[{"data":1,"prerenderedAt":1045},["ShallowReactive",2],{"/ja-jp/the-source/ai/agentic-ai-unlocking-developer-potential-at-scale/":3,"footer-ja-jp":57,"the-source-banner-ja-jp":366,"the-source-navigation-ja-jp":378,"the-source-newsletter-ja-jp":405,"article-site-categories-ja-jp":416,"agentic-ai-unlocking-developer-potential-at-scale-article-hero-category-ja-jp":418,"agentic-ai-unlocking-developer-potential-at-scale-the-source-gated-asset-ja-jp":441,"agentic-ai-unlocking-developer-potential-at-scale-category-ja-jp":456,"agentic-ai-unlocking-developer-potential-at-scale-the-source-resources-ja-jp":468,"agentic-ai-unlocking-developer-potential-at-scale-article-hero-author-ja-jp":496},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":8,"seo":15,"content":19,"type":49,"category":5,"slug":50,"_id":51,"_type":52,"title":7,"_source":53,"_file":54,"_stem":55,"_extension":56},"/ja-jp/the-source/ai/agentic-ai-unlocking-developer-potential-at-scale","ai",false,"",{"layout":9,"template":10,"articleType":11,"author":12,"featured":13,"gatedAsset":14},"the-source","TheSourceArticle","Regular","emilio-salvador",true,"source-lp-ai-guide-for-enterprise-leaders-building-the-right-approach",{"title":16,"description":17,"ogImage":18},"デベロッパーの可能性を大きく開放する自律型AI","自律型AIは、コード補完の枠を超え、複雑なタスクに主体的に挑むパートナーへと進化しつつあり、ソフトウェア開発を大きく変革しようとしています。","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463876/kiw4eb54r8xtzztvbozf.jpg",{"title":16,"date":20,"description":17,"timeToRead":21,"heroImage":18,"keyTakeaways":22,"articleBody":26,"faq":27},"2025-04-08","6分で読めます",[23,24,25],"AIエージェントは、コードベースのモダナイゼーションといった複雑なタスクを自律的に処理することで、開発時間を数週間から数時間に短縮し、重要なシステムに対しては、引き続き人間による柔軟な監視体制を維持できます。","従来の単純なコードアシスタントとは異なり、AIエージェントは他のエージェントと協力してさまざまなタスクを実行できるため、デベロッパーはイノベーションや価値の高い問題解決に集中できるようになります。","さまざまなモデルを搭載した専門のAIエージェントは、セキュリティやテストなど特定分野のタスクの実行に優れており、汎用的なソリューションよりも優れた成果をもたらします。","AIはすでに、デベロッパーの働き方を変えています。[GitLabの調査](https://about.gitlab.com/ja-jp/developer-survey/2024/ai/)によると、2024年にはDevSecOpsの専門家のうち39%がソフトウェア開発にAIを使用していると報告しており、前年比で16%増加しています。AI搭載のコードアシスタントは、コードの作成支援やコードベースの理解、ドキュメント作成をサポートする、今や一般的なツールとなっています。しかし現在、さらに大きな変化が起きています。受動的なサポーターではなく、能動的なパートナーとして働くAIエージェントが登場してきているのです。\n\n受け身のアシスタントから能動的なエージェントへの変化は、デベロッパーのソフトウェア開発のあり方を大きく変えつつあります。