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Agentic AI Market

Report Code TC 8694
Published in Jun, 2025, By MarketsandMarkets™
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Agentic AI Market by Software (Computational Agents, Robotic Agents), Agent Role (Marketing, Sales, Operations & Supply Chain, ITSM), Application (Workflow Automation, Planning & Decision Support, Navigation & Mobility) - Global Forecast to 2032

Overview

The agentic AI market is projected to grow from USD 13.81 billion in 2025 to USD 140.80 billion by 2032, registering a CAGR of 39.3% over the forecast period. This rapid expansion is driven by a fundamental transformation in enterprise automation strategies, particularly the shift from static task execution to dynamic, goal-directed autonomy. A key growth driver is the increasing adoption of multi-agent orchestration frameworks that enable intelligent systems to plan, reason, and act independently across complex workflows.

Platforms such as LangGraph, AutoGen, and CrewAI are used to build agents capable of handling long-horizon tasks like IT troubleshooting, customer query resolution, or supply chain coordination with minimal human intervention. Enterprises are re-architecting systems to embed agentic logic at the core, enabling autonomous decision loops that improve efficiency, reduce latency, and scale execution without traditional workflow constraints. This architectural shift positions agentic AI as a foundational layer across finance, healthcare, retail, and manufacturing sectors.

The agentic AI market is positively influenced by the rapid growth of IT support & service management, operations & supply chain, navigation & mobility, and intelligent workflow automation segments. Strong adoption in professional services and BFSI also contributes significantly to market momentum.

Agentic AI Market

Attractive Opportunities in the Agentic AI Market

ASIA PACIFIC

The agentic AI market in Asia Pacific is expanding rapidly due to national AI strategies and increasing demand for localized automation. Governments are investing in autonomous systems for smart cities, public services, and industrial automation, while local vendors are building lightweight agent frameworks optimized for language diversity.

Vendors that offer agent orchestration, safe execution environments, and domain-specific capabilities will lead enterprise adoption by offering control, scalability, and measurable impact via generalist and specialist agents.

Agentic AI is advancing toward cognitive agents that combine language understanding, tool use, and multi-agent coordination. These agents operate across cloud and edge environments, interacting with users, APIs, and physical systems.

Agentic AI is transforming enterprise automation by enabling intelligent agents to perform multi-step tasks, make decisions, and coordinate across systems. The result is a shift from task execution to outcome-driven autonomy.

Adoption is rising in domains requiring real-time adaptation, such as logistics, IT operations, customer support, and field services. Businesses are moving beyond deterministic automation toward agents that reason, plan, and interact using natural language.

Global Agentic AI Market Dynamics

Driver: Integration of agentic intelligence into enterprise productivity and workflow platforms

A major growth driver for the agentic AI market is integrating agentic intelligence into enterprise productivity and workflow platforms. As organizations shift from static automation tools to adaptive, AI-driven systems, agentic capabilities are embedded directly into daily-use software such as CRMs, ERPs, and IT service platforms. This trend is evident in offerings such as Microsoft Copilot, Salesforce Einstein Copilot and ServiceNow’s AI agents AI systems are not merely assisting but actively orchestrating tasks, retrieving contextual data, and initiating workflows based on user intent and system signals.

Introducing memory, tool use, and reasoning into these enterprise environments transforms how work gets done, thus enabling agents to handle multi-step activities such as sales forecasting, procurement approvals, or issue resolution autonomously. This embedded agentic layer also reduces the cognitive load on employees by converting fragmented software interactions into outcome-driven dialogues or decisions. Enterprises invest in modular agent APIs and agent-governance pipelines to ensure continuity, auditability, and customization across departments. As these intelligent agents demonstrate ROI in cost savings, efficiency gains, and reduced time-to-action, adoption is accelerating across industries. This widespread infusion of agentic AI into operational software stacks fuels market growth and establishes enterprise platforms as key distribution channels for autonomous AI capabilities.

Restraint: Limited generalization of computational AI agents across dynamic, multi-modal environments

A critical restraint in the growth of the agentic AI market is the limited generalization across dynamic, multi-modal environments. While agentic AI systems can perform well within narrowly defined contexts, their ability to operate reliably across varied data types, real-time signals, and changing workflows remains constrained. For instance, an agent trained to coordinate enterprise IT tasks may struggle to adapt when the underlying APIs, task structures, or user behaviors evolve without retraining or fine-tuning. Similarly, embodied agents such as drones or mobile robots often experience performance drops when exposed to novel spatial layouts, lighting conditions, or sensor noise.

