In the year 2024, the market value of agentic artificial intelligence (AI) stood at *** billion U.S.dollars. It is anticipated that this market value will surpass ** billion U.S.dollars, with a compound annual growth rate of over ** percent, as reported by Capgemini. This tremendous growth demonstrates the potential of agentic AI to transform industries through autonomous action and decision-making.

This dataset provides a comprehensive overview of various Agentic AI (autonomous AI) applications across multiple industries in 2025. It contains detailed records of how AI is being utilized to automate complex tasks, improve efficiency, and generate measurable outcomes. The dataset is designed to help researchers, data scientists, and businesses understand the current state and potential of Agentic AI in different sectors. Dataset Features: Industry: The sector where Agentic AI is applied (e.g., Healthcare, Finance, Manufacturing).

Application Area: The specific task or function performed by the AI agent (e.g., Fraud Detection, Predictive Maintenance).

AI Agent Name: The name of the AI system or agent deployed (e.g., HealthAI Monitor, FinSecure Agent).

Task Description: A brief description of the AI's function or role.

Technology Stack: The technologies powering the AI (e.g., Machine Learning, NLP, Computer Vision).

Outcome Metrics:The measurable impact of the AI deployment (e.g., 30% reduction in ER visits).

Deployment Year: The year the AI system was deployed (ranging from 2023 to 2025).

Geographical Region: The region where the AI application is implemented (e.g., North America, Asia, Europe).

Artificial intelligence agents are poised to revolutionize business operations in the coming years. While many organizations are planning to integrate AI agents within the next three years, a significant portion anticipate longer-term implementation for more complex tasks. This shift towards AI-driven business processes reflects the growing importance of automation and data-driven decision-making in the corporate world. Near-term focus on modernization and quality assurance By 2025, businesses are prioritizing the upgrade and modernization of functions and integrations, with 48 percent of organizations planning to implement these changes within the next three years. Quality assurance of digital functions and systems is also a key near-term objective, with 46 percent of companies focusing on this area. These initiatives indicate a strong emphasis on laying the groundwork for more advanced AI integration in the future. Long-term vision for comprehensive AI integration Looking beyond the immediate future, organizations have ambitious plans for AI agent integration. Many businesses expect to implement workflow automations that span across the organization and access functions from third-party systems within four or more years. This long-term outlook aligns with executive beliefs that the general public will be using AI agents more than websites or apps from 2031 onwards, with most anticipating this shift to occur from 2036 and later. As research in agentic systems continues to grow exponentially, businesses are preparing for a future where AI agents play a central role in their operations.

Market Overview

The global Agentic AI market is experiencing an extraordinary transformation as intelligent software agents that autonomously perceive, decide, and act are becoming integral components of digital enterprise transformation. Based on extensive analysis, the market size of Agentic AI market was valued at USD 5.1 billion in 2024 and is forecast to reach around USD 150 billion by 2033, driven by powerful growth factors and robust compound annual growth rates (CAGR) estimated at nearly 35% from 2025 to 2033. This explosive expansion is fueled by the rapid digitalization of enterprises, strong government and R&D funding, and the increasing need for automation to improve operational efficiency and reduce human error by up to 40% in some cases. Leading technology trends include advancements in machine learning models such as LLMs, increased data generation from IoT sensors, and the integration of low-latency connectivity enabled by global 5G rollouts, which together create a fertile environment for the adoption of agentic AI solutions.

Major industry reports and triangulated data from government sources, official annual reports, and regulatory filings consistently affirm that as organizations seek to streamline workflows and enhance real-time decision-making with autonomous agents, the market will begin its mainstream consolidation over the next decade. Enterprises spanning finance, healthcare, retail, IT, and manufacturing are incorporating intelligent solutions to optimize processes, reduce costs, and unlock new revenue streams from agent-driven automation. Furthermore, the steady integration of sophisticated AI agents into cloud and on-premise systems, coupled with the increasing penetration of AI software subscriptions and hardware investments, reinforces the market’s transformative potential. This enormous growth trajectory not only underlines agentic AI’s role as a key enabler of digital transformation but also highlights its potential to reshape entire industries.

