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市場調查報告書
商品編碼
2061992

基於代理的人工智慧開發平台：市場佔有率分析、行業趨勢和統計數據、成長預測（2026-2031）

Agentic AI Development Platform - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026 - 2031)
出版日期: | 出版商: Mordor Intelligence | 英文 170 Pages | 商品交期: 2-3個工作天內

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簡介目錄

根據 Mordor Intelligence 預測，基於代理商的 AI 開發平台市場規模預計將從 2025 年的 107.5 億美元成長到 2026 年的 146.2 億美元，然後從 2026 年到 2031 年以 35.34% 的複合年成長率，到 2031 年達到 6031 年達到 603.8 億美元。

智慧體人工智慧開發平台-市場-IMG1

本報告按元件（平台軟體、編配中間件、評估和安全工具等）、部署模式（公共雲端、私有雲端、本地部署等）、最終用戶產業（醫療保健和生命科學等）、組織規模（大型企業、中小企業）和地區進行細分。市場預測以價值（美元）表示。

全球基於代理的人工智慧開發平台市場趨勢與洞察

從副駕駛到自主工作流程編配的過渡

自主編配是傳統輔助駕駛工具與目前以代理為基礎的AI開發平台市場之間最顯著的差異。輔助駕駛工具主要提供提示和建議，而自主代理則能夠規劃任務、啟動工具、驗證結果並協調後續操作，從而顯著減少人工干預。這種運作模式需要專用的運行時環境、更強大的狀態管理以及對企業系統內操作的更嚴格控制。 ServiceNow在2026年發布的報告顯示，其「自主工作團隊」處理了超過90%的員工IT請求，每月解決超過1億個客戶案例。這體現了企業代理部署目前所期望的營運規模。一旦圍繞選定的運行時環境建立了工作流程，切換到其他運行時環境就會變得困難，需要進行迭代整合測試、重新訓練和工作流程檢驗。

LLM的推理能力、工具利用和多智慧體框架取得了快速發展。

在模型和框架效能提升的推動下，基於代理的AI開發平台市場也在不斷發展，從而能夠在生產環境中可靠地執行條件工作流程。對AdaptOrch框架的研究表明，透過拓撲感知調度，其性能相比靜態編配基準提升了12%至23%，其中在需要順序使用工具和分支邏輯的任務中，性能提升最為顯著。另一項針對DOVA框架的2026年研究發現，自適應思考協議透過省略不必要的增強推理，將日常任務的推理成本降低了40%至60%。隨著模型輸出趨於一致，買家將更多時間用於比較拓撲設計、記憶體管理和任務調優，而不是比較單一的底層模型供應商。這使得專業的運行時供應商能夠在基於代理的AI開發平台市場中保持其地位，即使超大規模資料超大規模資料中心業者提供了更廣泛的模型存取權。

自主代理的管治、可審計性和安全性漏洞

由於自主代理運行於多個系統之間，而傳統控制方法無法對其進行追蹤，因此安全問題仍然是基於代理的人工智慧開發平台市場面臨的最明顯障礙之一。根據麻省理工學院 (MIT) 於 2025 年發布的 AI 代理指數，在接受調查的 200 個運作代理中，僅有一個使用加密簽章進行操作檢驗，這凸顯了當前可審計性的持續局限性。 OWASP 在 2026 年發布的「MCP 安全十大風險」報告中正式指出了諸如透過工具輸出進行快速注入以及記憶體存取權限過寬等風險。這些問題促使企業安全團隊在批准在生產環境中使用之前，要求提供血緣追蹤、回滾控制和基於策略的存取控制。無法證明具備這些控制措施的供應商通常面臨更長的銷售週期和更高的概念驗證(PoC) 成本，尤其是在面對受監管客戶時。

細分市場分析

到2025年，平台軟體將佔據基於代理的AI開發平台市場佔有率的76.39%，這意味著支出仍然集中在編配引擎、代理運行時和LLM閘道器層。由於企業將這些層視為核心基礎設施，即使在實施生態系統尚未完全擴展之前，採購行為也優先考慮長期平台合約。這種模式是市場週期早期階段的典型特徵，此時企業更重視基礎控制和工作流程可靠性，而非其他輔助工具。這也解釋了為什麼與許多標準軟體採購相比，基於代理的AI開發平台市場的平台決策往往具有更長的評估期和更高的轉換門檻。

