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2104734

全球代理商務市場：按產品、交易類型、通路、應用程式、最終用戶和地區分類－市場規模、產業動態、機會分析和預測（2026-2035 年）

Global Agentic Commerce Market By Offering, Transaction Type, Channel, Application, End User, Region - Market Size, Industry Dynamics, Opportunity Analysis and Forecast For 2026-2035
出版日期: | 出版商: Astute Analytica | 英文 260 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

在全球人工智慧（AI）、自主代理和智慧自動化技術的快速普及推動下，智慧商務市場正成為數位商務領域成長最快的細分市場之一。預計到2025年，該市場規模約為10億美元，並預計在2035年大幅成長至約300億美元，在2026年至2035年的預測期內，複合年成長率將高達40.5%。

市場快速擴張的驅動力來自生成式人工智慧、大規模語言模型、機器學習演算法和雲端運算基礎設施的顯著進步。這些技術使人工智慧代理商能夠執行產品發現、建議產生、價格比較、供應商評估、交易管理和自動化客戶服務等高級功能。隨著企業將這些功能融入其商業策略，人工智慧代理正日益成為消費者和賣家之間的重要中介，從而改變產品的發現、評估和購買方式。

顯著的市場趨勢

Google、OpenAI 和亞馬遜等公司正在透過開發先進的語言模型、智慧助理和數位生態系統，在人工智慧主導的商業領域確立強大的地位，使消費者能夠透過基於自然語言的對話與商業平台進行互動。

谷歌也積極推動智慧商務生態系統的發展，致力於開發開放標準，使人工智慧代理商能夠更有效地與商務系統協同工作。通用商務協議 (UCP) 的推出旨在建立一個通用框架，使人工智慧代理商能夠參與商務流程的多個階段，例如產品發現、結帳和售後互動。

亞馬遜正透過Rufus等人工智慧購物助手，加強其在面向消費者的代理電商領域的影響力。 Rufus利用海量產品數據、客戶行為洞察和市場訊息，為大規模購物決策提供支援。透過將對話式人工智慧直接整合到購物體驗中，亞馬遜使用戶能夠詢問產品問題、比較選項、獲取推薦，並更有效率地做出購買決策。

主要成長要素

高轉換率和顯著的流量成長是推動智慧商務市場擴張的關鍵因素。這是因為企業越來越意識到人工智慧驅動的商務系統能夠創造更有效率、更個人化的購物體驗。與嚴重依賴客戶搜尋、瀏覽和人工產品比較的傳統數位商務管道不同，智慧商務使人工智慧系統能夠主動解讀消費者意圖，識別相關產品，並更精準地促成交易。這種向智慧化和自動化購物體驗的轉變，為零售商創造了新的機遇，有助於提升客戶參與、提高銷售效率並進一步提高轉換率。

新機會的趨勢

標準化協議的興起是一股新趨勢，預計將透過增強整個人工智慧主導商業生態系統的互通性、可靠性和擴充性，加速智慧商務市場的成長。隨著自主人工智慧代理擴大參與產品發現、購買決策、支付和交易執行，通用技術標準的需求變得至關重要。標準化協議為人工智慧代理商、經銷商、支付提供者和商務平台之間的有效溝通提供了必要的底層數位基礎設施。透過建立機器可讀框架，這些標準使智慧代理能夠安全地交換資訊、解讀產品資料、驗證交易要求並更有效率地執行商務活動。

最佳化障礙

日益嚴峻的網路安全和詐欺風險對智慧商務市場的發展構成重大挑戰，可能阻礙其成長。隨著企業擴大部署自主人工智慧代理來管理產品發現、購買決策、支付和客戶互動，機器主導交易的擴展也為惡意攻擊者利用漏洞創造了新的機會。與傳統電子商務系統不同，智慧商務環境涉及自主決策流程、互聯的數位平台以及人工智慧代理商、經銷商、支付提供者和第三方服務之間持續的資料交換。這種日益成長的複雜性擴大了潛在的攻擊面，並引發了與身分驗證、交易完整性、資料保護和未授權存取相關的新安全問題。

