에이전틱 AI 시장 : 구성요소별, 유형별, 전개 방식별, 용도별, 최종 이용 업계별, 기업 규모별 - 세계 예측(2025-2030년)

The Agentic AI Market was valued at USD 5.29 billion in 2024 and is projected to grow to USD 6.65 billion in 2025, with a CAGR of 27.12%, reaching USD 22.35 billion by 2030.

Exploring the Emergence of Agentic AI in Modern Enterprises and Its Potential to Transform Strategic Decision Making and Operational Efficiency

Agentic artificial intelligence represents a paradigm shift that transcends the boundaries of conventional automation by granting systems the autonomy to initiate actions without direct human instruction. This emerging category of AI manifests through self-directed adaptive learning and decision-making capabilities that can optimize complex operational workflows, enhancing both efficiency and strategic foresight within an organization. As more enterprises recognize the competitive advantage of deploying agents that can continuously refine their behavior based on data-driven insights, it becomes critical to understand the technological, regulatory, and organizational factors shaping this transformation.

In this executive summary, we introduce the complex interplay between software solutions and service offerings, where comprehensive service portfolios span managed and professional services while software applications drive intelligent process automation. We distinguish between agentic AI implementations categorized as hybrid agents combining human oversight with algorithmic autonomy, proactive agents anticipating outcomes before they occur, and reactive agents responding dynamically to real-time inputs. Deployment considerations range from versatile cloud environments-including both private and public cloud architectures-to secure on-premises installations. Application domains extend from content generation and customer engagement enhancements to advanced decision support leveraging predictive and prescriptive analytics, and even video analytics for real-time visual intelligence. Industry adoption encompasses traditional banking, capital markets, and insurance sectors alongside healthcare providers and retail enterprises, with differentiated strategies for large enterprises and small to medium enterprises.

By framing agentic AI within these multifaceted dimensions, this report lays a robust foundation for understanding the strategic imperatives and transformative potential poised to reshape technology road maps and business models across every sector.

Identifying the Transformative Shifts Reshaping the Global AI Landscape and Driving the Next Wave of Technological Innovation and Value Creation

The landscape of artificial intelligence is undergoing transformative shifts driven by a convergence of technological breakthroughs, evolving regulatory frameworks, and new business imperatives. Over the last two years, improvements in natural language understanding and reinforcement learning algorithms have accelerated the transition from rule-based automation to truly agentic capabilities that self-optimize against objectives. Meanwhile, the adoption of low-code development platforms has empowered organizations to prototype and deploy intelligent agents more rapidly, reducing the traditional barriers between data scientists and operational teams.

Furthermore, as data privacy regulations mature and cloud sovereignty initiatives gain traction, technology leaders are rethinking their architectures to ensure compliance while preserving agility. This dynamic interplay between regulatory pressure and innovation has prompted an increase in hybrid cloud strategies that balance control with scalability. Simultaneously, the integration of agentic AI with Internet of Things networks and edge computing platforms is redefining how real-time analytics inform decision-making at scale, unlocking opportunities for predictive maintenance in manufacturing and autonomous routing in logistics.

Consequently, enterprises are adopting new governance models for autonomous systems, establishing ethical guardrails and risk management protocols to ensure transparency and accountability. These evolving paradigms are not only reshaping operational workflows but also influencing talent strategies, as organizations seek professionals adept at orchestrating cross-functional teams capable of marrying AI capabilities with domain expertise. In this environment, the interplay of technological advancements, regulatory shifts, and organizational adaptation forms the crucible in which agentic AI will prove its strategic value.

Assessing the Cumulative Impact of United States Tariff Adjustments in 2025 on Agentic AI Ecosystems and Cross Border Technology Supply Chains

The announcement of updated United States tariffs in 2025 introduces a critical variable for stakeholders in the agentic AI ecosystem. By targeting a broad array of semiconductors, advanced sensors, and certain software licensing agreements, these measures have created cost pressures across hardware procurement and subscription models. Consequently, technology buyers are reevaluating supply chain configurations, with some opting to regionalize component sourcing to mitigate exposure to tariff volatility.

In practice, multinational corporations have responded by establishing strategic partnerships with regional contract manufacturers and data center operators to secure preferential procurement channels. At the same time, cloud service providers have adjusted their expansion plans by selectively investing in data center capacity within regions subject to lower duties. This realignment aims to shelter enterprises from border-tax fluctuations while preserving the performance and reliability demands of agentic AI workloads.

Moreover, the ripple effect of tariff policy has accelerated discussions around on-premises deployments versus cloud migrations. Organizations with existing infrastructure now face a cost-benefit analysis that weighs the upfront investment in localized hardware against the subscription dynamics of third-party platforms subject to tariff-inflated pricing. Investors and technology leaders are closely monitoring these developments, recognizing that supply chain resilience and pricing predictability will become defining factors in vendor selection and deployment strategy over the next three years.