This lack of cross-domain robustness limits scalability and increases the cost of customization for each deployment scenario. Multi-modal agents—those expected to process and integrate text, vision, voice, or geospatial data—face even greater complexity when required to act in unpredictable environments. As a result, vendors are forced to rely on tightly constrained workflows or hybrid control systems, which undercut the autonomy and flexibility that define agentic AI. Until foundational models and orchestration layers improve in few-shot adaptation and real-time grounding, enterprise adoption will remain conservative in high-variance settings like manufacturing, field operations, or public infrastructure.

 

Opportunity: Rising deployment of domain-specific agent frameworks in highly regulated industry verticals

A major opportunity for the agentic AI market lies in developing domain-specific agent frameworks for high-value verticals such as healthcare, finance, legal services, and industrial operations. Generic AI agents often lack the contextual depth, regulatory awareness, or task-specific logic needed for mission-critical use cases. However, as enterprises mature in their AI adoption, there is a growing demand for purpose-built agentic systems that combine foundational models with structured ontologies, industry-grade toolchains, and embedded compliance safeguards. For example, agentic AI can automate care coordination, pre-authorizations, and clinical documentation in healthcare by integrating with EHRs and following strict data privacy protocols. In finance, agents can handle trade reconciliation, risk flagging, and regulatory submissions, provided they are tuned to specific jurisdictional norms.

These verticalized agents deliver higher ROI and reduce safety and explainability risks by operating within controlled, well-defined boundaries. Vendors that offer modular agent stacks—combining domain-tuned LLMs, approved toolsets, and secure execution environments—are well-positioned to lead in this space. As industry-specific benchmarks emerge and interoperability improves, vertical agent frameworks are expected to unlock new monetization models and drive rapid expansion in enterprise-grade deployments.

Challenge: Lack of standardized evaluation metrics and benchmarking protocols for agent performance

A significant challenge in the agentic AI market is the lack of standardized evaluation metrics and benchmarking protocols for agent performance. Unlike traditional AI models, which are assessed using well-defined accuracy or latency metrics, agentic systems operate across multi-step, dynamic tasks where success is contextual and often non-deterministic. This creates major difficulties in measuring reliability, safety, efficiency, or goal completion in a consistent and reproducible way. For instance, evaluating an AI agent that autonomously troubleshoots IT issues or navigates a warehouse involves task complexity, tool accuracy, time to resolution, and recovery from unexpected inputs.

These variables are highly dynamic and vary based on the operating environment. Current testing methods often rely on synthetic benchmarks or one-off demonstrations, which fail to reflect real-world deployment conditions. This lack of benchmarking complicates vendor comparisons and limits regulatory acceptance and enterprise procurement. Organizations struggle to assess agent maturity without clear performance thresholds, leading to longer piloting cycles and slower adoption. Establishing task-specific evaluation suites, success rate metrics, and traceability standards will validate agent behavior and enable safe, scalable deployment across industries.

Global Agentic AI Market Ecosystem Analysis

The agentic AI ecosystem consists of layered capabilities spanning software, technology, and SaaS solutions. At the core are software providers building computational and robotic agents that can perceive, reason, and act autonomously. Technology providers enable this autonomy through specialized modules in embodied AI, generative modeling, and orchestration infrastructure.

SaaS vendors offer verticalized applications for workflow automation, knowledge retrieval, scene understanding, and domain-specific decision support on top of these layers. This stack collectively facilitates real-time autonomy in digital and physical environments, enabling enterprises to deploy intelligent agents at scale across logistics, finance, and healthcare manufacturing.

Top Companies in Agentic AI Market

Note: The above diagram only shows the representation of the Agentic AI Market ecosystem; it is not limited to the companies represented above.
Source: Secondary Research and MarketsandMarkets Analysis

 

Computational agents offering segment to account for highest growth rate during forecast period

Computational AI agents are poised to become the fastest-growing segment of the agentic AI market over the forecast period, driven by their high compatibility with existing enterprise infrastructure and rapid deployment potential. These agents operate across digital environments, enabling businesses to automate tasks such as customer query resolution, IT incident triaging, knowledge retrieval, and report generation without physical infrastructure. Unlike robotic agents, computational agents can be deployed directly into existing enterprise systems, including CRM platforms, productivity suites, and internal knowledge bases.

They are powered by large language models and orchestration frameworks that support planning, memory, tool integration, and real-time decision-making. Organizations across finance, healthcare, retail, and professional services are increasingly adopting these agents to improve operational efficiency, reduce latency, and scale intelligent workflows. The growing maturity of open-source runtimes and modular agent stacks is lowering development overhead, while cloud-native deployment options are accelerating time to production. As businesses prioritize automation that aligns with long-term goals and adaptive behavior, computational agents are becoming the foundation for scalable agentic AI strategies across a wide range of industries.