預計到2031年，專業服務將以36.14%的複合年成長率成長。這是因為部署仍然需要連接器工作、記憶體模式設計、管治策略配置以及系統間身份驗證規劃。自適應編配的研究表明，與靜態系統相比，拓撲感知代理管理可以將效能提高12%至23%，這項發現正在推動對架構設計和調優支援的需求。隨著MCP和代理間協定的採用率不斷提高，跨協定適配器和互通性層的價值日益凸顯，編配中間件的重要性也隨之提升。此外，隨著買家對生產環境中代理商的檢驗、監控和策略測試提出了更高的要求，評估和安全工具正從可選附加元件轉變為採購必需品。

到 2025 年，公共雲端將佔據基於代理的 AI 開發平台市場 52.61% 的佔有率，成為眾多企業部署的預設起點。超大規模超大規模資料中心業者提供的託管執行時間環境，將模型存取、編配工具和基礎設施控制整合到單一環境中，為使用者加速向生產環境過渡提供了途徑。微軟宣布，Azure AI Foundry 在 2025 年的單一季度處理了超過 100 兆個令牌，凸顯了企業早期對公共雲端基礎設施的需求。公共雲端的優勢也體現在許多組織先進行低風險的試點運營，然後再決定在哪些情況下需要更嚴格的資料居住要求和延遲控制。

隨著越來越多的買家將代理部署在更靠近資料來源、作業系統和受監管工作負載的位置，混合部署和邊緣部署預計到 2031 年將以 36.09% 的複合年成長率成長。這一趨勢在工業、公共部門環境以及由於往返延遲和資料傳輸法規而無法進行集中式雲端處理的行業中最為顯著。 AWS 於 2026 年透過 Bedrock AgentCore（一個託管代理框架平台）和對託管多代理管道的早期支持，進一步拓展了這條路徑。 UiPath 也於 2026 年 5 月發布了對公共部門環境的本地部署支持，這表明主權部署和空氣間隙部署正在成為基於代理的 AI 開發平台行業的一個獨特領域。在金融服務和醫療保健行業，私有雲端仍然至關重要，因為接近性記錄系統和完整的審計追蹤仍然是這些領域部署設計的核心要素。

區域分析

北美在收入方面保持主導地位，預計到2025年將佔據基於代理的AI開發平台市場38.73%的佔有率。該地區受益於超大規模資料中心業者基礎設施、大規模的企業軟體買家群體以及鼓勵自願管治的法規環境。微軟報告稱，到2025年，Azure AI Foundry的用戶將超過7萬，凸顯了其企業基本客群的規模。 OpenAI於2026年3月推出了企業平台“Frontier”，該平台已被惠普、Intuit、Oracle和Uber等公司採用。 ServiceNow預測，到2026年，AWS Marketplace的交易額將達到10億美元，這顯示雲端市場正成為重要的通路。

亞太地區預計到2031年將以36.34%的複合年成長率成長，主要驅動力包括中國企業的應用、印度提高生產力的應用以及日本切實可行的應用。 NTT Docomo Business計劃在2026年向企業客戶提供200種不同的代理，這體現了日本的系統性應用。韓國在半導體製造和金融服務領域取得了進展，並採用私有雲端模式來解決資料主權問題。該地區正從實驗階段過渡到以生產工作流程和合規性為中心的模式。

更嚴格的歐洲法規正在重塑基於代理的人工智慧開發平台市場。歐盟針對高風險系統的人工智慧法案將於2026年8月生效，同時生效的還有《數位營運韌性法案》下加強的審計機制。德國、英國和法國憑藉大規模的企業基礎設施和合規性的投入，在人工智慧的採用方面處於領先地位。根據歐盟委員會的數據，企業在18個月內為遵守歐盟人工智慧法案而投入的預算在210萬歐元至450萬歐元（237萬美元至509萬美元）之間。南美洲正率先採用人工智慧技術，巴西和阿根廷的發展勢頭強勁。中東和非洲地區也正在發展，這得益於阿拉伯聯合大公國、沙烏地阿拉伯、南非和埃及等國家主導的人工智慧投資，以及電信業的應用和銀行業的應用案例，但預計到2031年，該地區的支出仍將低於其他地區。