Ultimately, the cumulative impact of United States tariff adjustments in 2025 underscores the importance of agile procurement strategies and diversified supplier ecosystems as organizations navigate an increasingly complex international trade environment.

Deriving Actionable Insights from Core Market Segmentation to Illuminate Key Drivers in Services Component Deployment Modes and Application Domains

A nuanced understanding of market segmentation reveals the underlying dynamics that drive adoption and value creation in the agentic AI domain. Component analysis distinguishes between fully integrated software platforms-enabling advanced algorithmic orchestration-and comprehensive service bundles that include both managed services for ongoing optimization and professional services for system design and customization. This dual focus on software and services ensures that organizations can access end-to-end solutions tailored to their maturity level.

Simultaneously, agentic AI offerings diverge into three primary types: hybrid agents that seamlessly blend human decision oversight with autonomous execution; proactive agents that analyze historical and real-time data to anticipate outcomes before they transpire; and reactive agents that excel in dynamic environments by responding instantly to new inputs. Each agent type appeals to different operational profiles, with hybrid models often favored by risk-sensitive industries and proactive designs adopted by sectors prioritizing predictive accuracy.

Deployment mode is equally critical, spanning public and private cloud infrastructures as well as traditional on-premises installations. The choice between these environments reflects an organization's tolerance for data sovereignty considerations, performance requirements, and integration complexity. In many cases, private cloud configurations serve as a middle ground, delivering enhanced governance controls without sacrificing scalability.

When it comes to applications, the spectrum ranges from automated content generation that streamlines marketing workflows to sophisticated customer service virtual assistants. Decision support systems further subdivide into predictive analytics that identify future trends and prescriptive analytics that recommend concrete actions. Meanwhile, video analytics platforms leverage computer vision to deliver real-time operational insights.

Lastly, end user industries span the banking, capital markets, and insurance segments where risk management prevails; healthcare environments focused on patient outcome optimization; and retail contexts aiming to personalize consumer experiences. These solutions cater to both large enterprise deployments and the agile needs of small and medium enterprises, highlighting the breadth of market opportunity and the necessity of tailored go-to-market approaches.

Uncovering Regional Dynamics and Growth Patterns across the Americas, Europe Middle East Africa, and Asia Pacific with Deep Market Penetration Analysis

Regional dynamics play a pivotal role in shaping the trajectory of agentic AI adoption, as each geography presents distinct regulatory, economic, and technological factors. In the Americas, widespread cloud infrastructure investments coupled with established innovation ecosystems drive early adoption. Organizations on this continent benefit from supportive tax credits and a robust startup landscape that accelerates proof-of-concept initiatives, particularly within North America's leading research universities and technology hubs.

Europe, the Middle East, and Africa exhibit a different yet complementary pattern. In Europe, stringent data protection regulations have catalyzed the development of advanced privacy-preserving AI techniques, leading enterprises to integrate federated learning and next-generation encryption protocols. The Middle East is experiencing rapid digital transformation fueled by sovereign wealth funds investing in smart city initiatives, while Africa demonstrates a leapfrogging phenomenon through mobile-first deployments and regional innovation clusters that address unique infrastructure challenges.

Across the Asia Pacific region, a blend of mature markets and emerging economies defines the competitive landscape. Nations such as Japan and South Korea drive hardware innovation with established semiconductor industries, whereas China's accelerated rollout of 5G networks underpins large-scale agentic AI trials in manufacturing and logistics. India's burgeoning technology services sector focuses on customizing agentic solutions for enterprise resource planning and customer service automation.

Collectively, these three regional segments underscore the importance of tailored market entry strategies. By aligning with local regulatory frameworks, infrastructure capabilities, and innovation priorities, organizations can maximize uptake and realize the full potential of autonomous AI technologies across diverse economic environments.

Examining Leading Industry Players Navigating the Agentic AI Revolution and Their Strategies for Competitive Advantage and Market Differentiation

Understanding the competitive landscape requires profiling the key organizations that are shaping the agentic AI arena through strategic investments, partnerships, and technology advancements. Global technology conglomerates with integrated cloud platforms continue to expand their AI service portfolios by embedding autonomous agents into workflow orchestration tools. These enterprises leverage extensive developer communities and robust compliance frameworks to cater to sectors with stringent regulatory requirements.

At the same time, specialized system integrators differentiate themselves by offering vertical-focused implementations that combine domain expertise with tailored agentic AI modules. Their ability to deliver turnkey solutions has made them preferred partners for industries such as healthcare and financial services, where stringent performance and security demands prevail. Emerging pure-play AI startups further enrich the ecosystem by pioneering novel agentic algorithms and contributing open source libraries that accelerate community innovation.

