U.S. Agentic AI Market

Market Definition

The agentic AI market refers to the ecosystem of AI systems that autonomously plan and execute tasks with minimal human intervention. The market is transitioning from the adoption of simple automation to autonomous systems that manage complex workflows within defined security and governance boundaries. This empowers organizations to automate repetitive and time-consuming tasks leading to enhanced efficiency and operability. 

Agentic AI Market Overview

The U.S. agentic AI market size was valued at USD 6.20 billion in 2025 and is projected to reach USD 145.71 billion by 2033, representing a CAGR of 49.16% over the forecast period. Demand for automated platforms offering decision-making, workflow orchestration, and execution of complex tasks with minimal human intervention is fueling the growth and adoption of agentic AI systems in the U.S. Additionally, the presence of a strong research and innovation ecosystem, presence of global tech giants, availability of highly skilled AI engineers and researchers, and AI investments and funding programs are contributing towards the development of agentic AI market ecosystem in the U.S.

Key players operating in the market include AWS, Celonis, Cohere, Moveworks, Oracle Corporation, Pegasystems Inc., Salesforce, Inc., SAP SE, UiPath and others. These companies are improving operability of agentic AI system to enhance autonomous decision-making capabilities, improve enterprise productivity, expand AI-driven automation solutions, and enhance learning capabilities.

• In March 2026, Instructure, Inc. announced the launch of IgniteAI Agent, which provides a secure, transparent workflow capability, enabling institutions navigate towards agentic AI. The agent performs complex, multi-step operations on behalf of educators and administrators while preserving institutional control, transparency, and trust. 

Key Market Highlights:

1. The U.S. agentic AI market size was recorded at USD 6.20 billion in 2025.
2. The market is projected to grow at a CAGR of 49.16% from 2026 to 2033.
3. The agentic AI applications (SaaS) offering was estimated at USD 2.36 billion in 2025.
4. The software development and testing application is forecasted to register the fastest growth rate of 54.86% over the forecast period.
5. The software and technology end user industry garnered USD 1.24 billion in 2025.
6. The small and medium-sized business (SMBs) end user segment was valued at USD 1.12 billion with a CAGR of 55.09%.

How is the adoption of agentic AI in the commercial landscape of the U.S. driving the market growth?

Companies across commercial sectors in the U.S. comprising banking and financial services, retail, education, healthcare and others are deploying agentic AI systems to enhance efficiency and improve workflows. Banking and financial firms are adopting AI agents to provide customized financial advisory services, detect fraud, automate loan approvals, and address legal and compliance processes. Payment intermediaries such as VISA and Mastercard are introducing tokenization technology to create unique Agentic Tokens that tie AI agents to individual users while safeguarding payment credentials.

• In June 2026, Robinhood launched Agentic Trading feature which utilizes AI agents to execute stock trades within a dedicated, ring-fenced account. The company also introduced Agentic Credit Card which enables AI agents to make purchases on behalf of the users using a virtual card with a spending limit control. 

• In April 2026, Visa launched Visa Intelligent Commerce (VIC) which enables secure, trusted AI‑initiated transactions by embedding payment credentials, controls, authentication and protections into automated buying. This helps users facilitate seamless payments by utilizing agentic commerce. 

Retail companies are developing LLM-powered AI agents to automate personal shopping experiences and facilitate swift merchandise planning and problem resolution. Moreover, consumers are adopting agentic AI to search, compare, and manually check out after delegating product discovery and purchases prompts to AI agents.

• In May 2025, Walmart Inc. introduced agentic AI tools tailored to retail-specific tasks. The company utilized its own data and LLMs to automate business processes, enable product comparisons, personalize recommendations, offer customer support, plan merchandise, and enhance shopping experiences.

Market players are further unifying communication through innovations across shopping systems, enabling agentic AI to streamline product comparison, purchasing processes, and automate retail operations to improve business efficiency and scalability.

• In January 2026, Google introduced Universal Commerce Protocol (UCP), an open standard that unifies the communication of shopping systems. The objective is to support the rise of agentic AI-enabled shopping, further enabling users to compare options and enable purchase efficiently.
• In January 2026, Microsoft revealed its Agentic AI suite to automate retail merchandising, marketing, and store operations. The platform enables retailers to serve a wider customer base with resilience and efficiency. 

How do trust and security concerns restrain the growth of the agentic AI market in the U.S.?       

