[{"data":1,"prerenderedAt":1022},["ShallowReactive",2],{"/ja-jp/the-source/ai/ai-trends-for-2025-agentic-ai-self-hosted-models-and-more/":3,"footer-ja-jp":36,"the-source-navigation-ja-jp":347,"the-source-newsletter-ja-jp":374,"ai-trends-for-2025-agentic-ai-self-hosted-models-and-more-article-hero-category-ja-jp":386,"ai-trends-for-2025-agentic-ai-self-hosted-models-and-more-the-source-gated-asset-ja-jp":409,"ai-trends-for-2025-agentic-ai-self-hosted-models-and-more-category-ja-jp":424,"ai-trends-for-2025-agentic-ai-self-hosted-models-and-more-the-source-resources-ja-jp":436,"ai-trends-for-2025-agentic-ai-self-hosted-models-and-more-article-hero-author-ja-jp":462},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":8,"seo":16,"content":20,"_id":30,"_type":31,"title":7,"_source":32,"_file":33,"_stem":34,"_extension":35},"/ja-jp/the-source/ai/ai-trends-for-2025-agentic-ai-self-hosted-models-and-more","ai",false,"",{"layout":9,"type":10,"template":11,"slug":12,"articleType":13,"category":5,"author":14,"featured":6,"gatedAsset":15},"the-source","article","TheSourceArticle","ai-trends-for-2025-agentic-ai-self-hosted-models-and-more","Regular","emilio-salvador","source-lp-getting-started-with-ai-in-software-development-a-guide-for-leaders",{"title":17,"description":18,"ogImage":19},"自律型AI、GitLab 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アプリケーションの未来は、インテリジェントで適応性の高いAIエージェントへ\nこれまでのソフトウェア開発におけるAIは、コードの生成や補完を支援する受動的なコードアシスタントが中心でした。しかし、今後の主流となるのは自律型AIです。[インテリジェントで適応性の高いAIエージェント](https://about.gitlab.com/blog/2024/06/27/meet-gitlab-duo-workflow-the-future-of-ai-driven-development/)は、従来のソフトウェアの限界を超えていくでしょう。従来のように決まったインターフェースや事前に設定されたワークフローを操作するのではなく、直感的に反応し、学習し続けるAIエージェントとやり取りできるようになります。\n\nこうしたAI搭載のエージェントは、アプリケーションそのものとして機能し、よりインタラクティブで会話型の体験を提供します。AIエージェントは複雑なタスクを実行したり、ガイダンスを提供したり、ユーザーとのやり取りからリアルタイムで学習したりできるため、自律型AIによってよりパーソナライズされた、柔軟なアプリケーションが実現し、私たちのソフトウェアの使い方そのものを根本から変えていくでしょう。\n\n## 受動的なツールから積極的な協力者へ進化するAIアシスタント\n[AIアシスタントはますます賢くなり](https://about.gitlab.com/gartner-mq-ai-code-assistants/)、受動的なプロンプトベースのツールから、積極的に問題を解決する協力者へと進化しています。この進化の中で、AI搭載のツールは開発の中心的な存在となり、デベロッパーのニーズを予測しながら、アプリケーションのパフォーマンス、セキュリティ、メンテナンスを最適化するための提案をリアルタイムで行えるようになります。