Collaborative ecosystems are another defining feature of this market. Industry consortia and research partnerships between academia and commercial providers drive proof-of-concept demonstrations and establish interoperability standards for agentic systems. By engaging in joint sandbox environments and standards bodies, leading companies ensure that their solutions can integrate seamlessly within multi-vendor architectures.

As competitive pressures intensify, we observe a growing trend of strategic alliances between technology vendors and managed service providers. This model enables continuous optimization of deployed agents through iterative feedback loops and deep domain knowledge infusion. Organizations seeking to navigate the evolving vendor landscape should prioritize partners with proven capabilities in scaling agentic deployments and a track record of fostering open collaboration across diverse stakeholder groups.

Formulating Actionable Recommendations to Empower Industry Leaders in Harnessing Agentic AI for Sustainable Growth Operational Excellence and Strategic Agility

To harness the transformative potential of agentic AI, industry leaders must adopt a structured approach that balances innovation with governance. Establishing an AI center of excellence can centralize strategic oversight, ensuring that autonomous agents align with corporate objectives and ethical standards. This governance body should define clear performance metrics, risk management protocols, and transparency requirements that inform continuous improvement cycles.

Concurrently, organizations should invest in upskilling programs that cultivate cross-disciplinary talent capable of designing, deploying, and maintaining agentic systems. By blending data science, software engineering, and domain expertise, teams can iterate more rapidly and deliver solutions that address nuanced operational challenges. In parallel, engaging with academic institutions and industry alliances accelerates knowledge transfer and fosters a culture of experimentation.

From an infrastructure perspective, adopting a hybrid deployment strategy that combines private cloud environments for sensitive workloads with public cloud scalability can optimize both cost and compliance. This dual-track approach enables organizations to allocate resources dynamically, responding swiftly to changes in demand while preserving control over critical data assets.

Finally, forging partnerships with ecosystem players-including sensor manufacturers, edge computing providers, and cybersecurity specialists-ensures that agentic AI deployments remain resilient and interoperable. Through collaborative innovation, firms can co-create reference architectures and integrate best-in-class components, thereby minimizing integration risks and accelerating time to value.

Detailing a Rigorous Multi Stage Research Methodology Integrating Quantitative Data, Qualitative Insights, and Expert Validation to Ensure Analytical Integrity

This analysis rests on a rigorous multi-stage research methodology designed to ensure both analytical integrity and practical relevance. The initial phase comprised an extensive review of academic journals, patent filings, and open source repositories to map the evolution of agentic AI algorithms and architectures. During this secondary research stage, we cataloged hundreds of relevant technical papers and white papers published over the past five years.

Simultaneously, we conducted a series of in-depth interviews with C-level executives, technology leaders, and domain experts across multiple industries, including financial services, healthcare, and retail. These conversations yielded qualitative insights into adoption drivers, integration challenges, and organizational readiness factors. To complement these findings, we deployed structured surveys targeting IT decision makers to quantify priorities related to governance, deployment mode preferences, and application areas.

Subsequent data triangulation ensured that our segmentation analysis and regional insights were grounded in both empirical evidence and practitioner experience. We applied advanced analytics techniques to identify correlations between technology maturity levels and deployment outcomes. Finally, all preliminary conclusions underwent validation through expert workshops, where stakeholders reviewed our interpretations and provided feedback to refine the final framework.

By combining quantitative rigor with qualitative depth, this methodology delivers a comprehensive perspective on the agentic AI market that supports strategic decision making.

Synthesizing Core Findings from the Agentic AI Market Study to Highlight Strategic Imperatives, Emerging Opportunities, and Risk Mitigation Considerations

The study of agentic artificial intelligence reveals a technology agenda defined by autonomous decision making, adaptability, and strategic integration across functions. From the influence of new tariff regimes to the intricacies of segmentation across components, deployment modes, applications, and end user industries, a clear narrative emerges: organizations that proactively embrace agentic capabilities will gain differentiated operational agility and competitive resilience.

Regional analysis underscores the importance of localizing both technology and strategy to align with diverse regulatory, infrastructural, and cultural contexts. Equally, profiling leading vendors illustrates that success hinges on ecosystem partnerships and a balanced mix of service excellence and software innovation. Actionable recommendations highlight the need for robust governance frameworks, targeted talent development, and hybrid deployment architectures that optimize cost and performance.