自律型AIにより、誰もが簡単にソフトウェアを作れる時代が到来し、数十億人に届く革新的なプロダクトが次々と生まれることで、イノベーションの波は加速しています。ただし、この新たなイノベーションの波をリスクなく最大限に活用するためには、リーダーたちが、強固なセキュリティとコンプライアンスのガードレールを備えた自律型AIソリューションを見極める必要があります。\n\n## AIエージェントとAIアシスタントの違いとは？\nAIアシスタントとエージェントの主な違いは、その挙動にあります。コードアシスタントは受動的で、デベロッパーからの質問やリクエストを待ちます。コーディングのスピードアップやコード理解を助ける点では有効ですが、開発プロセスの中ではあくまで受け身の存在です。\n\n一方で、AIエージェントはチームメンバーのように振る舞います。推論や計画を行い、異なるタスク間でもコンテキストを維持しながら、ある程度の自律性を持って意思決定を下したり、他のエージェントと連携したり、状況の変化に適応したりします。エージェントへの移行により、AIはソフトウェア開発における真のパートナーとなります。\n\nチームがあらゆる仕事を担当する中、コーディングだけをサポートするアシスタントとは異なり、AIエージェントはセキュリティチェックやコンプライアンスレビューといった複雑なプロセス全体を積極的に管理できます。たとえば、コードレビューエージェントは、自動でコードをチェックし、問題を検出し、修正案を提示できます。アシスタントが各ステップで人間のインプットを必要とするのに対し、エージェントはプロジェクト目標に基づいてタスク間を自律的に移動できます。また、単純なアシスタントが過去のやり取りを記憶したり、ミスから学んだりできないのに対して、エージェントは時間とともに学習・適応することもできます。\n\n## 自律性の範囲\nAIエージェントの最も強力な特長のひとつは、柔軟に設定を変えたり、対話レベルを調整したりできる点です。エージェントによっては人間と多くやり取りするものもあれば、ほとんど、あるいは全く人間の介入なしに、複雑なタスクをバックグラウンドで実行できるものもあります。そのためチームは、エージェントの担当業務やタスクの重要度に応じて、人間による関与のレベルを柔軟に設定できます。\n\nたとえば、コードの要約やドキュメントの下書きのようなシンプルなタスクであれば、エージェントに独立して作業を進めさせ、完了時にだけ人間に通知する設定にすることも可能です。一方、中核となるビジネスロジックや機密データを扱う重要なタスクでは、承認チェックポイントを設けたり、エージェントの作業を密にモニタリングしたりできます。\n\nこの柔軟性によって、自動化によるスピード感と人間によるコントロールのバランスを取ることができます。オールオアナッシング、という極端な選択ではなく、タスクの種類や開発ライフサイクルのステージに応じて、自律性のレベルを細かく調整できるのです。\n\n## 特化型の力\n現在のAIコードアシスタントは、通常、一つの大規模言語モデルを使用しています。しかし、これからの時代は、異なるそれぞれのタスクに特化したモデルを搭載した、多くの特化型エージェントが登場するでしょう。\n\nすでに、以下のようなタスクに特化したエージェントが現れ始めています。\n- コードモダナイゼーション（既存のコードベースを新しい言語バージョンに変換する）\n- セキュリティ脆弱性の検出と修正\n- テストの生成と実行\n- パフォーマンス最適化\n- ドキュメント生成\n- パイプラインの失敗の根本原因分析\n\nこれらのタスクは、それぞれ専用に設計されたモデルを使うことで、最もよい結果が得られます。専門化することで、エージェントは万能型になろうとせずに、その特定のタスクにおいて優れた成果を発揮できます。\n\n今まさに生まれつつあるのは、特定のタスクに最適化された、搭載モデルの異なる複数のエージェント同士が連携して働くエコシステムです。このマルチモデルアプローチにより、単一の汎用モデルですべての開発タスクを処理しようとするよりも、より良い成果が期待できます。\n\n## AIエージェントが実社会に与える影響\nこれまで数週間かかっていたタスクが、AIエージェントを使えば数時間で完了できるようになっています。たとえば、大規模なJavaコードベースを新しいバージョンに更新する作業は、以前ならチームで何週間もかかっていたような作業ですが、エージェントによってはるかに高速に処理できるようになりました。\n\nさらに重要なことに、AIエージェントはデベロッパーの潜在能力を最大限に引き出す役割を果たします。ルーチンタスクをエージェントが肩代わりすることで、デベロッパーたちは本来得意とする複雑な問題解決や新しいソリューションの創出に集中できるようになります。これは、AIがデベロッパーを置き換えるという話ではなく、デベロッパーの能力を高め、より高次の思考やイノベーション、人間のインサイトが必要なクリエイティブな仕事に集中させるということです。\n\nAIエージェントを活用することで、これまで個人やチームでは実現できなかったスケールでの開発が可能になります。