この新世代のAIアシスタントは、人間の関与をほとんど必要とせず、複雑なプロジェクトやタスクを処理し、ソフトウェア開発プロセスを加速します。結果として開発のライフサイクル全体が効率化され、シンプルなユーザーインターフェースを通じて、より作業しやすくなります。\n\nこうしたAIの進化に伴い、ソフトウェアデベロッパーの役割も変化していきます。AIは人間のデベロッパーに取って代わるものではなく、人間の能力を拡張し、デベロッパーは最も情熱を注げる領域、つまり複雑な技術的問題の解決に専念できるようになります。また、AIアシスタントはルーチンワークを自動化し、専門的なガイダンスを提供することで、デベロッパーがビジネス課題の解決に深く関わりながら、コード品質を継続的に向上させ、新しい技術やスキルを探求する時間を確保できるようにします。\n\n## より多くの企業がオンプレミスでカスタマイズモデルを運用へ\n2025年には、組織はより小規模で専門性の高いAIのデプロイへとシフトしていきます。オープンソースのAIモデルがより費用対効果が高く、利用しやすくなるにつれて、企業では独自にカスタマイズしたモデルを自社のデータセンターで運用するケースが増えていくでしょう。結果として、企業が[独自の大規模言語モデルをホスティングし、それぞれのニーズに合わせて微調整する](https://about.gitlab.com/releases/2024/10/17/gitlab-17-5-released/#use-self-hosted-model-for-gitlab-duo-code-suggestions)ことが、これまでよりも安価・迅速・容易になります。また、企業は自社のデータと既存のAIモデルを組み合わせることで、現在のコストのごく一部で、カスタマーエクスペリエンスをよりパーソナライズできるようになるでしょう。\n\n一方で、AIに関連するコンプライアンスリスクの増大により、金融機関や政府機関などの規制が厳しい業界では、データの遅延を抑え、プライバシーやセキュリティをより細かく管理するために、インターネット未接続（エアギャップ）環境でAIモデルをデプロイする動きが加速する見込みです。\n\n## 結論\nソフトウェア開発の未来は、AIと密接に関連しています。AI技術は、ソフトウェアの開発、提供、保持のあり方を根本から変革しています。生成AI、能動的なAIアシスタント、完全自律型のAIエージェントといったさまざまなAIを活用することで、企業は競争力を高め、効率を向上させ、変化し続ける顧客のニーズに応える革新的なソリューションを提供できるようになります。\n\nしかし、この変革を成功させるためには、慎重な準備が不可欠です。具体的には、戦略的な計画、優秀な人材やインフラへの投資、そして継続的な学習と適応への取り組みが求められます。この急速に進化する環境にうまく適応できた企業は、デジタル時代で成功するための強い基盤を築くことができるでしょう。\n\n> ## よくある質問\n> ### 自律型AIとは何ですか？ソフトウェア開発にどのような影響を与えますか？\n> 自律型AIとは、自律的に動作し、やり取りから学習し、リアルタイムで適応するAIシステムを指します。従来のプロンプトに応じて反応するAIコードアシスタントとは異なり、自律型AIは積極的に作業を進め、ワークフローの自動化、効率の向上、ユーザーエクスペリエンスのパーソナライズを実現することで、ソフトウェア開発を効率化します。\n> \n> ### なぜ企業はセルフホスト型AIモデルに移行するのですか？\n> 組織は、データプライバシーを強化し、コストを削減し、特定のニーズに合わせてAIソリューションをカスタマイズするために、セルフホスト型AIモデルに移行しています。オープンソースAIの進歩により、企業はオンプレミス環境でモデルを微調整し、機密データを安全に管理しながら、規制へのコンプライアンスを確保し、パフォーマンスを向上させることができます。\n> \n> ### AI搭載のコーディングアシスタントはどのように進化していますか？\n> AIコーディングアシスタントは、事後反応的なツールから積極的な協力者へと進化しています。今後のAIアシスタントは、デベロッパーのニーズを予測し、インテリジェントな提案を行い、複雑なタスクを自動化し、ソフトウェアのセキュリティを強化することで、より効率的で、誰もが作業しやすいソフトウェア開発を実現します。\n> \n> ### オンプレミス環境でAIモデルを運用するメリットは何ですか？\n> オンプレミス環境でAIモデルを導入することで、組織はデータのセキュリティをより細かく管理し、規制要件へのコンプライアンスを向上させ、遅延を減らせます。このアプローチは、金融、医療、政府機関など、機密データを扱う業界にとって特に価値があります。\n> \n> ### 組織は2025年のAI主導のソフトウェア開発にどう備えるべきですか？\n> 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