其他好處：

  • Excel格式的市場預測（ME）表
  • 3個月的分析師支持

目錄

第1章：引言

  • 市場分析與定義的前提條件
  • 分析範圍

第2章 分析方法

第3章執行摘要

第4章 市場狀況

  • 市場概覽
  • 市場促進因素
    • 從副駕駛到自主工作流程編配的過渡
    • LLM的推理能力、工具利用和多智慧體框架的快速發展。
    • 透過低程式碼建構器和託管代理程式運行時降低採用門檻。
    • 受監管產業對管治人工智慧的需求日益成長
    • 圍繞 MCP 的標準化和新的代理間協議
    • ERP和工作流程系統現代化：為基於代理的新控制平面創造機會。
  • 市場限制因素
    • 自主代理的管治、可審計性和安全性挑戰
    • 整合舊有系統的複雜性以及超出有限工作流程後投資報酬率不明確。
    • 代幣密集型推理的經濟學原理以及代理擴散對金融運營帶來的壓力
    • 多智慧體系統的評估挑戰以及智慧體記憶體可追溯性的低問題。
  • 宏觀經濟因素對市場的影響
  • 產業價值鏈分析
  • 監理情勢
  • 技術展望
  • 波特五力分析

第5章：預測市場規模與成長率

  • 按組件
    • 平台軟體
    • 編配中介軟體
    • 評估和安全工具
    • 專業服務
  • 按部署模式
    • 公共雲端
    • 私有雲端
    • 現場
    • 混合邊緣
  • 按最終用戶行業分類
    • 銀行、金融服務和保險業 (BFSI)
    • 醫學與生命科​​學
    • 零售與電子商務
    • 製造業
    • 媒體與娛樂
    • 政府/公共部門
    • 其他終端用戶產業
  • 按組織規模
    • 大公司
    • 中小企業
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
      • 墨西哥
    • 南美洲
      • 巴西
      • 阿根廷
      • 其他南美國家
    • 歐洲
      • 英國
      • 德國
      • 法國
      • 義大利
      • 西班牙
      • 其他歐洲國家
    • 亞太地區
      • 中國
      • 日本
      • 印度
      • 韓國
      • 其他亞太國家
    • 中東和非洲
      • 中東
        • 阿拉伯聯合大公國
        • 沙烏地阿拉伯
        • 其他中東國家
      • 非洲
        • 南非
        • 埃及
        • 其他非洲國家

第6章 競爭情勢

  • 市場集中度
  • 策略趨勢
  • 市佔率分析
  • 公司簡介
    • Microsoft Corporation
    • Amazon Web Services, Inc.
    • Google LLC
    • OpenAI, LLC
    • Anthropic PBC
    • Salesforce, Inc.
    • ServiceNow, Inc.
    • International Business Machines Corporation
    • Oracle Corporation
    • SAP SE
    • UiPath, Inc.
    • Databricks, Inc.
    • Snowflake Inc.
    • C3.ai, Inc.
    • Dataiku, Inc.
    • LangChain, Inc.
    • LlamaIndex, Inc.
    • Kore.ai, Inc.
    • Aisera, Inc.
    • Stack AI, Inc.

第7章 市場機會與未來展望

簡介目錄
Product Code: 94581

According to Mordor Intelligence, the agentic AI development platform market size is expected to grow from USD 10.75 billion in 2025 to USD 14.62 billion in 2026 and is forecast to reach USD 66.38 billion by 2031 at 35.34% CAGR over 2026-2031.

