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市場調查報告書
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2080144

全球AI代理可觀測性市場：按提供、功能、監控模型類型、部署方式、組織規模和最終用戶行業分類—市場規模、行業動態、機會分析和預測（2026-2035年）

Global AI Agent Observability Market By Offering, Capability, Model Type Monitored, Deployment, Organization Size, End-Use Industry - Market Size, Industry Dynamics, Opportunity Analysis and Forecast For 2026-2035
出版日期: | 出版商: Astute Analytica | 英文 280 Pages | 商品交期: 最快1-2個工作天內

價格
簡介目錄

人工智慧代理可觀測性市場崛起，成為人工智慧和企業軟體生態系統中一個快速成長的領域，反映出各行業對自主和半自動代理框架的加速採用。儘管該市場仍處於商業化初期，但在企業試點計畫和早期全面部署的推動下，已展現出強勁的發展動力。

預計未來該市場將顯著擴張，到2035年將達到約71億美元。這意味著在2026年至2035年的預測期內，其年複合成長率將達到約33.3%，鞏固了人工智慧代理可觀測性作為企業人工智慧基礎設施中成長最快的領域之一的地位。這一強勁的成長動力反映了人工智慧應用正迅速從實驗階段轉向關鍵任務部署，並在包括客戶支援、軟體工程、財務營運和資料驅動決策系統整體業務職能領域中部署自主代理。

顯著的市場趨勢

目前，人工智慧代理可觀測性市場由少數領先平台組成，這些平台致力於推動自主系統在監控、評估和可靠性保障方面的創新。其中，LangSmith 脫穎而出，它與 LangChain 和 LangGraph 生態系統深度整合，提供了一種框架原生方法，尤其適用於現代基於代理的工作流程。

Langfuse 已成為人工智慧可觀測性領域的領先開放原始碼替代方案，並被眾多自託管和注重隱私的工程團隊廣泛採用。 Arize Phoenix 將傳統機器學習可觀測性的堅實基礎引進人工智慧代理監控領域，並將其專業知識擴展到機器學習模型（LLM）和基於代理的系統。

Braintrust 作為一個著重生產就緒和評估主導開發工作流程的企業級可觀測性平台，正日益受到關注。 Datadog 作為傳統可觀測性領域的長期領導者，正利用其廣泛的企業基礎設施和監控能力，將其平台擴展到人工智慧代理監控領域。

主要成長要素

全球日益嚴格的管治和審計要求正成為人工智慧代理可觀測性市場成長的主要驅動力。隨著人工智慧系統在金融、醫療保健、法律服務和關鍵基礎設施等高影響力領域部署，多司法管轄區的監管機構實施更嚴格的要求，以確保課責、透明度和運作安全。這種不斷演變的法律規範迫使各組織實施強大的監督系統，全面了解人工智慧主導的決策流程。

新機會的趨勢

由自主人工智慧驅動的「生產環境中的人工智慧可靠性危機」正成為影響人工智慧代理可觀測性市場成長的關鍵趨勢。隨著企業迅速從實驗部署轉向全面生產環境，它們面臨著與自主人工智慧系統不可預測性相關的根本性挑戰。與遵循確定性邏輯且執行路徑清晰的傳統軟體應用程式不同，人工智慧代理透過機率推理、多階段決策鏈以及與外部工具和資料來源的動態互動來運作。這引進了新的運作複雜性維度，使得在沒有進階監控和診斷能力的情況下難以保證可靠性，因為即使輸入相似，系統行為也可能存在顯著差異。

最佳化障礙

隱私、合規性和資料外洩方面的擔憂構成了一系列重大挑戰，可能會阻礙人工智慧代理可觀測性市場的成長。隨著可觀測平台與生產環境的整合日益深入，需要收集和分析越來越細粒度的系統級資料，才能診斷故障並確保代理的可靠性能。這些資料包括原始系統輸入、推理中間步驟、工具呼叫、API互動以及從外部資料庫檢索查詢結果，所有這些都在複雜的代理程式工作流程中產生。雖然這種程度的遙測資料對於有效的調試和效能最佳化非常重要，但也帶來了敏感資料外洩的重大風險。