これにより、従来のプロンプトベースで受け身だったAIの作業が、ソフトウェア開発全体をつなぐ能動的な一連の作業へと移行し、コーディングから計画、デザイン、テスト、リリース、メンテナンスに至るまでサポートできるようになります。\n\n## AIエージェントの導入時に考慮すべきこと\nソフトウェア開発やコードの急速な成長に備えるために、企業は事前に計画を立てる必要があります。AIエージェントをプロセスに取り入れる前に、次の重要なポイントに注目しましょう。\n\n1. **単に新しいツールやプロセスをチームに覚えさせるだけでなく、実際に生産性を向上させる方法を考える。** [自律型AIワークフローをDevSecOpsプラットフォームの一部として導入する](https://about.gitlab.com/blog/gitlab-duo-workflow-enterprise-visibility-and-control-for-agentic-ai/)ことで、[AIスプロール（AIが乱立している状態）](https://about.gitlab.com/the-source/ai/overcome-ai-sprawl-with-a-value-stream-management-approach/)を避けながら、デベロッパーが顧客に価値を届ける作業により多くの時間を割けるようサポートできます。また、プラットフォームに組み込まれたレポートやダッシュボード機能を活用すれば、[成功を測定](https://about.gitlab.com/the-source/ai/4-steps-for-measuring-the-impact-of-ai/)し、チームが正しい方向に進んでいるかを確認できます。\n2. **チーム全体で効果を発揮するソリューションを探す。**優れたAIエージェントは、一部のデベロッパーだけでなく、チーム全体の生産性を高めます。\n3. **セキュリティとコンプライアンスを優先する。**AIによる本番環境向けコードの生成が増える中、安全なソフトウェア開発をスケールさせるためには、包括的なDevSecOpsプラットフォームが欠かせません。規制の厳しい業界で働いている場合は、AIエージェントソリューションが厳格なセキュリティおよびデータプライバシー規則に準拠しているかを必ず確認しましょう。オフライン環境や[インターネット未接続（エアギャップ）のシステム](https://about.gitlab.com/the-source/ai/transforming-government-it-ai-for-air-gapped-environments/)で動作できるかどうかも、必要なセキュリティレベルに応じて確認してください。\n4. **人間によるエンタープライズコントロールを前提としたソリューションを選ぶ**。AIエージェントには、人間をプロセスに関与させ続けるための明確な承認ワークフローや柔軟なガードレールが求められます。このバランスを取ることで、自動化によるスピード感を維持しつつ、重要なシステムや戦略的意思決定に不可欠な適切なガバナンスを確保できます。\n\n自動化されたセキュリティスキャン、コンプライアンスガードレール、および標準化されたワークフローを備えたエンドツーエンドのDevSecOpsプラットフォームを活用する企業は、不要なリスクを増やすことなく、AIエージェントのメリットを最大限に引き出す体制を整えられます。適切なプラットフォームなしでは、AIエージェント導入に伴う複雑さやリスクをうまく管理できず、安全で信頼できる顧客体験を提供するのが難しくなってしまうでしょう。\n\n## 今後の展望\nソフトウェア開発におけるAIエージェント革命はまだ始まったばかりです。これらのツールが成熟するにつれて、人間のデベロッパーとAIエージェントのチームワークがさらに向上し、エージェントはソフトウェア開発におけるより強力なパートナーとなっていくでしょう。\n\n未来を見据えると、コードアシスタントとAIエージェントの融合が進む大きな可能性も見えてきます。コードアシスタントは、コーディングタスクをより自律的に処理したり、開発フローの中で積極的に問題解決を行ったり、他の開発ツールやプロセスとより深く統合されたりするなど、さらに高度なAIエージェント機能を取り込む方向へ進化していくでしょう。将来的には、単なるコード生成だけでなく、高レベルな要求事項に基づいて自律的にデバッグ、テスト、デプロイまでも行えるような、より自律性の高い「コードエージェント」へと進化する可能性もあります。\n\nこの50年あまりでソフトウェアは世界を大きく変えてきましたが、実際にソフトウェアを作るスキルを持つ人はごくわずかでした。それでも、少数のデベロッパーがスマートフォンやインターネットを通じて何十億人もの人々にソフトウェアを届けてきました。もし、もっと多くの人が、本番環境で使えるレベルのソフトウェアを自ら作り、安全性を確保し、届けられる世界を想像してみてください。自律型AIは、それを現実のものにしていきます。\n\n受け身のアシスタントから能動的な開発パートナーへの移行は、ソフトウェア開発において大きな前進です。