Agentic AI Development Platform - Market - IMG1

This report is Segmented by Component (Platform Software, Orchestration Middleware, Evaluation and Safety Tools, and More), Deployment (Public Cloud, Private Cloud, On-Premises, and More), End-User Industry (Healthcare and Life Sciences, and More), Organization Size (Large Enterprises, and Small and Med-Size Enterprises), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Agentic AI Development Platform Market Trends and Insights

Shift From Copilots to Autonomous Workflow Orchestration

Autonomous orchestration marks the clearest break between earlier copilot tools and the current agentic AI development platform market. Copilots mainly provide prompts and recommendations, while autonomous agents plan tasks, invoke tools, check results, and adjust their next actions with much less human input. That operating model requires a dedicated runtime, stronger state management, and tighter control over actions across enterprise systems. ServiceNow reported in 2026 that its Autonomous Workforce handled more than 90% of employee IT requests and resolved more than 100 million customer cases each month, which shows the operational scale now expected from enterprise agent deployments. Once workflows are built around a chosen runtime, replacement becomes difficult because integration testing, retraining, and workflow validation must be repeated.

Rapid Improvement in LLM Reasoning, Tool Use, and Multi-Agent Frameworks

The agentic AI development platform market has also advanced, as model and framework performance now enable more reliable completion of conditional workflows in production. Research on the AdaptOrch framework showed that topology-aware scheduling improved performance by 12-23% over static orchestration baselines, with the strongest gains in tasks that require sequential tool use and branching logic. A separate 2026 study on the DOVA framework found that adaptive thinking protocols reduced inference costs by 40-60% on routine tasks by skipping unnecessary extended reasoning. As model outputs converge, buyers are spending more time comparing topology design, memory management, and task coordination than comparing a single foundation model vendor. This is helping specialized runtime vendors in the agentic AI development platform market defend their position even when hyperscalers offer broader model access.

Governance, Auditability, and Security Gaps in Autonomous Agents

Security concerns remain one of the clearest brakes on the agentic AI development platform market because autonomous agents act across multiple systems where traditional controls were not designed to follow them. The MIT AI Agent Index reported in 2025 that only 1 of 200 reviewed production agents used cryptographic signing for action verification, underscoring the continued limitations of current auditability. OWASP published its MCP Security Top 10 in 2026 and formalized risks such as prompt injection via tool outputs and overly broad memory-retrieval permissions. These issues prompt enterprise security teams to request lineage tracking, rollback controls, and policy-based access enforcement before approving live use. Vendors that cannot show these controls often face longer sales cycles and higher proof-of-concept costs in regulated accounts.

Other drivers and restraints analyzed in the detailed report include:

  1. Falling Deployment Friction Through Low-Code Builders And Managed Agent Runtimes
  2. Rising Demand For Governed AI In Regulated Verticals
  3. Legacy-System Integration Complexity And Unclear ROI Beyond Narrow Workflows

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Platform software accounted for 76.39% of the agentic AI development platform market share in 2025, which shows that spending still centers on orchestration engines, agent runtimes, and LLM gateway layers. Enterprises treated this layer as core infrastructure, so buying behavior favored long-term platform commitments before implementation ecosystems had fully scaled. This pattern fits an early-cycle market where foundational control and workflow reliability matter more than adjacent tools. It also explains why platform decisions in the agentic AI development platform market tend to carry longer evaluation windows and higher switching barriers than many standard software purchases.

Professional services are projected to grow at a 36.14% CAGR through 2031, as deployment still requires connector work, memory schema design, governance policy setup, and cross-system authentication planning. Research on adaptive orchestration showed that topology-aware agent management can deliver 12-23% performance gains over static systems, and that finding is feeding demand for architecture design and tuning support. Orchestration middleware is gaining relevance as MCP and agent-to-agent protocol adoption increases the value of cross-protocol adapters and interoperability layers. Evaluation and safety tools are also moving from optional add-ons toward procurement requirements as buyers seek stronger validation, monitoring, and policy testing for production agents.