目錄

第1章 執行摘要：全球人工智慧代理可觀測性市場

第2章 調查方法與研究框架

  • 研究目標
  • 產品概述
  • 市場區隔
  • 定性研究
    • 一手和二手資訊
  • 量化研究
    • 一手和二手資訊
  • 主要調查受訪者組成：依地區分類
  • 本研究的前提
  • 市場規模估算
  • 資料三角測量

第3章 全球人工智慧代理可觀測性市場概述

  • 產業價值鏈分析
  • 產業展望
    • 全球人工智慧代理可觀測性和LLMOps產業概覽
    • 在生產環境中部署代理可以實現追蹤、評估和成本監控。
    • 受監管工作負載的管治、審計追蹤和防護措施要求
  • PESTLE分析
  • 波特五力分析
  • 市場成長及前景
    • 2020-2035年市場收入估算與預測
    • 價格趨勢分析：依提供

第4章 全球人工智慧代理可觀測性市場分析

  • 競爭儀錶板
    • 市場集中度
    • 企業市場占有率分析，2025年
    • 競爭對手分析與基準測試

第5章 全球人工智慧代理可觀測性市場分析

  • 市場動態和趨勢
    • 成長促進因素
    • 抑制因子
    • 機會
    • 主要趨勢
  • 市場規模及預測，2020-2035年
    • 依提供
    • 依功能
    • 依監測模型類型
    • 依部署
    • 依組織規模
    • 依最終用途行業分類
    • 依地區

第6章 北美市場分析

第7章 歐洲市場分析

第8章 亞太市場分析

第9章 中東和非洲市場分析

第10章 南美市場分析

第11章 公司簡介

  • Arize AI
  • Langfuse
  • LangChain（LangSmith）
  • Datadog
  • New Relic
  • Dynatrace
  • Cisco（Splunk）
  • Weights & Biases
  • Galileo
  • WhyLabs
  • Fiddler AI
  • Coralogix
  • Honeycomb
  • Microsoft
  • Google
  • 其他主要公司

第12章 附錄

簡介目錄
Product Code: AA06261852

The AI agent observability market is emerging as a rapidly expanding segment within the broader artificial intelligence and enterprise software ecosystem, reflecting the accelerated adoption of autonomous and semi-autonomous agent frameworks across industries. In 2025, the market is estimated to be valued at approximately USD 0.4 billion, indicating that it is still in a relatively early stage of commercialization but is already experiencing strong momentum driven by enterprise experimentation and initial production deployments.

Looking ahead, the market is projected to experience substantial expansion, reaching around USD 7.1 billion by 2035. This represents a compound annual growth rate (CAGR) of approximately 33.3% over the forecast period from 2026 to 2035, positioning AI agent observability as one of the fastest-growing segments within enterprise AI infrastructure. This strong growth trajectory reflects the rapid transition from experimental AI usage to mission-critical deployment of autonomous agents across business functions such as customer support, software engineering, financial operations, and data-driven decision-making systems.

Noteworthy Market Developments

The AI agent observability market is currently shaped by a concentrated group of leading platforms that are driving innovation in monitoring, evaluation, and reliability for autonomous systems. Among them, LangSmith has emerged as a deeply integrated solution within the LangChain and LangGraph ecosystem, offering a framework-native approach that is particularly well suited for modern agentic workflows.

Langfuse has established itself as a leading open-source alternative in the AI observability space, gaining strong adoption among self-hosted and privacy-conscious engineering teams. Arize Phoenix brings a strong foundation in traditional machine learning observability to the AI agent monitoring landscape, extending its expertise into LLM and agent-based systems.

Braintrust has gained traction as an enterprise-oriented observability platform with a strong focus on production readiness and evaluation-driven development workflows. Datadog, a long-established leader in traditional observability, has extended its platform into the AI agent monitoring space by leveraging its extensive enterprise infrastructure and monitoring capabilities.