目錄

第1章摘要整理：全球代理商務市場

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：按地區分類
  • 本研究的前提
  • 市場規模估算
  • 數據三角測量

第3章：全球代理商商務市場概覽

  • 產業價值鏈分析
  • 產業展望
    • 全球代理商業產業概覽
    • AI驅動的產品發現、代理對代理（A2A）交易和機器可讀目錄。
    • 基於代理的支付協議（ACP/UCP）、人機互動認證和機器人詐欺預防。
  • 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：透過報價

第4章：全球代理商電商市場分析

  • 競爭對手儀表板
    • 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：全球代理商電商市場分析

  • 市場動態和趨勢
    • 成長要素
    • 抑制因子
    • 機會
    • 主要趨勢
  • 市場規模及預測，2020-2035年
    • 報價
      • 關鍵見解
        • 平台/基礎設施
          • 代理支付網路
          • 代理協議/API
          • 目錄發現
        • 服務
    • 按交易類型
      • 關鍵見解
        • 代理輔助（人工輔助審核）
        • 完全自主
    • 通道特異性
      • 關鍵見解
        • B2C
        • B2B
    • 用途別
      • 關鍵見解
        • 零售和購物
        • 旅行和預訂
        • 金融服務
        • 定期訂閱和續訂
    • 最終用戶
      • 關鍵見解
        • 經銷商和零售商
        • 付款網路
        • 市場
    • 按地區
      • 關鍵見解
        • 北美洲
          • 美國
          • 加拿大
          • 墨西哥
        • 歐洲
          • 西歐
            • 英國
            • 德國
            • 法國
            • 義大利
            • 西班牙
            • 其他西歐國家
          • 東歐
            • 波蘭
            • 俄羅斯
            • 其他東歐國家
        • 亞太地區
          • 中國
          • 印度
          • 日本
          • 澳洲和紐西蘭
          • 韓國
          • ASEAN
          • 其他亞太國家
        • 中東和非洲（MEA）
          • 沙烏地阿拉伯
          • 南非
          • UAE
          • 其他中東和非洲國家
        • 南美洲
          • 阿根廷
          • 巴西
          • 其他南美國家

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

  • OpenAI
  • Salesforce, Inc.
  • Mastercard
  • Google LLC
  • Artisan AI Inc.
  • TENEO.AI
  • Stripe, Inc.
  • Verofax
  • Uniphore
  • Other Prominent Players

第12章附錄

簡介目錄
Product Code: AA07261896

The global agentic commerce market is emerging as one of the fastest-growing segments within the digital commerce landscape, driven by the rapid adoption of artificial intelligence (AI), autonomous agents, and intelligent automation technologies. The market is estimated to be valued at approximately USD 1.0 billion in 2025 and is projected to expand significantly to around USD 30 billion by 2035, registering a strong compound annual growth rate (CAGR) of 40.5% during the forecast period from 2026 to 2035.

The rapid expansion of the market is being fueled by significant advancements in generative AI, large language models, machine learning algorithms, and cloud-based computing infrastructure. These technologies enable AI agents to perform sophisticated functions such as product discovery, recommendation generation, price comparison, supplier evaluation, transaction management, and customer service automation. As businesses integrate these capabilities into their commerce strategies, AI agents are becoming increasingly important intermediaries between consumers and merchants, reshaping how products are discovered, evaluated, and purchased.

Noteworthy Market Developments

Companies such as Google, OpenAI, and Amazon have established strong positions in AI-driven commerce by developing advanced language models, intelligent assistants, and digital ecosystems that enable consumers to interact with commerce platforms through natural language-based conversations.

Google is also advancing the agentic commerce ecosystem through initiatives focused on creating open standards that allow AI agents to interact with commerce systems more effectively. The introduction of the Universal Commerce Protocol (UCP) represents an effort to establish a common framework through which AI agents can participate across multiple stages of the commerce journey, including product discovery, checkout, and post-purchase interactions.