The workflow automation application segment to hold largest share during forecast period

By application, workflow automation is the largest segment by market share in 2025, primarily due to its broad enterprise applicability, high deployment volume, and clear ROI. Agentic AI enables automation to move beyond static rule-based scripts toward dynamic, context-aware systems that can reason, prioritize, and act across multi-step business processes. In banking, insurance, telecom, and professional services, agentic AI transforms workflows related to claims processing, onboarding, ticket resolution, invoice management, and service requests. These are high-frequency, high-complexity operations where autonomous agents can generate significant cost and efficiency gains.

Unlike traditional automation tools that operate in siloed environments, agentic AI agents leverage memory, tool orchestration, and real-time decision-making to coordinate tasks across multiple systems and data sources. This allows enterprises to consolidate fragmented processes under intelligent, goal-driven agents that reduce manual intervention and operational delays. As enterprises continue to integrate these agents into CRM, ERP, and ITSM platforms, the scale and value of workflow automation are expanding rapidly. The agents’ ability to deliver measurable impact across front-office and back-office functions has positioned them as the most commercially mature and widely adopted application in the ecosystem.

North America to emerge as the largest region during forecast period

North America leads the agentic AI market in scale and sophistication, supported by an ecosystem that combines advanced infrastructure, capital depth, and early enterprise adoption. According to the Stanford AI Index 2024, the US attracted more than 50 percent of global private AI investment in 2023, a funding pool that accelerates the commercialization of advanced agents across finance, retail, and healthcare. Cloud hyperscalers headquartered in the region operate over 70 percent of the world’s hyperscale data center capacity, giving developers low-latency access to model training and agent orchestration services.

Financial services, retail, and healthcare enterprises embed computational agents in core workflows such as onboarding, claims processing, and supply chain automation. For instance, several major banks use AI agents to conduct document verification and compliance checks autonomously, reducing turnaround times from days to minutes. Retail operators integrate autonomous agents into warehouse and delivery operations to improve fulfillment speed and accuracy. Also, strategic funding in AI research, semiconductors, and autonomy-related innovation has reinforced the region’s dominance. North America has established itself as the leading region in scaling agentic AI commercialization, fueled by a strong need for automation and a clear return on investment pathways.

LARGEST REGION BY MARKET SHARE IN 2025
CANADA FASTEST-GROWING MARKET IN REGION
Agentic AI Market by region

Recent Developments of Agentic AI Market

  • In May 2025, IBM announced new tools and frameworks for building and managing networks of AI agents, including pre-built domain agents for HR, procurement, and sales, which are integrated with AWS Marketplace technologies. A key highlight is the planned integration between Amazon Q index and IBM watsonx Orchestrate, enabling AI agents to access and act on data from multiple third-party applications like Salesforce, Slack, and Zendesk for more personalized automation.
  • In April 2025, Microsoft updated Microsoft Dynamics 365, with hundreds of new features across all major modules. Key updates include expanded AI-powered Copilot and agent capabilities to automate tasks, improve customer service, enhance sales productivity, streamline finance and supply chain operations, and boost HR and commerce experiences. The release also brings better integration, automation, and analytics to help businesses work smarter and faster.
  • In April 2025, Google launched Agent Space, a new platform that allows businesses and developers to build AI agents that can work together, even across different organizations. These agents can perform tasks, find information, and interact with each other using an open Agent-to-Agent (A2A) protocol. This platform simplifies AI agent creation, enabling users to automate workflows, conduct real-time research, and streamline tasks, potentially leading to a marketplace for AI agents.
  • In April 2025, Waymo and Toyota announced a strategic partnership to accelerate the development and deployment of autonomous driving technologies. The collaboration will combine Waymo’s expertise in autonomous systems with Toyota’s strengths in vehicle design and safety, aiming to create a new autonomous vehicle platform.

Key Market Players

List of Top Agentic AI Market Companies

The Agentic AI Market is dominated by a few major players that have a wide regional presence. The major players in the Agentic AI Market are

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Scope of the Report

Report Attribute Details
Market size available for years 2020–2032
Base year considered 2024
Forecast period 2025–2032
Forecast units USD (Million)
Segments Covered Offering, Agent Role, Technology, Application, End User, and Region
Regions covered North America, Europe, Asia Pacific, Middle East & Africa, and Latin America

Key Questions Addressed by the Report

What is agentic AI?