Public cloud captured 52.61% of the agentic AI development platform market size in 2025, making it the default starting point for many enterprise deployments. Managed runtimes from hyperscalers gave buyers a faster path to production because model access, orchestration tools, and infrastructure controls were already bundled in a single environment. Microsoft stated that Azure AI Foundry processed more than 100 trillion tokens in a single quarter in 2025, highlighting the extent to which early enterprise demand remained concentrated on public cloud infrastructure. The public cloud lead also reflects the fact that many organizations began with lower-risk pilots before deciding where stricter residency or latency controls were needed.

Hybrid and edge deployments are projected to grow at 36.09% CAGR through 2031 as more buyers run agents closer to data sources, operating systems, and regulated workloads. That push is strongest in industrial settings, public-sector environments, and sectors where round-trip latency or data-transfer rules make centralized cloud processing less practical. AWS expanded this path in 2026 with Bedrock AgentCore, a managed agent-harness platform, and early support for managed multi-agent pipelines. UiPath also released on-premises support for public-sector environments in May 2026, which shows that sovereign and air-gapped deployments are becoming a distinct part of the agentic AI development platform industry. Private cloud continues to matter most in financial services and healthcare, where system-of-record proximity and full audit trails remain central to deployment design.

Geography Analysis

North America held 38.73% of the agentic AI development platform market share in 2025, maintaining its revenue leadership. The region benefits from hyperscaler infrastructure, a large enterprise software buyer base, and a regulatory environment favoring voluntary governance. Microsoft reported over 70,000 Azure AI Foundry customer organizations in 2025, highlighting the scale of its enterprise base. OpenAI launched its Frontier enterprise platform in March 2026 with adopters like HP, Intuit, Oracle, and Uber. ServiceNow's USD 1 billion in AWS Marketplace transactions in 2026 indicates cloud marketplaces are becoming key distribution channels.

Asia-Pacific is projected to grow at 36.34% CAGR through 2031, driven by enterprise deployment in China, productivity-led adoption in India, and practical implementation in Japan. NTT Docomo Business planned to offer 200 agent types to enterprise customers in 2026, reflecting structured deployments in Japan. South Korea is advancing in semiconductor manufacturing and financial services, with private cloud models addressing data-sovereignty concerns. The region is transitioning from experimentation to production workflows and compliance-focused models.

Europe's tighter regulations are shaping the agentic AI development platform market. The EU AI Act enforcement for high-risk systems began in August 2026, alongside increased auditability under the Digital Operational Resilience Act. Germany, the UK, and France lead deployments due to large enterprise bases and compliance spending. European Commission data shows enterprise adaptation budgets of EUR 2.1-4.5 million (USD 2.37-5.09 million) over 18 months for EU AI Act readiness. South America shows early adoption, with Brazil and Argentina gaining traction. The Middle East and Africa are growing through sovereign AI investments, telecom deployments, and banking use cases, led by the UAE, Saudi Arabia, South Africa, and Egypt, though spending remains lower than in other regions through 2031.

  1. Microsoft Corporation
  2. Amazon Web Services, Inc.
  3. Google LLC
  4. OpenAI, L.L.C.
  5. Anthropic PBC
  6. Salesforce, Inc.
  7. ServiceNow, Inc.
  8. International Business Machines Corporation
  9. Oracle Corporation
  10. SAP SE
  11. UiPath, Inc.
  12. Databricks, Inc.
  13. Snowflake Inc.
  14. C3.ai, Inc.
  15. Dataiku, Inc.
  16. LangChain, Inc.
  17. LlamaIndex, Inc.
  18. Kore.ai, Inc.
  19. Aisera, Inc.
  20. Stack AI, Inc.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Shift From Copilots to Autonomous Workflow Orchestration
    • 4.2.2 Rapid Improvement in LLM Reasoning, Tool Use, and Multi-Agent Frameworks
    • 4.2.3 Falling Deployment Friction Through Low-Code Builders and Managed Agent Runtimes
    • 4.2.4 Rising Demand for Governed AI in Regulated Verticals
    • 4.2.5 Standardization Around MCP and Emerging Agent-to-Agent Protocols
    • 4.2.6 ERP and Workflow-System Modernization Creating a New Agent Control Plane Opportunity
  • 4.3 Market Restraints
    • 4.3.1 Governance, Auditability, and Security Gaps in Autonomous Agents
    • 4.3.2 Legacy-System Integration Complexity and Unclear ROI Beyond Narrow Workflows
    • 4.3.3 Token-Intensive Inference Economics and Agent-Sprawl FinOps Pressure
    • 4.3.4 Evaluation Gaps for Multi-Agent Systems and Weak Traceability of Agent Memory
  • 4.4 Impact of Macroeconomic Factors on the Market
  • 4.5 Industry Value Chain Analysis
  • 4.6 Regulatory Landscape
  • 4.7 Technological Outlook
  • 4.8 Porter's Five Forces Analysis
    • 4.8.1 Bargaining Power of Suppliers
    • 4.8.2 Bargaining Power of Buyers
    • 4.8.3 Threat of New Entrants
    • 4.8.4 Threat of Substitutes
    • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

