2026年に企業でエージェンティックAIを使用する方法：青写真

Artificial Intelligence (AI) has evolved from automating simple tasks to enabling complex decision-making processes. At the forefront of this evolution is Agentic AI - autonomous systems capable of executing multi-step tasks with minimal human intervention. These intelligent agents can perceive their environment, reason, and act to achieve specific goals, making them invaluable assets for modern enterprises. 

As businesses grapple with increasing complexity and the need for agility, Agentic AI offers a transformative solution. By integrating these agents into various functions, enterprises can enhance efficiency, reduce operational costs, and foster innovation. 

What Is Agentic AI?

Agentic AI refers to systems capable of acting autonomously to achieve specific goals, rather than just responding to prompts. These AI agents can plan, make decisions, execute tasks, and adapt dynamically to changing environments. They can also coordinate with other agents, access external tools, and manage complex workflows, enabling more intelligent, goal-oriented, and scalable AI solutions compared to traditional reactive AI.

Why Businesses Should Think of Scaling Agentic AI in Enterprises

The integration of Agentic AI is not just a technological upgrade; it's a strategic necessity. Agentic AI—autonomous systems capable of executing complex tasks with minimal human intervention—is rapidly transitioning from a novel concept to a strategic necessity for enterprises. Its potential to revolutionize operations, decision-making, and scalability positions it as a critical focus for Chief AI Officers (CAIOs) and Chief Information Officers (CIOs) aiming to maintain competitive advantage.

• Operational Efficiency: Agents can automate routine tasks, freeing up human resources for more strategic activities. For instance, Omega Healthcare Management Services integrated AI into its operations, automating tasks such as medical billing and insurance claims processing. This integration saved over 15,000 employee hours per month, reduced documentation time by 40%, and cut turnaround time by 50%, achieving 99.5% accuracy and yielding a 30% ROI for clients
  
• Enhanced Decision-Making: With the ability to analyze vast datasets, agents provide insights that inform better business decisions. Snowflake mentions here that the future of Data Management is Agentic AI
  
• Scalability: Agentic systems can adapt to increasing workloads without proportional increases in human oversight.
  
• Competitive Advantage: Early adopters of Agentic AI can outpace competitors by innovating faster and responding more agilely to market changes. Amazon, for example, has established a new research and development team to develop an agentic AI framework for its robotics division. This initiative aims to enhance warehouse robots' capabilities, enabling them to interpret and act on natural language commands and perform multifaceted operations. Similarly, Infosys launched the Agentic AI Foundry as part of its Infosys Topaz™ offerings, deploying over 200 enterprise AI agents to accelerate clients' AI journeys
  

For CAIOs and CIOs, the integration of agentic AI  and scaling them necessitates careful planning. Gartner Hype Cycle for Artificial intelligence report for 2025 places Agentic AI at the peak of inflated expectations - while there has been a lot of hype around it, we believe that 2025 and 2026 will be the years when

Agentic AI will actually scale massively in Enterprises, driven by the formulation of a Scalable BluePrint to drive repeatable Agent build outs across various business functions. Each enterprise should recognise where they are in the overall Hype Cycle of Agentic AI and accordingly position themselves for success. Key considerations include:

• Infrastructure Readiness: Ensuring that the existing IT infrastructure can support the deployment and scaling of agentic AI systems.
  
• Workforce Training: Upskilling employees to work alongside AI agents, fostering a collaborative human-AI environment.
  
• Governance Frameworks: Establishing policies and procedures to manage AI ethics, data privacy, and compliance.

By addressing these areas, CAIOs and CIOs can effectively harness agentic AI's potential, driving innovation and maintaining a competitive edge in the evolving digital landscape. We cover the next section dedicated to the overall architectural blueprint for managing and scaling Agentic AI in Enterprises.

By 2028, agentic AI in enterprises is expected to transform enterprise capabilities, enabling more autonomous, adaptive, and collaborative workflows across organizations.