Agentic AI refers to artificial intelligence systems that autonomously pursue defined goals by perceiving their environment, making context-aware decisions, and executing multi-step actions without step-by-step human instructions. These systems are designed to reason, plan, use tools, and adapt dynamically based on feedback or changing objectives. Agentic AI can function in digital and physical environments, including software-based agents and those embedded within robots, drones, or vehicles. The focus lies on the software intelligence layer that enables proactive behavior, long-horizon task execution, and collaboration with other agents or systems to achieve complex outcomes.

What is the total CAGR expected to be recorded for the agentic AI market from 2025 to 2032?

The agentic AI market is expected to record a CAGR of 39.3% from 2025 to 2032.

How is the agentic AI market different from AI agents?

The agentic AI market is broader than the AI agents market, as it includes autonomous agents themselves and the full stack of technologies, orchestration frameworks, and service layers that enable goal-directed, adaptive behavior across digital and physical systems. While AI agents are the end products—software or robotic systems that act with autonomy—agentic AI encompasses the infrastructure, development tools, and governance models that support scalable, multi-agent intelligence in real-world applications.

Which are the key drivers supporting market growth?

Some factors driving the market growth include the rapid adoption of LLM orchestration frameworks accelerating autonomous agent deployment, advances in edge AI and neuromorphic chips enabling real-time autonomy in physical systems, rising industrial use of autonomous robotics boosting efficiency in logistics, agriculture, and field services, and growing demand for cost-effective, continuous operations driving autonomous system investments.

Which are the top 3 verticals prevailing in the agentic AI market?

BFSI, technology providers, and retail and e-commerce are the top verticals in the agentic AI market due to their high automation potential, data-rich environments, and clear ROI pathways. BFSI uses agents for fraud detection, compliance, and client onboarding. Technology firms embed agentic capabilities into cloud platforms and developer tools. Retail and e-commerce apply agents to optimize pricing, inventory, and fulfillment. These sectors are mature, digitally advanced, and focused on efficiency and personalization, driving rapid adoption.

Who are the key vendors in the market?

Some major players in the market include IBM (US), NVIDIA (US), OpenAI (US), Oracle (US), Microsoft (US), Google (US), AWS (US), Salesforce (US), LivePerson (US), Waymo (US), Tempus AI (US), Mobileye (Israel), Uber (US), DJI (China), Boston Dynamics (US), Shield AI (US), Anduril Industries (US), AeroVironment (US), Tesla (US), Kore.ai (US), Amelia (US), Softbank Robotics (Canada), Aisera (US), Rasa (US), Stability AI (UK), Infinitus Systems (US), Level AI (US), Leena AI (US), Cujo AI (US), Skydio (US), Cognigy (Germany), ANYbotics (Switzerland), Badger Technologies (US), Monica.im (Singapore), Deeproute.ai (US), Adept (US), Nanonets (US), Wayve (UK), Seegrid (US), and Blue River Technologies (US).

