The global agentic AI market would increase from an expected USD 13.81 billion in 2025 to USD 140.80 billion by 2032 at a compound annual growth rate (CAGR) of 39.3%. Self-evaluation modules that allow agents to evaluate their reasoning, try unsuccessful tasks again, or edit less-than-ideal outputs are important factors propelling the agentic AI market. Further pushing the limits of automation across companies are innovative frameworks like AutoGen, CrewAI, and LangGraph, which enable decentralized decision-making, role-based delegation, and dynamic task allocation. Agentic systems are also being used to handle enterprise SaaS platforms like SAP, Jira, Notion, and Salesforce.
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The agentic AI market is transforming rapidly as enterprises move beyond traditional automation toward goal-directed, adaptive systems capable of reasoning, planning, and decision-making. One of the most significant trends is the rise of orchestration frameworks that allow developers to build multi-agent systems with memory, tool use, and long-horizon task execution. These frameworks enable organizations to implement agents that work across APIs, structured data, and unstructured inputs to deliver end-to-end autonomy in workflows such as IT operations, financial processing, and customer service.
Another key dynamic is the convergence of generative AI and agentic architectures. Large language models are now being enhanced with planning and execution layers, giving rise to agents that can generate content and operate within enterprise systems. Meanwhile, physical deployments of embodied agents are gaining traction in logistics, healthcare, and field operations, fueled by advancements in real-time perception and edge-computing AI. Enterprises also prioritize agent observability, governance, and safety as these systems become more autonomous. With a growing demand for verticalized, domain-specific agents and increasing availability of agent-as-a-service platforms, the market is shifting toward scalable, composable ecosystems.
Strong enterprise demand for autonomous incident resolution has pushed IT support & service management to be the largest segment by agent role in 2025
IT support & service management will be the largest segment by agent role in 2025 due to the high volume, repeatability, and complexity of tasks well-suited for autonomous handling. Enterprises increasingly deploy agentic AI to automate incident triage, ticket routing, root cause analysis, and infrastructure provisioning—functions that traditionally require large support teams and structured workflows. These environments provide clean access to historical logs, standardized APIs, and well-defined KPIs, making them ideal for multi-agent orchestration and reasoning. Agentic systems in this domain reduce mean time to resolution, improve service uptime, and lower operational overhead, delivering clear and measurable ROI.
Additionally, major IT platforms are now integrating agentic capabilities directly into their ecosystems, allowing for plug-and-play deployments with enterprise-grade governance and observability. As organizations scale digital infrastructure and hybrid cloud operations, the demand for intelligent agents that can autonomously manage, remediate, and optimize IT services is accelerating. Many early adopters have already shifted to agent-first operating models in ITSM, creating a proven pathway for other sectors to follow. This widespread applicability, paired with enterprise urgency around operational efficiency, positions IT support and service management as the leading agent role segment by market share.
Professional service providers to register the fastest growth rate during the forecast period, driven by rising agentic AI deployment across research, analysis, and client interaction.
Over the forecast period, professional service providers are expected to be the fastest-growing end users of agentic AI, driven by the sector’s dependence on high-value, knowledge-intensive tasks that can benefit significantly from intelligent automation. The legal, consulting, auditing, and financial advisory industries involve extensive research, document generation, analysis, and client communication—areas where agentic AI can deliver substantial productivity gains. These agents are used to prepare case summaries, conduct compliance checks, analyze large datasets, and even manage project workflows by interacting with internal systems and external APIs. Unlike traditional automation, agentic AI can understand context, make decisions, and adapt its behavior dynamically, making it well-suited for the nuanced and client-specific nature of professional services. The segment rapidly grows due to rising client expectations for speed, personalization, and value. Firms using agentic systems can deliver faster turnaround, 24/7 availability, and scalable solutions without increasing staff. This evolution improves service quality and creates new monetization opportunities, making professional services a high-growth sector in the agentic AI market.
Asia Pacific is set to become the fastest-growing region over the forecast period, fueled by localized innovation, and accelerating agentic AI demand across finance and public services.