Architectural Blueprint for Agentic AI in Enterprises: Gateway Integration Model

To effectively deploy Agentic AI, enterprises require a robust architecture that ensures seamless integration, scalability, and security. The Gateway Integration Model serves as a comprehensive framework for this purpose, ensuring centralized governance and federated Execution 

Architecture for Scaling Agentic AI in Enterprises 

Key Components of an Agentic AI System in Enterprise 

• GenAI Applications: These include Retrieval-Augmented Generation (RAG) systems, chatbots, and intelligent agents that interact with users and other systems. People can implement and use frameworks like CrewAI, Agno, LangGraph, InHouse Frameworks etc for building these applications
• AI Gateway: Acts as the central orchestrator, managing requests, enforcing guardrails, logging interactions, providing a centralised observability and handling caching mechanisms for all the models and also a place to manage MCP Servers and A2A communication with proper authentications.
• LLM Fine-Tuning and Deployment: Utilizes logs and feedback to fine-tune Large Language Models (LLMs), which can be then deployed for inference tasks
• Model and API Integrations: Supports integration with various LLM providers, including OpenAI, Anthropic, AWS Bedrock, Azure, and self-hosted models.
• MCP Server and Tools: The Model Context Protocol (MCP) server exposes internal tools and APIs, enabling agents to perform specific tasks effectively. This could be an Atlassian MCP Server, Slack MCP Server, GitHub MCP Server, SalesForce MCP Server, etc
• Agent-to-Agent Protocol (A2A): Facilitates communication and coordination between multiple agents, allowing for collaborative workflows.
• Guardrails: Implement safety measures to ensure compliance and prevent misuse and the above architecture allows to enforce them both at: Input layer (before AI Gateway)  and Model and agent invocation layer (within or post-Gateway). The way you place Corporate guardrails and agent specific guardrails can be accordingly managed and controlled

The above architecture ensures that Agentic AI systems are modular, scalable, and secure, providing a solid foundation for enterprise-wide adoption. For enterprises to scale and not worry about the data flows, enterprises need a foundation layer to enable and run these systems within their VPC, where a central control plane like TrueFoundry comes into play.

Risks of omitting components from the above blueprint 

Omitting even one of the key components in an enterprise Agentic AI stack significantly undermines the system's scalability, reliability, and security, introducing risks that ripple across both technical and organizational dimensions. Without GenAI applications like RAG or chatbots, enterprises lack interfaces for end-users and systems to interact with AI capabilities, effectively silencing downstream automation.

Excluding the AI Gateway removes the central orchestration and observability layer, leading to fragmented governance, lack of unified logging, broken authentication pipelines, and an inability to enforce consistent guardrails—putting compliance and auditability at risk. Missing LLM fine-tuning and deployment hinders the ability to adapt models to proprietary contexts, degrading accuracy and efficiency, while lack of integration with diverse APIs and model providers (OpenAI, Anthropic, AWS Bedrock, etc.) constrains flexibility and vendor redundancy, increasing platform lock-in.

Without MCP servers, agents are decoupled from enterprise tools (like Jira, Salesforce, GitHub), turning powerful AI logic into isolated silos with no way to take meaningful action. Ignoring A2A protocols breaks multi-agent collaboration, reducing agentic intelligence to simple task automation rather than enabling complex, cross-functional workflows. Finally, skipping guardrails or failing to enforce them at both input and invocation layers opens the system to misuse, privacy violations, and unreliable outputs—eroding stakeholder trust.

Each missing piece chips away at the enterprise’s ability to scale confidently, govern responsibly, and respond reliably in mission-critical environments. This is why foundational control planes—like TrueFoundry—deployed within the enterprise VPC are essential: they enforce structure, observability, and secure operations at scale across all components of the Agentic AI lifecycle.