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Table of Contents

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TITLE
PAGE NO
INTRODUCTION
1
  • 1.1 OBJECTIVES OF THE STUDY
  • 1.2 MARKET DEFINITION
    INCLUSIONS AND EXCLUSIONS
  • 1.3 MARKET SCOPE
    MARKET SEGMENTATION
    REGIONS COVERED
    YEARS CONSIDERED FOR THE STUDY
  • 1.4 CURRENCY CONSIDERED
  • 1.5 STAKEHOLDERS
  • 1.6 SUMMARY OF CHANGES
RESEARCH METHODOLOGY
2
  • 2.1 RESEARCH DATA
    SECONDARY DATA
    PRIMARY DATA
    - BREAKUP OF PRIMARY PROFILES
    - KEY INDUSTRY INSIGHTS
  • 2.2 MARKET BREAKUP AND DATA TRIANGULATION
  • 2.3 MARKET SIZE ESTIMATION
    TOP-DOWN APPROACH
    BOTTOM-UP APPROACH
  • 2.4 MARKET FORECAST
  • 2.5 ASSUMPTIONS FOR THE STUDY
  • 2.6 LIMITATIONS OF THE STUDY
EXECUTIVE SUMMARY
3
PREMIUM INSIGHTS
4
  • 4.1 ATTRACTIVE OPPORTUNITIES IN THE GLOBAL AGENTIC AI MARKET
  • 4.2 AGENTIC AI MARKET, BY OFFERING, 2025 VS. 2032
  • 4.3 AGENTIC AI MARKET, BY AGENT ROLE, 2025 VS. 2032
  • 4.4 AGENTIC AI MARKET, BY TECHNOLOGY, 2025 VS. 2032
  • 4.5 AGENTIC AI MARKET, BY APPLICATION, 2025 VS. 2032
  • 4.6 AGENTIC AI MARKET, BY END-USER, 2025 VS. 2032
  • 4.7 AGENTIC AI MARKET, BY REGION
MARKET OVERVIEW AND INDUSTRY TRENDS
5
  • 5.1 INTRODUCTION
  • 5.2 MARKET DYNAMICS
    DRIVERS
    RESTRAINTS
    OPPORTUNITIES
    CHALLENGES
  • 5.3 EVOLUTION OF AGENTIC AI
  • 5.4 COMPARATIVE ANALYSIS: AI AGENTS VS AUTONOMOUS AI VS AGENTIC AI
  • 5.5 SUPPLY CHAIN ANALYSIS
  • 5.6 ECOSYSTEM ANALYSIS
  • 5.7 IMPACT OF 2025 US TARIFF – AGENTIC AI MARKET
    INTRODUCTION
    KEY TARIFF RATES
    PRICE IMPACT ANALYSIS
    - STRATEGIC SHIFTS AND EMERGING TRENDS
    IMPACT ON COUNTRY/REGION
    - US
    - CHINA
    - EUROPE
    - ASIA PACIFIC (EXCLUDING CHINA)
    IMPACT ON END-USE INDUSTRIES
    - BFSI
    - HEALTHCARE & LIFE SCIENCES
    - MANUFACTURING
    - RETAIL & E-COMMERCE
    - TELECOMMUNICATIONS
    - SOFTWARE & TECHNOLOGY PROVIDERS
    - MEDIA & ENTERTAINMENT
    - OTHER END-USE INDUSTRIES
  • 5.8 INVESTMENT AND FUNDING SCENARIO
  • 5.9 CASE STUDY ANALYSIS
    CASE STUDY 1
    CASE STUDY 2
    CASE STUDY 3
    CASE STUDY 4
    CASE STUDY 5
    TECHNOLOGY ANALYSIS
    - KEY TECHNOLOGIES
    - COMPLEMENTARY TECHNOLOGIES
    - ADJACENT TECHNOLOGIES
    TARIFF AND REGULATORY LANDSCAPE
    - TARIFF RELATED TO INDUSTRIAL ROBOTS (HSN: 847950)
    - REGULATORY BODIES, GOVERNMENT AGENCIES AND OTHER ORGANIZATIONS
    - KEY REGULATIONS
    TRADE ANALYSIS
    - EXPORT SCENARIO OF INDUSTRIAL ROBOTS (HSN: 847950)
    - IMPORT SCENARIO OF INDUSTRIAL ROBOTS (HSN: 847950)
    PATENT ANALYSIS
    - METHODOLOGY
    - PATENTS FILED, BY DOCUMENT TYPE, 2016–2025
    - INNOVATION AND PATENT APPLICATIONS
    PRICING ANALYSIS
    - AVERAGE SELLING PRICE OF OFFERING, BY KEY PLAYERS, 2025
    - AVERAGE SELLING PRICE, BY APPLICATION, 2025
    KEY CONFERENCES AND EVENTS, 2025-2026
    PORTER’S FIVE FORCES’ ANALYSIS
    - THREAT FROM NEW ENTRANTS
    - THREAT OF SUBSTITUTES
    - BARGAINING POWER OF SUPPLIERS
    - BARGAINING POWER OF BUYERS
    - INTENSITY OF COMPETITION RIVALRY
    TRENDS/DISRUPTIONS IMPACTING BUYER/CLIENTS OF AGENTIC AI MARKET
    KEY STAKEHOLDERS AND BUYING CRITERIA
    - KEY STAKEHOLDERS IN BUYING PROCESS
    - BUYING CRITERIA
AGENTIC AI MARKET, BY OFFERING
6
  • 6.1 INTRODUCTION
    OFFERING: AGENTIC AI MARKET DRIVERS
  • 6.2 SOFTWARE
    COMPUTATIONAL AGENTS
    ROBOTIC AGENTS
  • 6.3 SERVICES
    DEPLOYMENT & INTEGRATION
    CONSULTING
    SUPPORT & MAINTENANCE
AGENTIC AI MARKET, BY AGENT ROLE
7
  • 7.1 INTRODUCTION
    AGENT ROLE: AGENTIC AI MARKET DRIVERS
  • 7.2 MARKETING
  • 7.3 SALES
  • 7.4 IT SUPPORT & SERVICE MANAGEMENT
  • 7.5 FINANCE & ACCOUNTING
  • 7.6 OPERATIONS AND SUPPLY CHAIN
  • 7.7 OTHERS (R&D, HR)
AGENTIC AI MARKET, BY TECHNOLOGY
8
  • 8.1 INTRODUCTION
    TECHNOLOGY: AGENTIC AI MARKET DRIVERS
  • 8.2 MACHINE LEARNING
  • 8.3 NATURAL LANGUAGE PROCESSING
  • 8.4 COMPUTER VISION
  • 8.5 EMBODIED AI
  • 8.6 GENERATIVE AI
AGENTIC AI MARKET, BY APPLICATION
9
  • 9.1 INTRODUCTION
    APPLICATION: MARKET DRIVERS