Core Growth Drivers

Stringent global governance and auditing mandates are becoming a major force driving the growth of the AI agent observability market. As artificial intelligence systems are increasingly deployed in high-impact domains such as finance, healthcare, legal services, and critical infrastructure, regulators across multiple jurisdictions are introducing stricter requirements to ensure accountability, transparency, and operational safety. These evolving regulatory frameworks are pushing organizations to implement robust monitoring systems capable of providing complete visibility into AI-driven decision-making processes.

Emerging Opportunity Trends

The autonomous "Production AI reliability crisis" is emerging as a major opportunity trend shaping the growth of the AI agent observability market. As enterprises rapidly move from experimental deployments to full-scale production environments, they are encountering fundamental challenges related to the unpredictable nature of autonomous AI systems. Unlike traditional software applications that follow deterministic logic with clearly defined execution paths, AI agents operate through probabilistic reasoning, multi-step decision chains, and dynamic interactions with external tools and data sources. This introduces a new layer of operational complexity, where system behavior can vary significantly even under similar inputs, making reliability difficult to guarantee without advanced monitoring and diagnostic capabilities.

Barriers to Optimization

Privacy, compliance, and data leakage concerns represent a significant set of challenges that may hamper the growth of the AI agent observability market. As observability platforms become more deeply integrated into production environments, they are required to collect and analyze increasingly granular system-level data in order to diagnose failures and ensure reliable agent performance. This includes capturing raw system inputs, intermediate reasoning steps, tool calls, API interactions, and external database query results generated throughout complex agent workflows. While this level of telemetry is essential for effective debugging and performance optimization, it also introduces substantial risks related to sensitive data exposure.

Detailed Market Segmentation

By capability, agent tracing applications have captured clear and absolute dominance within the AI agent observability market in 2026. This leadership is primarily driven by the fundamental shift in how artificial intelligence systems are being designed and deployed across enterprise environments. Organizations are increasingly moving away from isolated prompt-response interactions and toward complex, multi-agent workflows where multiple autonomous systems interact, collaborate, and execute tasks across interconnected environments. In this new paradigm, understanding how and why an AI agent reaches a particular decision has become a critical operational requirement, making tracing capabilities indispensable.

By model type monitored, proprietary models firmly established their dominant position within the AI agent observability market throughout 2025 and have continued to maintain strong leadership into 2026. This dominance is largely driven by widespread enterprise reliance on closed, commercially developed AI systems that are designed and maintained by leading technology providers. These proprietary models are typically integrated into mission-critical workflows where reliability, performance consistency, and enterprise-grade support are essential requirements. As organizations scale their use of AI agents across sensitive and high-value operations, the need for tightly controlled and well-governed model environments has significantly increased.

By organization size, large enterprises have decisively dominated the AI agent observability market throughout 2025 and continue to maintain clear supremacy in 2026. These organizations possess the financial strength, technical resources, and organizational scale required to deploy sophisticated autonomous and semi-autonomous AI agents across multiple business functions simultaneously. As AI adoption shifts from isolated use cases to enterprise-wide integration, large corporations are leading the transition by embedding agentic systems into core workflows such as customer service, software development, finance operations, supply chain management, and data analytics.

By deployment, cloud computing models continue to maintain a dominant position in the global AI agent observability market, reflecting a broader enterprise shift toward scalable, distributed digital infrastructure. As organizations increasingly deploy intelligent agents across critical business workflows, the volume, velocity, and complexity of telemetry data generated by these systems have grown exponentially. This includes logs, traces, performance metrics, behavioral signals, and decision-making pathways produced by autonomous or semi-autonomous agents operating in real time. Traditional on-premise infrastructures often struggle to efficiently process and store this continuous data stream, creating bottlenecks in monitoring, analysis, and operational visibility.