  • 5.1 By Component
    • 5.1.1 Platform Software
    • 5.1.2 Orchestration Middleware
    • 5.1.3 Evaluation and Safety Tools
    • 5.1.4 Professional Services
  • 5.2 By Deployment Model
    • 5.2.1 Public Cloud
    • 5.2.2 Private Cloud
    • 5.2.3 On-premises
    • 5.2.4 Hybrid and Edge
  • 5.3 By End-user Industry
    • 5.3.1 BFSI
    • 5.3.2 Healthcare and Life Sciences
    • 5.3.3 Retail and E-Commerce
    • 5.3.4 Manufacturing
    • 5.3.5 Media and Entertainment
    • 5.3.6 Government and Public Sector
    • 5.3.7 Other End-user Industries
  • 5.4 By Organization Size
    • 5.4.1 Large Enterprises
    • 5.4.2 Small and Mid-size Enterprises (SMEs)
  • 5.5 By Geography
    • 5.5.1 North America
      • 5.5.1.1 United States
      • 5.5.1.2 Canada
      • 5.5.1.3 Mexico
    • 5.5.2 South America
      • 5.5.2.1 Brazil
      • 5.5.2.2 Argentina
      • 5.5.2.3 Rest of South America
    • 5.5.3 Europe
      • 5.5.3.1 United Kingdom
      • 5.5.3.2 Germany
      • 5.5.3.3 France
      • 5.5.3.4 Italy
      • 5.5.3.5 Spain
      • 5.5.3.6 Rest of Europe
    • 5.5.4 Asia-Pacific
      • 5.5.4.1 China
      • 5.5.4.2 Japan
      • 5.5.4.3 India
      • 5.5.4.4 South Korea
      • 5.5.4.5 Rest of Asia-Pacific
    • 5.5.5 Middle East and Africa
      • 5.5.5.1 Middle East
        • 5.5.5.1.1 United Arab Emirates
        • 5.5.5.1.2 Saudi Arabia
        • 5.5.5.1.3 Rest of Middle East
      • 5.5.5.2 Africa
        • 5.5.5.2.1 South Africa
        • 5.5.5.2.2 Egypt
        • 5.5.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
    • 6.4.1 Microsoft Corporation
    • 6.4.2 Amazon Web Services, Inc.
    • 6.4.3 Google LLC
    • 6.4.4 OpenAI, L.L.C.
    • 6.4.5 Anthropic PBC
    • 6.4.6 Salesforce, Inc.
    • 6.4.7 ServiceNow, Inc.
    • 6.4.8 International Business Machines Corporation
    • 6.4.9 Oracle Corporation
    • 6.4.10 SAP SE
    • 6.4.11 UiPath, Inc.
    • 6.4.12 Databricks, Inc.
    • 6.4.13 Snowflake Inc.
    • 6.4.14 C3.ai, Inc.
    • 6.4.15 Dataiku, Inc.
    • 6.4.16 LangChain, Inc.
    • 6.4.17 LlamaIndex, Inc.
    • 6.4.18 Kore.ai, Inc.
    • 6.4.19 Aisera, Inc.
    • 6.4.20 Stack AI, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-Space and Unmet-Need Assessment