Concerns over agent transparency and decision-making restrain the widespread deployment and adoption of agentic AI. Enterprises are cautious about deploying autonomous agentic AI models due to their non-deterministic nature compared to conventional software-based systems. This makes their operations less predictable and increases the risk of errors in critical applications such as healthcare, financial services, and enterprise operations. Additionally, multi-agent and multi-cloud environments expand the attack surface which raises concerns over data privacy, cybersecurity, identity management, and unauthorized access. These obstacles highlight the absence of robust governance, continuous model validation, and real-time safety guardrails to ensure reliable AI performance.

To address the challenge, the U.S. Cybersecurity and Infrastructure Security Agency (CISA), in collaboration with the National Security Agency (NSA) has identified five major security risks associated with Agentic AI. The list includes excessive access privileges, poor system design, unpredictable agent behavior, vulnerabilities arising from interconnected multi-agent systems, and limited accountability due to autonomous decision-making. CISA has recommended implementing least-privilege access controls, strong identity management, continuous monitoring, comprehensive testing (including adversarial testing and red teaming), robust safety guardrails, detailed audit logs, and phased deployment of AI agents.

• In June 2026, Okta announced the expansion of its Cross App Access (XAA) ecosystem for organizations to manage AI agent-to-app and app-to-app connections. The initiative allows AI agents to access enterprise applications through centralized identity policies, thus improving security, governance, and visibility by ensuring all agent activities are logged and access is tightly controlled. 

• In May 2026, Cyberhaven announced expansion of its Unified AI & Data Security Platform, which governs autonomous agents and protects data across enterprise workflows. The product introduces Agentic AI Security, which enables security teams to discover, monitor, and control AI agents. 

• In March 2026, Microsoft released Agent 365, a control plane for agents that equips IT, security, and business teams with visibility and tools required to secure and govern agents. The platform includes new Microsoft Defender, Entra, and Purview capabilities which help secure agent access, prevent data oversharing, and defend against emerging threats. 

• In October 2024, Salesforce, Inc. released Agentforce which enables companies to build and deploy AI agents. The platform uses advanced reasoning abilities to make business decisions and resolve customer cases, qualify sales leads, and optimize marketing campaigns. 

How is the adoption of AI agent orchestration models emerging as a notable trend in the U.S. agentic AI market?

AI agent orchestration involves the coordination of multiple specialized AI agents within a unified system to efficiently achieve shared objectives. The models are gaining traction across enterprises owing to the deployment of a single AI agent that handles roles including customer support, data retrieval, and automation. This ensures parallel collaboration, efficient workflows and smooth communication between multiple agents, applications, and enterprise systems. This results in scalability, reliability, and operational accuracy by reducing agentic AI silos between systems and enabling better coordination. Moreover, the ability of AI agent orchestration to isolate faults helps prevent cascading failures. This ensures operational continuity as it facilitates allocated task execution in case of an agent failure, while the failed agent retries the task or the workflow is re-routed around the issue.  

• In April 2026, T1U (The One-Unified System) introduced an AI Business Operating System, which unifies complex enterprise functions such as CRM, finance, HR, projects, inventory, customer support, and automation on a single platform. 

• In June 2025, Pegasystems Inc. introduced Pega Agentic Process Fabric, an orchestration layer that connects AI Agents, employees, customers, systems, and workflows across an organization.  

• In March 2025, NiCE launched CXone Orchestrator, which delivers end-to-end automation for customer service by unifying all touchpoints, including virtual agents, live agents and back-office workflows. 

• In March 2025, Adobe launched Adobe Experience Platform (AEP) Agent Orchestrator, which coordinates multiple specialized AI agents to execute complex, multi-step customer experience tasks using natural language prompts.

 Agentic AI Market Report Snapshot

 Market Segmentation

• By Offering (Agentic AI Infrastructure, Agentic AI Development Platforms, Agentic AI Applications (SaaS), Agentic AI Professional Services). The agentic AI applications (SaaS) captures the highest market share of 38% in 2025 with a valuation of USD 2.36 billion. 

• By Application (Customer Experience, Software Development & Testing, Knowledge Management & Retrieval, Sales, Data Analytics & BI, Security Operations, Finance & Accounting, Marketing, HR & Workplace Operations, Others). The software development and testing application captures the fastest growth rate of 54.86% over the forecast period. 

• By End-Use Industry (Software & Technology, BFSI, Healthcare & Life Sciences, Retail & E-commerce, Professional Services, Manufacturing, Telecommunications, Government & Defense, Logistics & Transportation, Others). The software & technology segment garnered USD 1.24 billion in 2025 and is anticipated to register a CAGR of 50.31% over the forecast period. 