こうした特化型エージェントが進化していくことで、ソフトウェア開発はこれまで以上に速く、信頼性が高く、そして開発者にとってやりがいのあるものへと変わっていくでしょう。",[28,31,34,37,40,43,46],{"header":29,"content":30},"ソフトウェア開発における自律型AIとは何ですか？","自律型AIとは、人間のプロンプトを必要とする受動的なコードアシスタントとは異なり、自ら推論し、計画を立て、主体的にタスクを進めることができるAIエージェントを指します。これらのエージェントは、チームメンバーのように振る舞い、最小限のモニタリングで複雑なタスクを遂行し、ソフトウェア開発ライフサイクル全体にわたって能動的なワークフローを実現します。",{"header":32,"content":33},"AIエージェントは従来のコードアシスタントとどう違うのですか？","コードアシスタントがデベロッパーからのプロンプトに応答するのに対し、AIエージェントは自律的に複数ステップにわたるタスクを完了し、他のエージェントと連携し、プロジェクト目標に応じて調整できます。セキュリティスキャン、テスト生成、コードレビューといった機能も、各ステップごとに人間の介入を必要とせずに処理できます。",{"header":35,"content":36},"デベロッパーがAIエージェントを使用するメリットは？","AIエージェントは、コードベースの更新、コンプライアンスチェックの実行、ドキュメントの作成といった時間のかかるタスクを自動化することで、手作業の負担を軽減します。これにより、デベロッパーはイノベーション、問題解決、戦略的開発といったより価値の高い業務に集中できるようになり、最終的には品質を損なうことなく、開発のスピードを加速できます。",{"header":38,"content":39},"AIエージェントは、人間による関与のレベルに応じてカスタマイズできますか？","はい。チームは、タスクの重要度に応じてエージェントの自律性を設定できます。ルーチンタスクではエージェントが独立して動作できる一方、リスクの高い業務やビジネスクリティカルな作業では、人間による承認チェックポイントを組み込んで、ガバナンスとコンプライアンスを維持できます。",{"header":41,"content":42},"特化型AIエージェントは、汎用モデルよりも効果的ですか？","セキュリティ、テスト、根本原因分析といった特定の機能に特化してトレーニングされた特化型AIエージェントは、通常、その分野において汎用モデルよりも高いパフォーマンスを発揮します。このモジュール型・マルチエージェントアプローチにより、ドメインに最適化されたモデルの強みを活かして、精度と効率を向上できます。",{"header":44,"content":45},"自律型AIを導入する際に、企業は何を考慮すべきですか？","組織は、AIエージェントが自社のセキュリティ、コンプライアンス、ガバナンス要件に適合しているかを確認する必要があります。また、AIスプロールを防ぎ、人間による関与を通じてコントロールを維持し、一貫したワークフローによって全社的なアドプションをサポートできるよう、エンドツーエンドのDevSecOpsプラットフォームに統合することが重要です。",{"header":47,"content":48},"自律型AIはソフトウェア開発の未来をどう変革しますか？","自律型AIは、より多くの人が本番環境レベルのソフトウェアを構築・管理できるようにすることで、ソフトウェア開発の民主化を促進します。エージェントがさらに自律性を高め、システムに深く統合されていくことで、イノベーションのサイクルは加速し、コード品質は向上し、開発はより手軽で、スケーラブルかつセキュアなものになっていくでしょう。","article","agentic-ai-unlocking-developer-potential-at-scale","content:ja-jp:the-source:ai:agentic-ai-unlocking-developer-potential-at-scale:index.yml","yaml","content","ja-jp/the-source/ai/agentic-ai-unlocking-developer-potential-at-scale/index.yml","ja-jp/the-source/ai/agentic-ai-unlocking-developer-potential-at-scale/index","yml",{"_path":58,"_dir":59,"_draft":6,"_partial":6,"_locale":7,"data":60,"_id":362,"_type":52,"title":363,"_source":53,"_file":364,"_stem":365,"_extension":56},"/shared/ja-jp/main-footer","ja-jp",{"text":61,"source":62,"edit":68,"contribute":73,"config":78,"items":83,"minimal":354},"GitはSoftware 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