Asia Pacific is projected to be the fastest-growing region in the agentic AI market due to strong government backing, rapid digital infrastructure expansion, and rising enterprise automation demand across diverse industries. China, India, South Korea, Japan, and Singapore actively invest in AI through national programs focused on smart cities, manufacturing modernization, and AI-led public service delivery. These initiatives drive large-scale adoption of agentic systems in logistics, transportation, healthcare, and governance. The region also benefits from a highly competitive technology ecosystem where startups and large enterprises build localized agentic solutions that accommodate regional languages, regulatory requirements, and infrastructure constraints.
In addition, the growing adoption of cloud-native platforms and the proliferation of mobile-first enterprise workflows are creating a favorable environment for deploying both computational and robotic agents on a scale. Retail, banking, and telecom use agentic AI for intelligent customer engagement, automated onboarding, and supply chain optimization. The cost sensitivity of many Asian markets is also accelerating the adoption of autonomous agents as businesses seek to scale operations without proportional increases in labor costs.
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Unique Features in the Agentic AI Market
Agentic AI systems are uniquely defined by their ability to make decisions independently based on predefined goals or evolving contexts. Unlike traditional AI systems that rely on explicit instructions or narrow tasks, agentic AI agents can assess environments, adapt strategies, and execute actions without continuous human input. This autonomy enables applications ranging from intelligent virtual assistants to robotics and enterprise process automation.
A standout feature of agentic AI is its capacity for setting and pursuing goals dynamically. These systems operate with a sense of purpose—initiating actions, prioritizing tasks, and monitoring outcomes. They are not just reactive but proactive, making them ideal for complex and evolving scenarios such as supply chain optimization, customer service, or scientific research automation.
Agentic AI utilizes real-time data and contextual signals to continuously refine its behavior. With embedded reinforcement learning or other adaptive algorithms, these agents improve over time by learning from outcomes. This adaptability empowers them to handle novel or unexpected situations, making them highly effective in domains like cybersecurity, personalized education, or financial advising.
The agentic AI market emphasizes collaborative intelligence, where multiple agents can work together or negotiate in shared environments. Whether in smart grids, traffic systems, or digital ecosystems, these agents coordinate with each other, manage dependencies, and collectively achieve larger system-wide goals—offering a level of scalability and interactivity not found in standard AI tools.
Major Highlights of the Agentic AI Market
The agentic AI market is witnessing accelerated growth as enterprises and industries seek intelligent systems that go beyond traditional automation. The rising demand for AI solutions capable of autonomous goal-setting, proactive task execution, and dynamic adaptation is propelling adoption across sectors such as finance, healthcare, logistics, and defense.
Agentic AI is increasingly being integrated with cutting-edge technologies like generative AI, edge computing, IoT, and digital twins. These integrations enhance agentic systems’ capabilities to perceive, analyze, and act in real time, creating synergies that improve decision-making, reduce latency, and expand application possibilities in smart environments and industrial automation.
There is growing momentum behind the development of multi-agent systems that enable distributed intelligence. These collaborative agent ecosystems are being used in applications like traffic management, disaster response, and supply chain optimization—where multiple intelligent agents interact, negotiate, and operate toward shared or conflicting objectives efficiently.
The market is seeing significant investment from both major technology firms and AI-focused startups. Companies like OpenAI, Google DeepMind, Microsoft, and Anthropic are exploring agentic frameworks to power next-gen applications, while startups are innovating in vertical-specific agents such as healthcare copilots, autonomous research bots, and workflow assistants.
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Top Companies in the Agentic AI Market
Some leading players in the agentic AI market include IBM (US), NVIDIA (US), OpenAI (US), Oracle (US), Microsoft (US), Google (US), AWS (US), Salesforce (US), LivePerson (US), Waymo (US), Tempus Al (US), Mobileye (Israel), Uber (US), DJI (China), Boston Dynamics (US), Shield Al (US), Anduril Industries (US), AeroVironment (US), and Tesla (US). These players have adopted various organic and inorganic growth strategies, such as collaborating with cloud providers, chipmakers, consulting firms, and startups to co-develop solutions or scale distribution and introducing usage-based, per-user, or consumption-based pricing models to lower entry barriers for SMEs and developers to expand their presence in the agentic AI market.