  • 9.2 WORKFLOW AUTOMATION
  • 9.3 INSPECTION & MONITORING
  • 9.4 NAVIGATION & MOBILITY
  • 9.5 PLANNING & DECISION SUPPORT
  • 9.6 KNOWLEDGE RETRIEVAL & REASONING
  • 9.7 SIMULATION & VIRTUAL AUTONOMY TRAINING
  • 9.8 ENVIRONMENT PERCEPTION & SCENE UNDERSTANDING
  • 9.9 OTHER APPLICATIONS
AGENTIC AI MARKET, BY END-USER
10
  • 10.1 INTRODUCTION
    END-USER: MARKET DRIVERS
  • 10.2 INDIVIDUAL USERS
  • 10.3 ENTERPRISES
    BFSI
    TELECOMMUNICATIONS
    GOVERNMENT AND DEFENSE
    HEALTHCARE & LIFE SCIENCES
    MANUFACTURING
    MEDIA & ENTERTAINMENT
    RETAIL AND E-COMMERCE
    TECHNOLOGY PROVIDERS
    PROFESSIONAL SERVICE PROVIDERS
    - LAW FIRMS
    - TRANSPORTATION & LOGISTICS
    - ENERGY AND UTILTIES
    - OTHER ENTERPRISES (EDUCATION, TRAVEL & HOSPITALITY, AGRICULTURE, AND CONSTRUCTION & REAL ESTATE)
AGENTIC AI MARKET, BY REGION
11
  • 11.1 INTRODUCTION
  • 11.2 NORTH AMERICA
    NORTH AMERICA: AGENTIC AI MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR NORTH AMERICA
    UNITED STATES
    CANADA
  • 11.3 EUROPE
    EUROPE: AGENTIC AI MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR EUROPE
    UK
    GERMANY
    FRANCE
    ITALY
    SPAIN
    REST OF EUROPE
  • 11.4 ASIA PACIFIC
    ASIA PACIFIC: AGENTIC AI MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
    CHINA
    INDIA
    JAPAN
    SOUTH KOREA
    AUSTRALIA & NEW ZEALAND
    SINGAPORE
    REST OF ASIA PACIFIC
  • 11.5 MDDLE EAST AND AFRICA
    MDDLE EAST AND AFRICA: AGENTIC AI MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR MIDDLE EAST AND AFRICA
    SAUDI ARABIA
    UAE
    QATAR
    SOUTH AFRICA
    REST OF MDDLE EAST AND AFRICA
  • 11.6 LATIN AMERICA
    LATIN AMERICA: AGENTIC AI MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR LATIN AMERICA
    BRAZIL
    MEXICO
    ARGENTINA
    REST OF LATIN AMERICA
COMPETITIVE LANDSCAPE
12
  • 12.1 OVERVIEW
  • 12.2 STRATEGIES ADOPTED BY KEY PLAYERS
  • 12.3 REVENUE ANALYSIS FOR KEY PLAYERS, 2020 - 2024
    MARKET SPECIFIC REVENUE ANALYSIS
  • 12.4 MARKET SHARE ANALYSIS, 2024
    MARKET RANKING ANALYSIS
  • 12.5 PRODUCT COMPARATIVE ANALYSIS
  • 12.6 VALUATION AND FINANCIAL METRICS OF KEY AGENTIC AI VENDORS
  • 12.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
    STARS
    EMERGING LEADERS
    PERVASIVE PLAYERS
    PARTICIPANTS
    COMPANY FOOTPRINT: KEY PLAYERS, 2024
    - COMPANY FOOTPRINT
    - REGION FOOTPRINT
    - OFFERING FOOTPRINT
    - AGENT ROLE FOOTPRINT
    - APPLICATION FOOTPRINT
    - END-USER FOOTPRINT
  • 12.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
    PROGRESSIVE COMPANIES
    RESPONSIVE COMPANIES
    DYNAMIC COMPANIES
    STARTING BLOCKS
    COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
    - DETAILED LIST OF KEY STARTUPS/SMES
    - COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
  • 12.9 COMPETITIVE SCENARIO
    PRODUCT LAUNCHES AND ENHANCEMENTS
    DEALS
    OTHERS
COMPANY PROFILES
13
  • 13.1 INTRODUCTION
  • 13.2 KEY PLAYERS
    OPENAI
    ALPHABET (GOOGLE)
    AWS
    IBM
    NVIDIA
    SALESFORCE
    MICROSOFT
    ORACLE
    LIVEPERSON
    - TEMPUS AI
    - ALPHABET (WAYMO)
    - MOBILEYE
    - UBER
    - DJI
    - BOSTON DYNAMICS
    - SHIELD AI
    - ANDURIL INDUSTRIES
    - AEROVIRONMENT
    - TESLA
  • 13.3 SMES/START-UPS
    AMELIA
    KORE.AI
    LEENA AI
    COGNIGY
    INFINITUS SYSTEMS
    STABILITY AI
    RASA
    AISERA
    CUJO AI
    - LEVEL AI
    - SKYDIO
    - SOFTBANK ROBOTICS
    - ANYBOTICS
    - BADGER TECHNOLOGIES
    - MANUS
    - DEEPROUTE.AI
    - ADEPT
    - NANO NET
    - WAYVE
    - SEEGRID
    - BLUE RIVER TECHNOLOGY
ADJACENT AND RELATED MARKETS
14
  • 14.1 INTRODUCTION
  • 14.2 ARTIFICIAL INTELLIGENCE (AI) MARKET – GLOBAL FORECAST TO 2032
    MARKET DEFINITION
    MARKET OVERVIEW
  • 14.3 AI AGENTS MARKET – GLOBAL FORECAST TO 2032
    MARKET DEFINITION
    MARKET OVERVIEW
APPENDIX
15
  • 15.1 DISCUSSION GUIDE
  • 15.2 KNOWLEDGE STORE: MARKETANDMARKETS’ SUBSCRIPTION PORTAL
  • 15.3 AVAILABLE CUSTOMIZATIONS
  • 15.4 RELATED REPORTS
  • 15.5 AUTHOR DETAILS