Amazon has strengthened its influence in consumer-facing agentic commerce through AI-powered shopping assistants such as Rufus, which leverages extensive product data, customer behavior insights, and marketplace intelligence to support shopping decisions at a large scale. By integrating conversational AI directly into the shopping experience, Amazon enables users to ask product-related questions, compare options, receive recommendations, and navigate purchasing decisions more efficiently.

Core Growth Drivers

High conversion rates and significant traffic growth represent major factors driving the expansion of the agentic commerce market, as businesses increasingly recognize the ability of artificial intelligence (AI)-powered commerce systems to generate more efficient and personalized purchasing experiences. Unlike traditional digital commerce channels that depend heavily on customer-initiated searches, browsing behavior, and manual product comparisons, agentic commerce enables AI systems to actively interpret consumer intent, identify relevant products, and facilitate transactions with greater precision. This shift toward intelligent, automated shopping interactions is creating new opportunities for retailers to improve customer engagement, increase sales efficiency, and achieve stronger conversion performance.

Emerging Opportunity Trends

The rise of standardized protocols represents an emerging opportunity trend expected to accelerate growth in the agentic commerce market by enabling greater interoperability, trust, and scalability across AI-driven commerce ecosystems. As autonomous AI agents become increasingly involved in product discovery, purchasing decisions, payments, and transaction execution, the need for common technical standards has become critical. Standardized protocols provide the underlying digital infrastructure required for AI agents, merchants, payment providers, and commerce platforms to communicate effectively with one another. By establishing machine-readable frameworks, these standards allow intelligent agents to securely exchange information, interpret product data, verify transaction requirements, and execute commerce activities more efficiently.

Barriers to Optimization

Heightened cybersecurity and fraud risks represent a significant challenge that may restrain the growth of the agentic commerce market. As businesses increasingly adopt autonomous AI agents to manage product discovery, purchasing decisions, payments, and customer interactions, the expansion of machine-driven transactions creates new opportunities for malicious actors to exploit vulnerabilities. Unlike traditional e-commerce systems, agentic commerce environments involve autonomous decision-making processes, interconnected digital platforms, and continuous data exchange between AI agents, merchants, payment providers, and third-party services. This increased complexity expands the potential attack surface and introduces new security concerns related to identity verification, transaction integrity, data protection, and unauthorized access.

Detailed Market Segmentation

By traction type, the agentic commerce market in 2026 remains primarily centered around supervised autonomy, with agent-assisted frameworks accounting for approximately 78% of the market share. This dominance reflects the current balance between advancing artificial intelligence capabilities and the need for human oversight in financial transactions, data security, and regulatory compliance. While AI agents have become increasingly capable of performing complex tasks such as product discovery, recommendation generation, supplier evaluation, and purchase optimization, fully autonomous machine-to-machine commerce remains limited due to unresolved challenges related to financial accountability, transaction authorization, and risk management.

By channel, the business-to-consumer (B2C) segment represents the leading category in the agentic commerce market, accounting for approximately 61% of the overall market share. This dominance is primarily driven by the rapid adoption of AI-powered personalized shopping assistants that enable consumers to discover products, compare options, receive recommendations, and complete transactions with minimal manual effort. B2C commerce environments generate large volumes of consumer interaction data, making them highly suitable for the implementation of autonomous AI agents capable of understanding individual preferences, purchasing behavior, and real-time shopping intent. As consumers increasingly demand faster, more convenient, and personalized digital experiences, businesses are integrating agentic technologies to enhance customer engagement and improve conversion efficiency.

By application, retail and shopping applications represent the dominant segment within the agentic commerce market, accounting for approximately 55% of the market share and surpassing other application areas such as travel and hospitality. The strong position of retail applications is primarily driven by the sector's extensive digital product catalogs, complex purchasing environments, and high demand for personalized, automated customer experiences. Retail businesses generate vast amounts of structured and unstructured data, including product descriptions, pricing information, customer preferences, inventory updates, reviews, and purchasing patterns. These large-scale datasets provide an ideal environment for artificial intelligence (AI) agents and language models to analyze, optimize, and deliver more intelligent commerce interactions in real time.