IBM
IBM strategically invests in agentic AI through its Watsonx platform, which integrates foundational models with orchestration tools for enterprise-grade autonomy. The company emphasizes trust, transparency, and governance as core competencies, providing agentic capabilities that align with regulatory and compliance standards. IBM’s agentic AI systems leverage granite models, integrate with AI factsheets, and support long-context reasoning with retrieval-augmented generation (RAG).
IBM is developing task-aware agents for dynamic tool use, adaptive planning, and multi-modal integration in banking, insurance, and supply chains. Through partnerships with NASA, SAP, and Hugging Face, IBM has focused on open governance and extensible frameworks. Recent initiatives include agent-based customer support and intelligent automation pilots in the telecom and government sectors. IBM’s enterprise-grade AI orchestration, strong model governance stack, and integration with hybrid cloud (Red Hat OpenShift) position it well in regulated industries looking for compliant agentic AI deployments.
Oracle
Oracle’s agentic AI efforts are anchored in its OCI AI agents offering, enabling autonomous decision-making across Oracle Cloud infrastructure and fusion applications. The company’s core strength lies in embedding intelligent agents directly within ERP, HCM, and SCM systems, thus enabling task automation, self-service analytics, and predictive workflows. Oracle’s strategy focuses on integrating agentic orchestration at the data layer by using its proprietary LLMs, integrating with MySQL HeatWave, and employing retrieval augmented generation from structured enterprise databases. Oracle’s AI agents support dynamic task decomposition and can perform planning and tool execution across finance, HR, and operations workflows.
The company also integrates conversational AI and digital assistants into its agentic stack to streamline user interaction. Recent deal activities include joint innovation with NVIDIA and Cohere for model hosting and optimization. Oracle’s verticalized approach, high data affinity, and native integration with enterprise systems provide a strong platform for scalable Agentic AI deployments across finance, healthcare, and manufacturing.
NVIDIA
NVIDIA, a leading U.S. technology company, is deeply invested in the burgeoning agentic AI market the next frontier beyond generative AI by providing a full-stack ecosystem of hardware, software, and blueprints that empower enterprises to build autonomous AI agents equipped for reasoning, planning, and task execution. Its NVIDIA AI Enterprise suite (which includes NeMo, NIM microservices, DGX Cloud) and Blueprints offer developers reference architectures ranging from PDF-to-audio agents to video summarizers while the open-source AgentIQ toolkit enables orchestration across agent frameworks. Partnering with firms like ServiceNow, EY, Accenture, and CrowdStrike, NVIDIA is embedding agentic AI in enterprise workflows from financial compliance and IT automation to security triage underpinned by its GPU dominance and performance-chasing chip roadmap.
OpenAI
OpenAI is at the forefront of the agentic AI market, developing both foundational tools and consumer-facing agents that can autonomously perform multi-step tasks. In early 2025, it launched Operator, a browser-controlling agent in research preview that can navigate sites, fill forms, and even place orders, marking its major entry into autonomous assistants. Simultaneously, OpenAI has rolled out a suite of developer tools including the Responses API, Agents SDK, Model Context Protocol support, web/file search, and computer-use capabilities enabling businesses to easily build, orchestrate, and monitor custom AI agents for use cases like research, customer service, and IT automation. It’s also pursuing patents for “generative customized AI models,” indicating strategic IP development around agent customization and governance. These initiatives position OpenAI to lead as enterprises increasingly adopt autonomous AI workflows.
Microsoft
Microsoft, a U.S. tech powerhouse, is at the forefront of the agentic AI market with its Copilot Studio, Azure AI Foundry, Microsoft 365 Copilot, Security Copilot, and GitHub Copilot platforms enabling organizations to build, orchestrate, and deploy autonomous AI agents across business, security, and development workflows. These agents can proactively address tasks like sales lead qualification, employee onboarding, cybersecurity triage, and code refactoring all using no‑code/low‑code interfaces, robust governance frameworks (through Azure AI Foundry, Entra Agent ID, and Purview), and multi‑agent orchestration with support for standards like A2A and Model Context Protocol. Its strategy also includes consumer-facing AI agents in Windows 11 and industry-specific solutions (e.g., healthcare, finance, supply chain), emphasizing proactive assistance embedded within UI and workflows, positioning Microsoft as a leader in transitioning from chat-based AI to an ecosystem of autonomous software agents.
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