Use Cases for Agentic AI in Enterprises

Let’s now look at the Enterprise specific real-life use of Agentic AI across the various business functions.

1. IT Operations and Incident Management

In the context of IT operations, Agentic AI systems can monitor infrastructure, detect anomalies, diagnose issues, and implement resolutions autonomously, thereby enhancing efficiency and building pro-active maintenance capabilities which can be measured in terms of reduction in mean time to resolution (MTTR) and the reduction in number of Service disruptions or downtimes.

Architecture & Workflow: Agentic AI in Incident Management

A typical Agentic AI-driven incident management architecture encompasses the following components:

1. Monitoring Tools: Systems like Prometheus or Datadog continuously collect metrics and logs from various infrastructure components and you will need the architecture to be able to invoke the MCP servers of these tools.
   
2. AI Agents: These agents analyze the collected data to detect anomalies, identify root causes, and determine appropriate remediation actions.
   
3. Incident Management Systems: Platforms like ServiceNow or PagerDuty receive incident reports from AI agents and coordinate response efforts.
   
4. Communication Channels: Tools such as Slack or Microsoft Teams facilitate communication between AI agents and human operators, ensuring transparency and collaboration. Again the Agent will be interfacing with the MCP servers of Slack, teams etc.
   
5. Knowledge Bases: Repositories of historical incident data and resolutions that AI agents can reference to inform their decision-making processes including access to configuration management databases (CMDBs) and incident response playbooks

Workflow of the above Agentic AI system 

• Detection: AI agents monitor real-time data streams to identify anomalies or performance degradations.
• Diagnosis: Upon detecting an issue, agents analyze logs and metrics to ascertain the root cause.
• Resolution: Agents execute predefined remediation actions or suggest solutions to human operators.
• Learning: Post-incident, agents update their knowledge bases with new insights to improve future responses

Real-World Example: Microsoft’s Autonomous Incident Management

Microsoft has implemented Agentic AI to enhance its cloud incident management processes. By leveraging AI agents, Microsoft can autonomously detect service issues, analyze root causes, and implement fixes without human intervention. This approach has led to significant reductions in mean time to resolution (MTTR) and improved overall service reliability. 

Additional read: Implementing a Geographically Distributed Agentic AI in Banking

Sales and Marketing Automation

Sales and marketing operations in modern enterprises are increasingly complex, requiring real-time personalization, data-driven outreach, and efficient coordination between platforms. Agentic AI can identify high-intent leads, personalize engagement strategies, and optimize marketing performance through continuous learning and experimentation. The measurable benefits include higher lead conversion rates, improved customer experience, and significantly reduced operational overhead for sales and marketing teams.

Architecture & Workflow: Agentic AI in Sales and Marketing Automation

A typical Agentic AI architecture for sales and marketing automation includes the following components:

1. Customer Data Sources: Behavioral data from websites, product usage, CRM logs Integrated via MCP Servers (e.g., Salesforce MCP, HubSpot MCP)

2. AI Agents: They Perform lead scoring, segmentation, message personalization, and campaign orchestration and Interact with MCP servers of CRM and marketing tools to fetch, push, and update records.

3. Campaign Platforms: Tools like Mailchimp, HubSpot, and Outreach are invoked through agents to launch email or multichannel campaigns.

4. Lead Intelligence Tools: ZoomInfo, Clearbit, and LinkedIn Sales Navigator MCP servers provide enrichment data that agents use to prioritize leads.

5. A/B Testing & Feedback Analysis: Agents experiment with different subject lines, CTAs, timings and can Use data analytics platforms like Amplitude or internal dashboards to close the feedback loop.

6. Communication Tools: Integration with Slack/Teams MCPs enables agents to notify human sales reps with summaries or meeting suggestions.

7. Voice & Dialer Systems: Tools like Aircall, RingCentral used by agents for automated calling sequences and voicemail drops.