Segment Breakdown

By Offering

  • Solutions / Software
  • Tracing & Evaluation
  • Monitoring & Alerting
  • Guardrails & Quality
  • Services
  • Integration
  • Managed

By Capability

  • Agent Tracing
  • Prompt & Output Evaluation
  • Cost & Token Monitoring
  • Latency & Performance
  • Drift & Hallucination Detection
  • Security & Guardrails

By Model Type Monitored

  • Proprietary / Closed
  • Open-Source
  • Fine-Tuned / Custom

By Deployment

  • Cloud
  • On-Premises
  • Hybrid

By Organization Size

  • Large Enterprises
  • SMEs

By End-Use Industry

  • BFSI
  • IT & Telecom
  • Healthcare
  • Retail
  • Manufacturing
  • Government
  • Others

By Region

  • North America
  • The U.S.
  • Canada
  • Mexico
  • Europe
  • Western Europe
  • The UK
  • Germany
  • France
  • Italy
  • Spain
  • Rest of Western Europe
  • Eastern Europe
  • Poland
  • Russia
  • Rest of Eastern Europe
  • Asia Pacific
  • China
  • India
  • Japan
  • Australia & New Zealand
  • South Korea
  • ASEAN
  • Rest of Asia Pacific
  • Middle East & Africa (MEA)
  • Saudi Arabia
  • South Africa
  • UAE
  • Rest of MEA
  • South America
  • Argentina
  • Brazil
  • Rest of South America

Geography Breakdown

  • North America currently holds the largest share of the AI agent observability market, supported by early and widespread adoption of agentic AI architectures across major technology hubs such as Silicon Valley, as well as large-scale Fortune 500 enterprises. The region benefits from a mature digital infrastructure ecosystem, strong venture capital presence, and an advanced enterprise software culture that has been quick to experiment with and deploy generative AI and autonomous agent systems.
  • The United States, in particular, serves as the central hub for both foundational model development and AI observability innovation. Many of the leading developers of large language models and agent frameworks are headquartered in the country, creating a tightly integrated ecosystem where model creation, deployment tooling, and observability platforms evolve in parallel. This concentration of expertise and capital has fostered rapid iteration cycles and deep interoperability between AI systems and monitoring tools.

Leading Market Participants

  • Arize AI
  • Langfuse
  • LangChain (LangSmith)
  • Datadog
  • New Relic
  • Dynatrace
  • Cisco (Splunk)
  • Weights & Biases
  • Galileo
  • WhyLabs
  • Fiddler AI
  • Coralogix
  • Honeycomb
  • Microsoft
  • Google
  • Other Prominent Players

Table of Content

Chapter 1. Executive Summary: Global AI Agent Observability Market

Chapter 2. Research Methodology & Research Framework

  • 2.1. Research Objective
  • 2.2. Product Overview
  • 2.3. Market Segmentation
  • 2.4. Qualitative Research
    • 2.4.1. Primary & Secondary Sources
  • 2.5. Quantitative Research
    • 2.5.1. Primary & Secondary Sources
  • 2.6. Breakdown of Primary Research Respondents, By Region
  • 2.7. Assumption for Study
  • 2.8. Market Size Estimation
  • 2.9. Data Triangulation

Chapter 3. Global AI Agent Observability Market Overview

  • 3.1. Industry Value Chain Analysis
    • 3.1.1. Foundation Model & LLM / Agent Framework Providers
    • 3.1.2. Telemetry, Tracing & OpenTelemetry Instrumentation Layer
    • 3.1.3. AI Observability, Evaluation & Guardrail Platform Vendors
    • 3.1.4. Integration, Managed-Service & AIOps Partners
    • 3.1.5. Enterprise DevOps, ML & Compliance Teams (BFSI, IT & Telecom, Healthcare)
  • 3.2. Industry Outlook
    • 3.2.1. Overview of the Global AI Agent Observability & LLMOps Industry
    • 3.2.2. Production Agent Deployment Driving Tracing, Evaluation & Cost Monitoring
    • 3.2.3. Governance, Audit-Trail & Guardrail Requirements for Regulated Workloads
  • 3.3. PESTLE Analysis
  • 3.4. Porter's Five Forces Analysis
    • 3.4.1. Bargaining Power of Suppliers
    • 3.4.2. Bargaining Power of Buyers
    • 3.4.3. Threat of Substitutes
    • 3.4.4. Threat of New Entrants
    • 3.4.5. Degree of Competition
  • 3.5. Market Growth and Outlook
    • 3.5.1. Market Revenue Estimates and Forecast (US$ Mn), 2020-2035
    • 3.5.2. Price Trend Analysis, By Offering

