에이전틱 커머스 시장 : 제공, 거래 유형, 채널, 용도, 최종사용자, 지역별 - 시장 규모, 업계 역학, 기회 분석 및 예측(2026-2035년)

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