Workflow of the above Agentic AI system 

• Lead Identification: AI agents continuously monitor CRM and behavioral data to detect buying signals and intent.
• Personalization: Agents generate campaign content tailored to each lead's behavior, role, and company profile.
• Campaign Execution: Using MCP integrations, agents autonomously launch email, SMS, LinkedIn messages, or automated calls.
• Optimization: Agents analyze performance metrics in real-time and dynamically adjust messaging, timing, or segments.
• Sales Handoff: Once a lead is sales-qualified, agents create opportunities in CRM and coordinate meetings through calendar tools.

‍Real-World Example: Salesforce's Agentforce AI

Salesforce has introduced tools like Agentforce AI and Sales Coach, representing real-world applications of Agentic AI. These agents assist sales reps by automatically summarizing customer histories, suggesting talking points, and even recommending negotiation strategies.

Integrated tightly with Salesforce CRM and Slack MCPs, these agents enable reps to personalize outreach instantly and reduce preparation time for meetings. The result: faster sales cycles, higher engagement, and reduced workload. According to recent reports, these tools are already improving lead follow-up efficiency by over 40%, demonstrating the immediate ROI of Agentic AI in enterprise sales and marketing.

‍Product Engineering and Innovation

Product engineering in the modern enterprise is increasingly complex, involving fast-paced iteration, continuous integration, distributed teams, and data-driven experimentation. Agentic AI unlocks a new layer of efficiency and innovation by enabling autonomous agents to collaborate across the engineering toolchain.

These agents can automate code generation, perform intelligent test orchestration, monitor deployment health, analyze product analytics, and generate insights that feed back into the development process. This results in faster release cycles, reduced errors, improved product-market fit, and enhanced collaboration between engineering and product teams.

Architecture & Workflow: Agentic AI in Product Engineering and Innovation

A robust Agentic AI architecture for product engineering includes the following interconnected components:

1. Source Code Repositories:GitHub, GitLab, Bitbucket MCP servers:Agents monitor pull requests, issues, and commit patterns

2. AI Agents:Perform code review suggestions, auto-generate unit/integration tests, and optimize CI/CD pipelines.:Can trigger workflows and interact with infrastructure-as-code platforms.

3. CI/CD Systems:Tools like Jenkins, GitHub Actions, CircleCI accessed via MCP servers.Agents ensure tests run, handle rollbacks, and manage deployment approvals.

4. Issue Trackers:Jira, Linear, and Azure Boards MCP integrations help agents triage bugs, assign tasks, and suggest sprint adjustments.

5. Test Automation Frameworks: Selenium, Cypress, Playwright for UI tests.Agents select relevant test suites based on code changes and historical failure patterns.

6. Monitoring and Observability: Datadog, New Relic, or Grafana MCPs feed data to agents for production health tracking.Agents trigger alerts or remediations for anomaly detection.

7. Product Analytics Platform: Amplitude, Mixpanel, PostHog MCPs provide feedback on user engagement.Agents suggest feature improvements or flag churn signals.

Workflow of the above Agentic AI system 

• Code Contribution Monitoring: Agents monitor repositories and suggest code refactors or test improvements based on style guides and issue patterns.
• Test Generation & Execution: Agents generate missing tests, prioritize regression test suites, and trigger only necessary pipelines.
• Deployment & Monitoring: Agents manage deployment rollouts and monitor KPIs to catch issues early.
• Feedback Loop: Based on product analytics, agents suggest UI tweaks or feature enhancements.
• Sprint Optimization: Agents update Jira tickets, propose backlog re-prioritization, or even auto-close resolved issues.

‍Real-World Example: Morgan Stanley’s DevGen.AI

In January 2025, Morgan Stanley introduced DevGen.AI, an in-house AI Agent built on OpenAI's GPT models, designed to tackle the challenge of modernizing outdated legacy code—a common issue in large enterprises. Since its launch, DevGen.AI has reviewed over 9 million lines of code, saving developers approximately 280,000 hours.