As enterprises stand at the threshold of widespread agentic AI adoption, they must prioritize iterative experimentation, cross-functional collaboration, and transparent risk management protocols. By doing so, they can transform how decisions are made, processes are executed, and value is created. The insights presented in this report equip senior leaders with the strategic clarity required to navigate the complexities of an autonomous future.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

• 4.1. Introduction
• 4.2. Market Sizing & Forecasting

5. Market Dynamics

• 5.1. Enterprise adoption of agentic AI assistants to automate cross-functional workflows
• 5.2. Emergence of self-governing agentic AI models for dynamic real-time decision-making processes
• 5.3. Integration of agentic AI with IoT networks to enable autonomous industrial asset monitoring
• 5.4. Regulatory frameworks evolving to address ethical concerns in agentic AI-driven autonomous systems
• 5.5. Advancements in explainable agentic AI algorithms to enhance transparency in automated operations
• 5.6. Growth of agentic AI-enabled robotic process automation in financial services risk management
• 5.7. Emergence of agentic AI platforms enabling cross functional collaboration and autonomous project management
• 5.8. Innovations in continuous learning mechanisms for adaptive agentic AI in dynamic environments

6. Market Insights

• 6.1. Porter's Five Forces Analysis
• 6.2. PESTLE Analysis

7. Cumulative Impact of United States Tariffs 2025

8. Agentic AI Market, by Component

• 8.1. Introduction
• 8.2. Services
  • 8.2.1. Managed Services
  • 8.2.2. Professional Services
• 8.3. Software

9. Agentic AI Market, by Type

• 9.1. Introduction
• 9.2. Hybrid Agents
• 9.3. Proactive Agents
• 9.4. Reactive Agents

10. Agentic AI Market, by Deployment Mode

• 10.1. Introduction
• 10.2. Cloud
  • 10.2.1. Private Cloud
  • 10.2.2. Public Cloud
• 10.3. On Premises

11. Agentic AI Market, by Application

• 11.1. Introduction
• 11.2. Content Generation
• 11.3. Customer Service
• 11.4. Decision Support
  • 11.4.1. Predictive Analytics
  • 11.4.2. Prescriptive Analytics
• 11.5. Video Analytics

12. Agentic AI Market, by End User Industry

• 12.1. Introduction
• 12.2. Banking, Financial Services, and Insurance
  • 12.2.1. Banking
  • 12.2.2. Capital Markets
  • 12.2.3. Insurance
• 12.3. Healthcare
• 12.4. Retail

13. Agentic AI Market, by Enterprise Size

• 13.1. Introduction
• 13.2. Large Enterprises
• 13.3. Small & Medium Enterprises

14. Americas Agentic AI Market

• 14.1. Introduction
• 14.2. United States
• 14.3. Canada
• 14.4. Mexico
• 14.5. Brazil
• 14.6. Argentina

15. Europe, Middle East & Africa Agentic AI Market

• 15.1. Introduction
• 15.2. United Kingdom
• 15.3. Germany
• 15.4. France
• 15.5. Russia
• 15.6. Italy
• 15.7. Spain
• 15.8. United Arab Emirates
• 15.9. Saudi Arabia
• 15.10. South Africa
• 15.11. Denmark
• 15.12. Netherlands
• 15.13. Qatar
• 15.14. Finland
• 15.15. Sweden
• 15.16. Nigeria
• 15.17. Egypt
• 15.18. Turkey
• 15.19. Israel
• 15.20. Norway
• 15.21. Poland
• 15.22. Switzerland

16. Asia-Pacific Agentic AI Market

• 16.1. Introduction
• 16.2. China
• 16.3. India
• 16.4. Japan
• 16.5. Australia
• 16.6. South Korea
• 16.7. Indonesia
• 16.8. Thailand
• 16.9. Philippines
• 16.10. Malaysia
• 16.11. Singapore
• 16.12. Vietnam
• 16.13. Taiwan

17. Competitive Landscape

• 17.1. Market Share Analysis, 2024
• 17.2. FPNV Positioning Matrix, 2024
• 17.3. Competitive Analysis
  • 17.3.1. Accenture PLC
  • 17.3.2. Adept AI Labs Inc.
  • 17.3.3. Agentico Ltd.
  • 17.3.4. Amazon Web Services, Inc.
  • 17.3.5. C3.ai, Inc.
  • 17.3.6. Clarion Analytics
  • 17.3.7. Clarion Technologies
  • 17.3.8. Cognizant Technology Solutions Corporation
  • 17.3.9. Coupa Software Incorporated
  • 17.3.10. Endava plc
  • 17.3.11. Google LLC by Alphabet Inc.
  • 17.3.12. International Business Machines Corporation
  • 17.3.13. Microsoft Corporation
  • 17.3.14. NVIDIA Corporation
  • 17.3.15. Oracle Corporation
  • 17.3.16. Put It Forward Inc.
  • 17.3.17. qBotica Inc.
  • 17.3.18. Salesforce, Inc.
  • 17.3.19. Siemens AG
  • 17.3.20. UiPath, Inc.
  • 17.3.21. Zycus Inc.
  • 17.3.22. ServiceNow, Inc.

18. ResearchAI

19. ResearchStatistics

20. ResearchContacts

21. ResearchArticles

22. Appendix