By end user, merchants represent the foundational backbone of the agentic commerce market, accounting for the largest share with approximately 48% market dominance. The leading position of merchants is driven by their critical role in the digital commerce ecosystem, where businesses are increasingly adopting artificial intelligence (AI)-powered solutions to enhance product discovery, customer engagement, transaction efficiency, and operational decision-making. As consumer behavior shifts toward AI-assisted shopping experiences, merchants are recognizing the need to optimize their digital infrastructure for interactions not only with human customers but also with autonomous AI agents that increasingly influence purchasing decisions.

Segment Breakdown

By Offering

  • Platforms/Infrastructure
  • Agent Payment Rails
  • Agent Protocols/APIs
  • Catalog & Discovery
  • Services

By Transaction Type

  • Agent-Assisted (Human-Approved)
  • Fully Autonomous

By Channel

  • B2C
  • B2B

By Application

  • Retail & Shopping
  • Travel & Booking
  • Financial Services
  • Subscriptions & Renewals

By End User

  • Merchants & Retailers
  • Payment Networks
  • Marketplaces

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 holds a leading position in the agentic commerce market, accounting for approximately 38% of the global market share in 2025. The region's dominance is primarily driven by its highly advanced digital ecosystem, strong cloud computing infrastructure, widespread enterprise technology adoption, and rapid implementation of artificial intelligence (AI)-based commercial solutions. North America has established itself as a global hub for AI innovation due to the presence of leading technology companies, mature digital payment networks, sophisticated e-commerce platforms, and a strong ecosystem of startups developing autonomous AI systems.
  • The United States serves as the primary growth engine within the North American agentic commerce market, contributing approximately 83% of the regional market value. The country's leadership is supported by a combination of strong venture capital activity, a highly developed technology ecosystem, and early enterprise adoption of AI-powered commerce models.
  • Retail companies across the United States are making significant investments in advanced digital commerce architectures, particularly headless commerce models that separate front-end customer experiences from back-end commerce infrastructure. These architectures provide the flexibility required to support seamless interactions between AI agents and retail platforms, enabling machine customers to search for products, compare options, complete purchases, and manage transactions autonomously.

Leading Market Participants

  • OpenAI
  • Salesforce, Inc.
  • Mastercard
  • Google LLC
  • Artisan AI Inc.
  • TENEO.AI
  • Stripe, Inc.
  • Verofax
  • Uniphore
  • Other Prominent Players

Table of Content

Chapter 1. Executive Summary: Global Agentic Commerce 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 Agentic Commerce Market Overview