This significant time savings has allowed Morgan Stanley's 15,000 developers to focus more on strategic initiatives rather than manual code translation. By automating the generation of readable specifications from legacy code, DevGen.AI exemplifies how Agentic AI can streamline the modernization process, reduce the risk of errors, and accelerate the integration of legacy systems into modern architecture 

What Are The Implementation Challenges With Agentic AI? 

Implementing agentic AI offers powerful capabilities, but it also comes with several practical challenges. From autonomous decision-making to secure multi-agent coordination, organizations must address these complexities to deploy reliable and scalable AI systems.

• Autonomous Decision-Making: Designing agents that can plan, prioritize, and act independently while ensuring they make accurate and contextually appropriate decisions.
  
• Multi-Agent Coordination: Managing communication, task allocation, and collaboration between multiple agents without conflicts or duplication of effort.
  
• Task Lifecycle Management: Ensuring that long-running or complex tasks are tracked, updated, and completed reliably, with proper handling of intermediate outputs or artifacts.
  
• Tool and Resource Integration: Safely connecting agents to external tools, APIs, and data sources while maintaining secure access controls and minimizing errors.
  
• Security and Compliance: Protecting sensitive data, enforcing authentication and authorization, and adhering to regulatory standards in multi-agent or enterprise deployments.
  
• Error Handling and Observability: エージェントのアクションや通信における障害を検知、報告、復旧し、監視とデバッグのためにワークフローの可視性を維持すること。
  
• 適応性と予測可能性の両立： エージェントが動的な状況に適応できるようにしつつ、その行動が一貫性、信頼性を保ち、ビジネス目標と合致していることを保証すること。
  
• ワークフロー統合： 既存のシステムやプロセスにエージェントAIをシームレスに組み込み、運用を妨げたり、過度な手動監視を必要としないようにすること。

AIエージェントの安全な導入と規模拡大のための必須制御策

AIエージェントを安全に導入し、効果的に規模を拡大するためには、組織はアクセス、監視、ガバナンスにわたる主要な制御策を実装する必要があります。

アクセス管理

許可されたエージェントとユーザーのみがツール、データ、ワークフローにアクセスできるよう、明確な役割、権限、認証を定義します。

監査証跡とロギング

エージェントのアクション、タスク実行、意思決定プロセスの詳細な記録を維持し、追跡可能性、説明責任、インシデント後の分析を可能にします。

フェイルセーフとオーバーライド

エージェントのアクションをリアルタイムで一時停止、停止、またはオーバーライドするメカニズムを実装し、意図しない結果や有害な行動を防ぎます。

行動監視

エージェントの活動と相互作用を継続的に監視し、異常、パフォーマンスの問題、または予期された行動からの逸脱を検知します。

バージョン管理とロールバック

エージェントのロジック、ワークフロー、ツール統合への変更を追跡し、必要に応じて以前の安定したバージョンへの安全なロールバックを可能にします。

倫理およびコンプライアンスポリシー

エージェントが倫理的境界内で行動し、規制を遵守し、プライバシー、公平性、責任あるAIの原則を尊重することを保証するポリシーを確立します。

結論：エージェントAIの未来を受け入れる

エージェンティックAIは、企業運営におけるパラダイムシフトを意味し、かつてないレベルの自動化、効率性、インテリジェンスを提供します。ゲートウェイ統合モデルを採用し、インテリジェントエージェントを主要機能に戦略的に展開することで、企業は新たな可能性を解き放ち、デジタル時代において競争優位性を維持できます。

他の革新的なテクノロジーと同様に、成功は慎重な導入、継続的な学習、そして適応性にかかっています。今日エージェンティックAIを導入する企業は、明日のインテリジェントエコノミーの先駆者となるでしょう。

TrueFoundryのプラットフォームを利用すれば、エージェンティックAIシステムをデプロイし、スケールさせることができ、チームは実験段階から本番環境への移行をより迅速に行えます。