 

The global agentic AI market report’s research methodology involved extensive secondary sources, directories, and various reputed open-source databases to identify and collect information useful for this technical and market-oriented study. In-depth interviews were conducted with various primary respondents, including agentic AI software providers, agentic AI service providers, embodied AI providers, individual end users, and enterprise end users; high-level executives of multiple companies offering agentic AI solutions; and industry consultants to obtain and verify critical qualitative and quantitative information and assess the market prospects and industry trends.

Secondary Research

Several secondary sources were referred to identify and gather information for the study during the secondary research process. These included annual reports; press releases and investor presentations of companies; white papers; certified publications such as Journal of Artificial Intelligence Research (JAIR), Transactions of the Association for Computational Linguistics (TACL), Journal of Machine Learning Research (JMLR), IEEE Transactions on Neural Networks and Learning Systems, Nature Machine Intelligence, Artificial Intelligence Journal (AIJ), ACM Transactions on Information Systems (TOIS), Pattern Recognition Journal, and Neural Computation (MIT Press); and articles from recognized associations and government publishing sources including but not limited to Association for Computational Linguistics (ACL), International Association for Machine Learning (IAMLE), Artificial Intelligence Industry Association (AIIA), International Speech Communication Association (ISCA), Natural Language Processing Association (NLPA), Machine Learning and AI Industry Research Association (MLAIRA), and AI Infrastructure Alliance (AIIA).

The secondary research was used to obtain key information about the industry’s value chain, the market’s monetary chain, the overall pool of key players, market classification and segmentation according to industry trends to the bottom-most level, regional markets, and key developments from the market and technology-oriented perspectives.

Primary Research

In the primary research process, diverse stakeholders from the supply and demand sides of the agentic AI ecosystem were interviewed to gather qualitative and quantitative insights specific to this market. From the supply side, key industry experts, such as chief executive officers (CEOs), vice presidents (VPs), marketing directors, technology & innovation directors, and technical leads from vendors offering agentic AI software & services, were consulted. Additionally, system integrators, service providers, and IT service firms that implement and support agentic AI were included in the study. Input was gathered from IT decision-makers, infrastructure managers, and business leaders of major enterprise end users to grasp user perspectives and adoption challenges across targeted industries.

The primary research ensured that all crucial parameters affecting the agentic AI market, from technological advancements and evolving use cases (workflow automation, inspection & monitoring, navigation & mobility, planning & decision support, etc.) to regulatory and compliance needs (GDPR, CCPA, Europe AI Act, AIDA, etc.), were considered. Each factor was thoroughly analyzed, verified through primary research, and evaluated to obtain precise quantitative and qualitative data for this market.