  • 3.1. Industry Value Chain Analysis
    • 3.1.1. Foundation Model, Reasoning-Agent & LLM Providers
    • 3.1.2. Agent Payment Rails, Protocol (ACP/UCP) & API Infrastructure Developers
    • 3.1.3. Merchant Catalog, Discovery & Machine-Readable Feed Enablement Providers
    • 3.1.4. Integration, Identity/Fraud & Managed-Service Partners
    • 3.1.5. End Users (Merchants & Retailers, Payment Networks, Marketplaces)
  • 3.2. Industry Outlook
    • 3.2.1. Overview of the Global Agentic Commerce Industry
    • 3.2.2. AI-Agent Product Discovery, Agent-to-Agent (A2A) Transactions & Machine-Readable Catalogs
    • 3.2.3. Agent Payment Protocols (ACP/UCP), Human-in-the-Loop Authorization & Bot Fraud Governance
  • 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 Agentic Commerce 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 Agentic Commerce 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. Platforms/Infrastructure
          • 5.2.1.1.1.1. Agent Payment Rails
          • 5.2.1.1.1.2. Agent Protocols/APIs
          • 5.2.1.1.1.3. Catalog & Discovery
        • 5.2.1.1.2. Services
    • 5.2.2. By Transaction Type
      • 5.2.2.1. Key Insights
        • 5.2.2.1.1. Agent-Assisted (Human-Approved)
        • 5.2.2.1.2. Fully Autonomous
    • 5.2.3. By Channel
      • 5.2.3.1. Key Insights
        • 5.2.3.1.1. B2C
        • 5.2.3.1.2. B2B
    • 5.2.4. By Application
      • 5.2.4.1. Key Insights
        • 5.2.4.1.1. Retail & Shopping
        • 5.2.4.1.2. Travel & Booking
        • 5.2.4.1.3. Financial Services
        • 5.2.4.1.4. Subscriptions & Renewals
    • 5.2.5. By End User
      • 5.2.5.1. Key Insights
        • 5.2.5.1.1. Merchants & Retailers
        • 5.2.5.1.2. Payment Networks
        • 5.2.5.1.3. Marketplaces
    • 5.2.6. By Region
      • 5.2.6.1. Key Insights
        • 5.2.6.1.1. North America
          • 5.2.6.1.1.1. The U.S.
          • 5.2.6.1.1.2. Canada
          • 5.2.6.1.1.3. Mexico
        • 5.2.6.1.2. Europe
          • 5.2.6.1.2.1. Western Europe
            • 5.2.6.1.2.1.1. The UK
            • 5.2.6.1.2.1.2. Germany
            • 5.2.6.1.2.1.3. France
            • 5.2.6.1.2.1.4. Italy
            • 5.2.6.1.2.1.5. Spain
            • 5.2.6.1.2.1.6. Rest of Western Europe
          • 5.2.6.1.2.2. Eastern Europe
            • 5.2.6.1.2.2.1. Poland
            • 5.2.6.1.2.2.2. Russia
            • 5.2.6.1.2.2.3. Rest of Eastern Europe
        • 5.2.6.1.3. Asia Pacific
          • 5.2.6.1.3.1. China
          • 5.2.6.1.3.2. India
          • 5.2.6.1.3.3. Japan
          • 5.2.6.1.3.4. Australia & New Zealand
          • 5.2.6.1.3.5. South Korea
          • 5.2.6.1.3.6. ASEAN
          • 5.2.6.1.3.7. Rest of Asia Pacific
        • 5.2.6.1.4. Middle East & Africa (MEA)
          • 5.2.6.1.4.1. Saudi Arabia
          • 5.2.6.1.4.2. South Africa
          • 5.2.6.1.4.3. UAE
          • 5.2.6.1.4.4. Rest of MEA
        • 5.2.6.1.5. South America
          • 5.2.6.1.5.1. Argentina
          • 5.2.6.1.5.2. Brazil
          • 5.2.6.1.5.3. 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 Transaction Type
      • 6.2.1.3. By Channel
      • 6.2.1.4. By Application
      • 6.2.1.5. By End User
      • 6.2.1.6. 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 Transaction Type
      • 7.2.1.3. By Channel
      • 7.2.1.4. By Application
      • 7.2.1.5. By End User
      • 7.2.1.6. 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 Transaction Type
      • 8.2.1.3. By Channel
      • 8.2.1.4. By Application
      • 8.2.1.5. By End User
      • 8.2.1.6. 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 Transaction Type
      • 9.2.1.3. By Channel
      • 9.2.1.4. By Application
      • 9.2.1.5. By End User
      • 9.2.1.6. 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 Transaction Type
      • 10.2.1.3. By Channel
      • 10.2.1.4. By Application
      • 10.2.1.5. By End User
      • 10.2.1.6. 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. OpenAI
  • 11.2. Salesforce, Inc.
  • 11.3. Mastercard
  • 11.4. Google LLC
  • 11.5. Artisan AI Inc.
  • 11.6. TENEO.AI
  • 11.7. Stripe, Inc.
  • 11.8. Verofax
  • 11.9. Uniphore
  • 11.10. Other Prominent Players

Chapter 12. Annexure

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