デモを予約する エージェンティックAIを大規模に運用する方法をご覧ください。

よくある質問

企業におけるエージェンティックAIとは何ですか？

企業におけるエージェンティックAIとは、単にテキストを生成するだけでなく、推論、計画、そして特定の目標を達成するために多段階のタスクを実行できる自律型AIシステムの使用を指します。基本的なチャットボットとは異なり、これらのエージェントは社内ツール、データベース、APIと連携し、複雑な問題を自律的に解決します。TrueFoundryは、これらのシステムに不可欠なインフラストラクチャを提供し、企業データのプライバシーを損なうことなくエージェントを管理・拡張できる安全な環境を提供します。

エージェンティックAIの一般的な企業でのユースケースは何ですか？

企業におけるエージェンティックAIの一般的なアプリケーションには、コードレビューやプルリクエスト管理などのソフトウェア開発サイクルの自動化、およびリアルタイムデータにアクセスしてチケットを解決できるエージェントによる顧客業務の効率化が含まれます。その他のユースケースとしては、金融取引の検証や自動データレポート作成などがあります。TrueFoundryは、インテリジェントモデルと機密性の高い社内データソース間のギャップをエージェントが安全に埋めることを可能にする標準化されたコネクタ（MCP）を提供することで、これらのワークフローを実現します。

企業環境でエージェンティックAIをサポートするツールは何ですか？

企業環境でエージェンティックAIを構築するには、オーケストレーションフレームワーク、メモリ用のベクトルデータベース、およびモデルコンテキストプロトコル（MCP）のような標準化されたプロトコルを含む専門的なスタックが必要です。中心となるコンポーネントは、モデルのフェイルオーバー、セキュリティポリシー、レート制限を管理する高性能AIゲートウェイです。TrueFoundryは、このスタックの主要なプラットフォームとして機能し、お客様自身のクラウドアカウント内で動作する統合コントロールプレーンを提供することで、すべてのエージェンティックなツール呼び出しが安全かつ監査可能であることを保証します。

エージェンティックAIはビジネスの生産性を向上させることができますか？

はい、企業ワークフローにおけるエージェンティックAIは、これまで人間の介入が必要だった時間のかかる反復的な認知タスクを自動化することで、生産性を劇的に向上させることができます。これらのエージェントは、ワークフローを並行して実行し、最小限の監視で複雑なツールチェーンを処理できます。TrueFoundryは、デプロイと監視プロセスを簡素化することで、この生産性をさらに向上させます。これにより、エンジニアリングチームは数ヶ月ではなく数日で本番環境対応のエージェントを出荷できるようになり、インフラコストを最大70%最適化できます。

TrueFoundryはエージェンティックAIワークフローのセキュリティをどのように確保していますか？

TrueFoundryは、すべてのエージェンティックAIワークロードをお客様自身のクラウドアカウント内で実行することで、エンタープライズグレードのセキュリティを提供し、データがお客様の環境から出ることはありません。このプラットフォームは、厳格なアクセス制御、監査ログ、およびセキュアなAPI統合を強制し、エージェントが社内ツールや機密データと安全に連携できるようにします。これにより、企業はコンプライアンスやプライバシー基準を損なうことなく、自律型AIを活用することが可能になります。

TrueFoundryのエージェンティックAIソリューションはどの程度スケーラブルですか？

エージェンティックAIはリソースを大量に消費する可能性がありますが、TrueFoundryのプラットフォームは水平および垂直スケーリングに対応するように構築されています。モデルのオーケストレーション、リソース割り当て、フェイルオーバー処理を自動的に管理し、多様なチームや部門で複数のエージェントを同時に実行できるようにします。これにより、エンタープライズグレードのエージェンティックAIソリューションは、需要の増加に応じて、高性能、高信頼性、費用対効果を維持できます。