Chapter 4. Global AI Agent Observability Market Analysis

  • 4.1. Competition Dashboard
    • 4.1.1. Market Concentration Rate
    • 4.1.2. Company Market Share Analysis (Value %), 2025
    • 4.1.3. Competitor Mapping & Benchmarking

Chapter 5. Global AI Agent Observability Market Analysis

  • 5.1. Market Dynamics and Trends
    • 5.1.1. Growth Drivers
    • 5.1.2. Restraints
    • 5.1.3. Opportunity
    • 5.1.4. Key Trends
  • 5.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 5.2.1. By Offering
      • 5.2.1.1. Key Insights
        • 5.2.1.1.1. Solutions / Software
          • 5.2.1.1.1.1. Tracing & Evaluation
          • 5.2.1.1.1.2. Monitoring & Alerting
          • 5.2.1.1.1.3. Guardrails & Quality
        • 5.2.1.1.2. Services
          • 5.2.1.1.2.1. Integration
          • 5.2.1.1.2.2. Managed
    • 5.2.2. By Capability
      • 5.2.2.1. Key Insights
        • 5.2.2.1.1. Agent Tracing
        • 5.2.2.1.2. Prompt & Output Evaluation
        • 5.2.2.1.3. Cost & Token Monitoring
        • 5.2.2.1.4. Latency & Performance
        • 5.2.2.1.5. Drift & Hallucination Detection
        • 5.2.2.1.6. Security & Guardrails
    • 5.2.3. By Model Type Monitored
      • 5.2.3.1. Key Insights
        • 5.2.3.1.1. Proprietary / Closed
        • 5.2.3.1.2. Open-Source
        • 5.2.3.1.3. Fine-Tuned / Custom
    • 5.2.4. By Deployment
      • 5.2.4.1. Key Insights
        • 5.2.4.1.1. Cloud
        • 5.2.4.1.2. On-Premises
        • 5.2.4.1.3. Hybrid
    • 5.2.5. By Organization Size
      • 5.2.5.1. Key Insights
        • 5.2.5.1.1. Large Enterprises
        • 5.2.5.1.2. SMEs
    • 5.2.6. By End-Use Industry
      • 5.2.6.1. Key Insights
        • 5.2.6.1.1. BFSI
        • 5.2.6.1.2. IT & Telecom
        • 5.2.6.1.3. Healthcare
        • 5.2.6.1.4. Retail & E-commerce
        • 5.2.6.1.5. Manufacturing
        • 5.2.6.1.6. Government
        • 5.2.6.1.7. Others
    • 5.2.7. By Region
      • 5.2.7.1. Key Insights
        • 5.2.7.1.1. North America
          • 5.2.7.1.1.1. The U.S.
          • 5.2.7.1.1.2. Canada
          • 5.2.7.1.1.3. Mexico
        • 5.2.7.1.2. Europe
          • 5.2.7.1.2.1. Western Europe
            • 5.2.7.1.2.1.1. The UK
            • 5.2.7.1.2.1.2. Germany
            • 5.2.7.1.2.1.3. France
            • 5.2.7.1.2.1.4. Italy
            • 5.2.7.1.2.1.5. Spain
            • 5.2.7.1.2.1.6. Rest of Western Europe
          • 5.2.7.1.2.2. Eastern Europe
            • 5.2.7.1.2.2.1. Poland
            • 5.2.7.1.2.2.2. Russia
            • 5.2.7.1.2.2.3. Rest of Eastern Europe
        • 5.2.7.1.3. Asia Pacific
          • 5.2.7.1.3.1. China
          • 5.2.7.1.3.2. India
          • 5.2.7.1.3.3. Japan
          • 5.2.7.1.3.4. Australia & New Zealand
          • 5.2.7.1.3.5. South Korea
          • 5.2.7.1.3.6. ASEAN
          • 5.2.7.1.3.7. Rest of Asia Pacific
        • 5.2.7.1.4. Middle East & Africa (MEA)
          • 5.2.7.1.4.1. Saudi Arabia
          • 5.2.7.1.4.2. South Africa
          • 5.2.7.1.4.3. UAE
          • 5.2.7.1.4.4. Rest of MEA
        • 5.2.7.1.5. South America
          • 5.2.7.1.5.1. Argentina
          • 5.2.7.1.5.2. Brazil
          • 5.2.7.1.5.3. Rest of South America

