアクションを繰り返すのではなく、そのアクションに基づいて構築する必要があります。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003Eツールへのアクセスとシステム統合\u003C/h3\u003E\n\u003Cp\u003E目標についての推論は、システムが目標に影響を与えることができる場合にのみ価値があります。そのためには、エンタープライズデータプラットフォーム、CRMシステム、財務アプリケーション、外部サービスへのセキュアな接続が必要です。これらの統合を通じて、エージェントはライブデータのクエリ、レコードの更新、ワークフローのトリガーを実行できます。重要なのは、統合によってケイパビリティが向上するだけでなく、リスクも増大します。すべての接続は、意図的なものであり、範囲を定め、監視する必要があります。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E目標ベースの推論\u003C/h3\u003E\n\u003Cp\u003Eほとんどのエージェント型システムの中心となるのは、推論エンジンとして機能するLLMです。目標が割り当てられると、モデルは計画を生成します。望ましい状態を実現する可能性が最も高いアクションのシーケンスを決定します。代替案を検討し、必要に応じて戦略を修正します。こうした計画レイヤーは、自律型エージェントと静的自動化を区別します。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003Eフィードバックループと適応性\u003C/h3\u003E\n\u003Cp\u003Eエンタープライズ環境が静的であることはほとんどありません。硬直した計画に従ったシステムは、すぐに現実との整合性を失います。自律型エージェントは、状況の変化に適応するためにフィードバックループを繰り返し実行します。各アクションの影響を測定し、新しいシグナルを取り入れ、それに応じてアプローチを修正します。 \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003Eガードレールとガバナンス\u003C/h3\u003E\n\u003Cp\u003EAIの自律性は、定義された権限レベルの枠内で設計される必要があります。たとえば、データへのアクセスはロールベースで、監査ログは各ステップを記録し、エスカレーションパスは曖昧な意思決定や影響力の高い意思決定を人間の監視下に移動させます。ガバナンスは、システム自体の一部である必要があります。明確な境界やオブザーバビリティがなければ、自律性は価値をもたらすよりも早くリスクをもたらします。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 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aem-GridColumn--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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-d8d61a8a7b","additionalClasses":"headline-decoration","type":"heading2","lines":["自律エージェントの仕組み"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-e53fd41268","text":"\u003Cp\u003E自律型AIエージェントは、観察、推論、アクションの継続的なループを通じて動作します。目標が割り当てられると、システムはコンテキストを収集することから始めます。関連するデータを取得して現状を評価し、アプローチを形成する制約を特定します。この観察フェーズは、現在起きていること、以前に起きたこと、適用される制限といったベースラインを確立します。\u003C/p\u003E\n\u003Cp\u003Eそこから、エージェントは推論フェーズに入ります。大規模言語モデルは、システムの認知エンジンとして機能します。LLMは、割り当てられた目標を解釈し、構造化された段階的な計画に分解します。この分解フェーズは重要です。複雑な目標は、システムが実行できる個別のアクションに変換する必要があります。\u003C/p\u003E\n\u003Cp\u003E計画が生成されると、システムはアクションを開始します。エージェントは、\u003Ca href=\"https://www.snowflake.com/ja/fundamentals/data-orchestration/\"\u003Eオーケストレーション\u003C/a\u003Eレイヤーを通じて推論エンジンをツールやエンタープライズシステムに接続します。データベースのクエリ、CRMレコードの更新、ワークフローのトリガー、通信の生成などが可能です。それぞれのアクションが測定可能な成果を生み出します。\u003C/p\u003E\n\u003Cp\u003Eその後、エージェントは再び観察し、結果が期待値と整合しているかどうかを評価します。結果が予測から逸脱した場合、LLMは状況を再評価し、計画を修正して代替戦略をテストします。\u003C/p\u003E\n\u003Cp\u003Eこのサイクルは、目標が達成されるか、時間制限に達するか、ガバナンスの境界がエスカレーションを必要とするまで繰り返されます。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"and",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-9274a1fcc7","additionalClasses":"headline-decoration","type":"heading2","lines":["自律型エージェントと従来のAIエージェントの比較"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-6c86b1d945","text":"\u003Cp\u003E「AIエージェント」と「自律型エージェント」は、同じ意味でよく使用されていますが、機能のレベルは異なります。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E責任の範囲\u003C/h3\u003E\n\u003Cp\u003E通常、AIエージェントはプロンプトに応じて個別のタスクを実行します。要約の生成、入力の分類、レコメンデーションの生成などが可能です。自律型エージェントは、持続的な目標を管理します。複数ステップのワークフローを計画し、長期にわたって関与を維持します。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E自律性の度合い\u003C/h3\u003E\n\u003Cp\u003EAIエージェントは、各タスクを明示的にトリガーして人間による開始を繰り返す必要があります。自律型エージェントは、目標が定義されると独立して運用され、完了またはエスカレーションするまで必要に応じて介入します。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E適応性\u003C/h3\u003E\n\u003Cp\u003E従来のAIエージェントは通常、現在の入力のみに基づいて出力を生成します。自律システムは、プロセスの途中で結果を評価し、フィードバックを取り込み、戦略を修正します。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003Eワークフローオーナーシップ\u003C/h3\u003E\n\u003Cp\u003EAIエージェントはワークフロー内で支援的な役割を果たしますが、自律型エージェントはワークフローの管理責任を担います。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_259718203":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--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-v2-069ace23f8","additionalClasses":"headline-decoration","type":"heading2","lines":["自律エージェントの種類：自律のスペクトル"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-4e5419a52c","text":"\u003Cp\u003Eすべての自律型エージェントが同じ洗練度で機能するわけではありません。企業における展開は、通常、以下のような自律性のスペクトルに沿って進化していきます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003Eシンプルな応答エージェント\u003C/h3\u003E\r\n\u003Cp\u003Eこれらのエージェントは、事前定義されたルールを使用して環境条件に直接応答します。履歴コンテキストを保持しないため、自律性は即時のトリガーに限定されます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003Eモデルベースのエージェント\u003C/h3\u003E\r\n\u003Cp\u003Eモデルベースのエージェントは、環境の内部表現を維持します。行動する前に、履歴データやコンテキストシグナルを考慮します。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E目標ベースのエージェント\u003C/h3\u003E\r\n\u003Cp\u003E目標ベースのエージェントは、定義された目標をどの程度効果的に進めたかに基づいてアクションを評価します。意思決定は、成果の最適化を中心に行われます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E学習エージェント\u003C/h3\u003E\r\n\u003Cp\u003E学習エージェントは、フィードバックを使用して時間をかけて戦略を洗練していきます。観測された結果に基づいて意思決定基準を調整します。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003Eマルチエージェントシステム\u003C/h3\u003E\r\n\u003Cp\u003Eより高度な実装では、複数の専門エージェントが連携します。あるエージェントが情報を収集し、別のエージェントが分析し、さらに別のエージェントがアクションを実行します。エージェント間の調整により、複雑なワークフローオーナーシップが可能になります。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_289473020":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn 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