Investor confidence and aggressive R&D investments by both entrenched tech giants and specialized agents have been a primary driver of this market’s momentum, especially as major vendors consistently report significant revenue contributions from their AI platforms. The market is set to witness an unparalleled increase in operational efficiency as large enterprises along with SMEs leverage cloud-based and hybrid AI solutions to deploy scalable, intelligent virtual agents. With wide-ranging applications from customer service chatbots to autonomous robotics and decision-support systems, the Agentic AI market is positioned to undergo a multi-fold expansion that mirrors the evolution witnessed in cloud computing over the past decade. Overall, the forecasted growth and expansion of the market underscore a pivotal shift in how digital businesses will operate through fully integrated intelligent agents.

The projected market figures, coupled with the impressive CAGR, indicate enormous potential for both existing vendors and new startups as they compete to capture market share in this high-growth environment. The strategic repositioning of traditional tech companies into agile, AI-driven solution providers and the entry of independent innovators have already started to blur traditional boundaries, leading to a more integrated ecosystem where hardware, software, and services combine for maximum impact. The quantitative data not only supports the promise of explosive scaling but also reinforces the expectation that agentic AI will become a cornerstone in delivering business-driven intelligence and automation for enterprises across the globe.

The Agentic AI Market size to reach USD 48.5 Billion in 2034 growing at a CAGR of 18.5%. The research report classifies market by share, trend, demand and based on segmentation by Component, Technology, Industry Vertical, Deployment Model And Region.

In 2024, less than *** percent of the enterprise software applications included agentic AI. Which means most of the applications with AI integrated rely heavily on user prompts. However, this is projected to change drastically by 2028, with almost ******* of all enterprise software applications incorporating agentic AI, enabling a higher level of autonomy in decision-making.

In 2025, just ** percent of organizations are very familiar with agentic AI, while over half claim to be somewhat familiar with it. However, most organizations remain in the exploratory phase.

Agentic AI in Energy Market is estimated to reach USD 10,700 Million By 2034, Riding on a Strong 36.4% CAGR throughout the forecast period.

Analyst Viewpoint

The Agentic AI in Data Engineering market is on track for rapid growth, driven by the increasing reliance on data integration solutions and AI-powered technologies like Natural Language Processing (NLP).

The cloud-based deployment model will continue to dominate, especially in large enterprises that require scalable, efficient data management solutions. The BFSI sector’s strong presence indicates significant demand for advanced data engineering solutions to streamline operations, reduce risks, and improve customer experiences.

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Global Agentic AI For Data Engineering Market size is expected to reach USD 63.8 Bn by 2033, from USD 3.4 Bn in 2024, growing at a CAGR of 36.2% from 2024 to 2033

The global agentic AI startup landscape has experienced explosive growth in 2024, with funding skyrocketing to *** billion U.S. dollars and *** deals completed. This marks a dramatic increase from just ** million U.S. dollars and * deals in 2020, highlighting the rapidly evolving nature of the AI industry. The surge in investment reflects growing confidence in AI technologies and their potential to revolutionize various sectors. Global competition intensifies As funding pours into AI startups, Israel has emerged as a formidable player, boasting a median mosaic score of *** across ** funded AI companies in 2024. This puts Israel ahead of established tech hubs like the United Kingdom and Germany. The United States continues to dominate AI research capacity, partly due to contributions from tech giants like Microsoft, Google, Apple, and Amazon. European mergers grow European AI startup acquisition has grown considerably recently, jumping from around ** in 2020 to over *** in 2024. This suggests an increase in European AI startups that become noteworthy and successful, a good indicator for a continent that has been slower to capitalize on AI advances than the United States and China.