Chapter 6. North America Market Analysis

  • 6.1. Market Dynamics and Trends
    • 6.1.1. Growth Drivers
    • 6.1.2. Restraints
    • 6.1.3. Opportunity
    • 6.1.4. Key Trends
  • 6.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 6.2.1. Key Insights
      • 6.2.1.1. By Offering
      • 6.2.1.2. By Capability
      • 6.2.1.3. By Model Type Monitored
      • 6.2.1.4. By Deployment
      • 6.2.1.5. By Organization Size
      • 6.2.1.6. By End-Use Industry
      • 6.2.1.7. By Country

Chapter 7. Europe Market Analysis

  • 7.1. Market Dynamics and Trends
    • 7.1.1. Growth Drivers
    • 7.1.2. Restraints
    • 7.1.3. Opportunity
    • 7.1.4. Key Trends
  • 7.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 7.2.1. Key Insights
      • 7.2.1.1. By Offering
      • 7.2.1.2. By Capability
      • 7.2.1.3. By Model Type Monitored
      • 7.2.1.4. By Deployment
      • 7.2.1.5. By Organization Size
      • 7.2.1.6. By End-Use Industry
      • 7.2.1.7. By Country

Chapter 8. Asia Pacific Market Analysis

  • 8.1. Market Dynamics and Trends
    • 8.1.1. Growth Drivers
    • 8.1.2. Restraints
    • 8.1.3. Opportunity
    • 8.1.4. Key Trends
  • 8.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 8.2.1. Key Insights
      • 8.2.1.1. By Offering
      • 8.2.1.2. By Capability
      • 8.2.1.3. By Model Type Monitored
      • 8.2.1.4. By Deployment
      • 8.2.1.5. By Organization Size
      • 8.2.1.6. By End-Use Industry
      • 8.2.1.7. By Country

Chapter 9. Middle East & Africa Market Analysis

  • 9.1. Market Dynamics and Trends
    • 9.1.1. Growth Drivers
    • 9.1.2. Restraints
    • 9.1.3. Opportunity
    • 9.1.4. Key Trends
  • 9.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 9.2.1. Key Insights
      • 9.2.1.1. By Offering
      • 9.2.1.2. By Capability
      • 9.2.1.3. By Model Type Monitored
      • 9.2.1.4. By Deployment
      • 9.2.1.5. By Organization Size
      • 9.2.1.6. By End-Use Industry
      • 9.2.1.7. By Country

Chapter 10. South America Market Analysis

  • 10.1. Market Dynamics and Trends
    • 10.1.1. Growth Drivers
    • 10.1.2. Restraints
    • 10.1.3. Opportunity
    • 10.1.4. Key Trends
  • 10.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 10.2.1. Key Insights
      • 10.2.1.1. By Offering
      • 10.2.1.2. By Capability
      • 10.2.1.3. By Model Type Monitored
      • 10.2.1.4. By Deployment
      • 10.2.1.5. By Organization Size
      • 10.2.1.6. By End-Use Industry
      • 10.2.1.7. By Country

Chapter 11. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, and Business Strategy Outlook)

  • 11.1. Arize AI
  • 11.2. Langfuse
  • 11.3. LangChain (LangSmith)
  • 11.4. Datadog
  • 11.5. New Relic
  • 11.6. Dynatrace
  • 11.7. Cisco (Splunk)
  • 11.8. Weights & Biases
  • 11.9. Galileo
  • 11.10. WhyLabs
  • 11.11. Fiddler AI
  • 11.12. Coralogix
  • 11.13. Honeycomb
  • 11.14. Microsoft
  • 11.15. Google
  • 11.16. Other Prominent Players

Chapter 12. Annexure

  • 12.1. List of Secondary Sources
  • 12.2. Key Country Markets- Macro Economic Outlook/Indicators