• By End User (Large Enterprises, SMBs, Government Organizations, Individuals). The small and medium-sized business (SMBs) end user segment was valued at USD 1.12 billion in 2025 and is anticipated to register a CAGR of 55.09% over the forecast period.

What is the scenario in the U.S. Agentic AI market?

The U.S. is designated as a premier hub for agentic AI development owing to the vast software development ecosystem, which is fostering the development of artificial intelligence products and services. The AI Action plan introduced in January 2025 establishes a national framework to guide AI infrastructure initiatives and federal policymaking across priority areas. This establishes a growth trajectory for the deployment and utilization of agentic AI across commercial, industrial, and government sectors in the U.S. For instance, federal agencies in the U.S. are testing limitations of agentic AI to boost mission delivery and operational growth. The focus is to emphasize designing, deployment and scaling of AI systems in high-impact environments such as defense, federal services, and government operations to boost operational efficiency.

• In May 2026, U.S. Department of defense (DOD), Pentagon deployed more than 100,000 AI agents through the GenAI.mil platform. The AI agents support mission activities including drafting after-action reports, analyzing operational data, and reviewing images. 

• In May 2025, the U.S. Food and Drug Administration (FDA) introduced the deployment of agentic AI capabilities for all agency employees. The move is targeted at enabling the creation of more complex AI workflows through Agentic AI capabilities. 

Regulatory Frameworks

• The Artificial Intelligence 2025 Legislation establishes a national U.S. policy framework to reduce regulatory barriers to AI innovation. It directs federal agencies to identify and challenge state AI laws that are inconsistent, or harmful to innovation, including through a newly created AI Litigation Task Force. 

• The Colorado Artificial Intelligence Act reduces employer compliance obligations and delays towards AI adoption. The law applies to automated decision-making technologies including agentic AI that influence business decisions. The Act further removes several requirements including AI impact assessments, annual reviews, and mandatory reporting to regulators. 

• The NIST Center for AI Standards and Innovation (CAISI) launched AI Agent Standards Initiative on February 17, 2026. The framework establishes interoperability and security standards for agentic AI systems. It ensures AI agents capable of autonomous actions can interoperate smoothly across the digital ecosystem. 

Competitive Landscape

Key players operating in the agentic AI market, such as Aisera, Celonis, Cohere, Google LLC, Moveworks, Pegasystems Inc., Salesforce, Inc., SAP SE, UiPath and others, are focusing on strategic collaborations, partnerships, and technological innovation to improve operational efficiency and expand market share. The players are prioritizing the development of multi-agent systems and AI copilots that improve decision-making, operational efficiency, and scalability across business functions.

• In December 2025, Meta acquired Manus to integrate its autonomous general-purpose agents in its products. The agents independently execute complex tasks including market research, coding, and data analysis thereby enabling Meta to scale advanced AI agents to its users globally. 

• In November 2025, Salesforce, Inc. acquired Informatica to integrate its data catalog, metadata management, and Master Data Management (MDM) services to the Salesforce platform. The acquisition is further aimed at establishing a unified and comprehensive data foundation for agentic AI, thus enabling AI agents to operate safely, responsibly, and at scale across the modern enterprise. 

List of Key Companies:

• Amazon Web Services
• Aisera
• Anthropic PBC
• Celonis
• Cohere
• Google LLC
• IBM Corporation
• Moveworks
• Microsoft Corporation
• OpenAI
• Oracle Corporation
• Pegasystems Inc.
• Salesforce, Inc.
• SAP SE
• UiPath

 Recent Developments

• In June 2026, Fortinet, Inc. launched FortiSOC, a unified cloud-delivered security operations center (SOC) platform that consolidates six security operations functions into a single SaaS-based solution. The platform incorporates agentic AI to autonomously investigate and correlate alerts across assets and identities and simultaneously executing response actions under analyst oversight. 

• In June 2026, NTT DATA launched an AI agent service designed to accelerate early-stage product planning for food, beverage and consumer goods companies. The AI agent service incorporates generative AI technologies, including retrieval-augmented generation (RAG) and multi-agent architectures to enable teams to generate and structure product concepts in minutes and move them more quickly to the internal evaluation and decision-making phases. 

• In June 2026, Databricks introduced Genie One, an agentic AI coworker that enables business teams to automate workflows and orchestrate tasks across structured and unstructured enterprise data. The platform leverages Genie Ontology, which is a live context layer that extracts and updates business knowledge from AI tools and connected workplace to deliver efficient responses at lower token costs.