Market Insights

Based on insights provided by Market.us, the Agentic AI in HR & Recruitment market is projected to witness substantial growth, reaching approximately USD 23,172.8 million by 2034, up from USD 842.3 million in 2024, expanding at an impressive compound annual growth rate (CAGR) of 39.3% during the forecast period from 2025 to 2034. The rapid adoption of AI-driven recruitment solutions, automation in HR processes, and demand for data-driven talent acquisition strategies are key drivers fueling this market expansion.

In 2024, North America led the market, capturing over 37.1% of the total revenue, which amounted to USD 312.4 million. The region’s strong position is driven by the widespread adoption of AI-powered HR technologies among large enterprises, the increasing need for efficient hiring processes, and the presence of major AI solution providers. The integration of AI in talent acquisition, employee engagement, and workforce analytics is significantly transforming HR operations across industries.

As organizations continue to prioritize efficiency, diversity, and unbiased recruitment, the demand for AI-driven HR solutions is expected to surge. The expansion of generative AI, machine learning algorithms, and natural language processing in HR applications will further accelerate market growth. With advancements in AI-based predictive hiring, candidate assessment, and workforce management, the Agentic AI in HR & Recruitment market is set to redefine the future of talent acquisition and employee experience globally.

Agentic Data Access Benchmark (ADAB)

Agentic Data Access Benchmark is a set of real-world questions over few "closed domains" to illustrate the evaluation of closed domain AI assistants/agents. Closed domains are domains where data is not available implicitly in the LLM as they reside in secure or private systems e.g. enterprise databases, SaaS applications, etc and AI solutions require mechanisms to connect an LLM to such data. If you are evaluating an AI product or building your… See the full description on the dataset page: https://huggingface.co/datasets/hasura/agentic-data-access-benchmark.

Generative AI for automation or agentic AI is the most interesting technology development for organizations in 2024.

The rate of research papers published with a focus on agentic systems has grown exponentially in the past 5 years, with thousands of papers being released just in the last year.

Scientific modeling often requires navigating a trade-off between physical interpretability and empirical accuracy—a task that can take weeks of iteration, especially in systems with partial observability, structural complexity, and experimental errors. Here, we show how a state-of-the-art agentic reasoning-and-coding large language model, OpenAI o3, autonomously solved a modeling challenge in surface chemistry that puzzled us for months: quantifying the competitive adsorption of carboxylic acids on metal–organic layers. With experimental data and a concise problem formulation, o3 rapidly formulated a physically grounded adsorption model, derived the mathematical equations, implemented the corresponding codes to fit the experimental data, revised its assumptions, and ultimately derived a competitive adsorption model with three parameters that matched experimental data across more than a dozen of tested molecules. The resulting model—simple, mechanistically transparent, and quantitatively robust—incorporates both classical Langmuir competition and structural constraints such as site accessibility. Beyond addressing this particular challenge, our findings highlight a transformative shift in scientific methodology: from manual trial-and-error approaches to AI-driven hypothesis generation and model refinement. This represents a new paradigm in research, wherein language models surpass the traditional roles of machine learning in data analysis and computational support, actively participating in scientific reasoning and hypothesis development. Here is the experimental dataset in this work.

How Growth is Impacting the Economy

The remarkable expansion of agentic AI in digital engineering is reshaping economies by accelerating innovation and increasing productivity across multiple sectors. By automating complex engineering tasks, AI reduces time-to-market, lowers costs, and improves design accuracy, driving operational efficiency. This fosters a competitive environment that stimulates investments in technology, research, and development.

Job creation is notable in AI development, software engineering, and advanced manufacturing, bolstering skilled labor markets. Enhanced digital workflows enable companies to respond rapidly to market demands and disruptions, contributing to economic resilience.

Furthermore, agentic AI supports sustainable engineering by optimizing resource use and minimizing waste. However, economies must address challenges, including workforce upskilling and data security, to sustain growth. Overall, agentic AI in digital engineering is a critical driver of future economic growth and technological leadership.

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The number of funding deals for startups specializing in humanoid robots has grown considerably between 2020 to 2024, from 10 to 44. This is a near quadrupling of the number of deals and is likely heavily influenced by leaps in AI technologies in the same period.

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

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