Once the initial market engineering phase was completed, including detailed calculations for market statistics, segment-specific growth forecasts, and data triangulation, an additional round of primary research was undertaken. This step was crucial for refining and validating critical data points, such as agentic AI offerings (software & services), industry adoption trends, the competitive landscape, and key market dynamics such as demand drivers (rapid adoption of LLM orchestration frameworks accelerating autonomous agent deployment, advances in edge AI and neuromorphic chips enabling real-time autonomy in physical systems, rising industrial use of autonomous robotics boosting efficiency in logistics, agriculture, and field services, and growing demand for cost-effective, continuous operations driving autonomous system investments), challenges (fragmented autonomy stacks and missing interoperability standards restricting system integration, legal and ethical gaps around autonomous actions delaying adoption in regulated sectors), and opportunities (autonomous agents scaling across BFSI, telecom, and manufacturing for digital transformation, expanding human-machine collaboration via agent-augmented collaborative robots [cobots], and emerging AI regulations unlocking new markets for compliant autonomy).

In the complete market engineering process, the top-down and bottom-up approaches and several data triangulation methods were extensively used to perform the market estimation and market forecast for the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analysis was performed on the complete market engineering process to record the critical information/insights throughout the report.

Agentic AI Market Size, and Share

Note: Three tiers of companies are defined based on their total revenue as of 2024; tier 1 = revenue more than USD 500
million, tier 2 = revenue between USD 500 million and 100 million, tier 3 = revenue less than USD 100 million
Source: MarketsandMarkets Analysis

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

The top-down and bottom-up approaches were employed to estimate and forecast the agentic AI market and its dependent submarkets. This multi-layered analysis was further reinforced through data triangulation, incorporating primary and secondary research inputs. The market figures were also validated against the existing MarketsandMarkets repository for accuracy.

Agentic AI Market : Top-Down and Bottom-Up Approach

Agentic AI Market Top Down and Bottom Up Approach

Data Triangulation

After determining the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.

Market Definition

Agentic AI refers to autonomous systems that can perceive context, make decisions, and execute multi-step actions to achieve defined goals, often with minimal human intervention. These agents operate across digital and physical environments, guided by software capable of planning, tool use, adaptive reasoning, and collaboration with other agents or systems. Whether applied in virtual workflows or embedded within embodied systems such as vehicles or robotics, the emphasis is on the agentic AI software orchestration layer that enables situational awareness, autonomy, and dynamic behavior across complex, real-world tasks.

Stakeholders

  • Agentic AI software developers
  • Agentic AI infrastructure providers
  • Agentic AI integrated service providers
  • Agentic AI training dataset providers
  • Core data service providers
  • Business analysts
  • Cloud service providers
  • Consulting service providers
  • Enterprise end users
  • Distributors and value-added resellers (VARs)
  • Government agencies
  • Independent software vendors (ISV)
  • Managed service providers
  • Market research and consulting firms
  • Support & maintenance service providers
  • System integrators (SIs)/migration service providers
  • Language service providers
  • Technology providers
  • Academia & research institutions
  • Investors & venture capital firms

Report Objectives

  • To define, describe, and forecast the agentic AI market, by offering (software and services), agent role, technology, application, and end user
  • To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing market growth
  • To analyze the micro markets concerning individual growth trends, prospects, and their contribution to the total market
  • To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the agentic AI market
  • To assess market opportunities and provide details of the competitive landscape for stakeholders and market leaders
  • To forecast the market size of segments for North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
  • To profile the key players and comprehensively analyze their market ranking and core competencies
  • To analyze competitive developments, such as partnerships, product launches, and mergers & acquisitions, in the agentic AI market
  • To analyze the impact of recession in the agentic AI market across all regions

Available Customizations

With the given market data, MarketsandMarkets offers customizations to meet the company’s specific needs.

Product Analysis

  • Product matrix provides a detailed comparison of the product portfolio of each company.

Geographic Analysis as per Feasibility

  • Further breakup of the North American market for Agentic AI
  • Further breakup of the European market for Agentic AI
  • Further breakup of the Asia Pacific market for Agentic AI
  • Further breakup of the Middle Eastern & African market for Agentic AI
  • Further breakup of the Latin American market for Agentic AI

Company Information

  • Detailed analysis and profiling of additional market players (up to five)

Previous Versions of this Report

Agentic AI Market by Software (Computational Agents, Robotic Agents), Agent Role (Marketing, Sales, Operations & Supply Chain, ITSM), Application (Workflow Automation, Planning & Decision Support, Navigation & Mobility) - Global Forecast to 2032

Report Code TC 8694
Published in Jun, 2023, By MarketsandMarkets™
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Growth opportunities and latent adjacency in Agentic AI Market

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