Executives believe that overall the general public will be using AI agents more than websites or apps from 2031 onwards, though most believe it will happen from 2036 and later.

The near term targets for AI agent integration in organizations worldwide in 2025 is focused on upgrading and modernizing functions as well as assuring the quality of digital functions.

The Sotheby's International Realty dataset provides a premium collection of real estate data, ideal for training AI models and enhancing various business operations in the luxury real estate market. Our data is carefully curated and prepared to ensure seamless integration with your AI systems, allowing you to innovate and optimize your business processes with minimal effort. This dataset is versatile and suitable for small boutique agencies, mid-sized firms, and large real estate enterprises.

Key features include:

Custom Delivery Options: Data can be delivered through Rest-API, Websockets, tRPC/gRPC, or other preferred methods, ensuring smooth integration with your AI infrastructure. Vectorized Data: Choose from multiple embedding models (LLama, ChatGPT, etc.) and vector databases (Chroma, FAISS, QdrantVectorStore) for optimal AI model performance and vectorized data processing. Comprehensive Data Coverage: Includes detailed property listings, luxury market trends, customer engagement data, and agent performance metrics, providing a robust foundation for AI-driven analytics. Ease of Integration: Our dataset is designed for easy integration with existing AI systems, providing the flexibility to create AI-driven analytics, notifications, and other business applications with minimal hassle. Additional Services: Beyond data provision, we offer AI agent development and integration services, helping you seamlessly incorporate AI into your business workflows. With this dataset, you can enhance property valuation models, optimize customer engagement strategies, and perform advanced market analysis using AI-driven insights. This dataset is perfect for training AI models that require high-quality, structured data, helping luxury real estate businesses stay competitive in a dynamic market.

Historical price and volatility data for Test AI Agent in US Dollar across different time periods.

Historical price and volatility data for Kimi AI Agent in Taiwan New Dollar across different time periods.

Comparison data for 463 AI tools across 19 categories

A survey conducted in 2023 shows how likely consumers are to adopt the use of artificial intelligence (AI) when shopping online, and in which categories would that happen the most. Around 70 percent of respondents said they would use AI when purchasing flights and close to this number, around 65 percent, would use the tool to look for hotels and resorts. Consumers who would use AI to buy medicine, clothes, beauty products and electronics range from 50 to 60 percent.

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The dataset explains the dynamics of an agent-based simulation conducted over 157,097 steps, tracking the behaviors and interactions of artificial agents under varying environmental pressures and population structures. It contains 12 key metrics:tick: Simulation step or iteration.cooperators: Number of cooperative agents.defectors: Number of defecting agents.super_reciprocators: Number of agents employing advanced cooperative strategies.free_riders: Number of agents exploiting resources without contributing.avg_alignment: Average alignment index of the agents (scale 0–1).entropy: A measure of system diversity.coherence: Degree of collective alignment.adaptation_pressure: Measure of agents' stress in adapting to environmental changes.environmental_stress: External stress imposed on the system.mean_energy: Average energy levels of agents.mean_reputation: Average reputation levels of agents.

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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 global market for Dynamic AI Agents is experiencing robust growth, driven by increasing demand for automated customer service, improved operational efficiency, and the need for personalized user experiences across various industries. While precise market sizing data isn't provided, considering similar AI-powered solutions and their growth trajectories, a reasonable estimation for the 2025 market size could be around $5 billion, with a Compound Annual Growth Rate (CAGR) of 20% projected for the forecast period (2025-2033). This growth is fueled by several key factors. Firstly, the shift towards cloud-based solutions offers scalability and cost-effectiveness, driving wider adoption among both large enterprises and SMEs. Secondly, advancements in Natural Language Processing (NLP) and Machine Learning (ML) are enabling more sophisticated and human-like interactions, improving customer satisfaction and reducing operational costs. Finally, the rising integration of Dynamic AI Agents into diverse applications, from chatbots and virtual assistants to personalized recommendations, is expanding market reach and application opportunities. The market segmentation reveals a significant share held by cloud-based solutions, reflecting the trend towards agility and reduced infrastructure management. Large enterprises currently dominate the application segment, leveraging the technology for streamlining complex workflows and optimizing customer interactions. However, the SME segment shows significant potential for future growth as adoption accelerates. Geographic analysis suggests that North America and Europe currently hold the largest market shares, owing to early adoption and technological advancements. However, the Asia-Pacific region is expected to witness the fastest growth due to increasing digitalization and a large pool of potential users. Despite this promising outlook, challenges such as data security concerns, integration complexities, and the need for ongoing maintenance and updates could potentially restrain market growth to some extent. The success of Dynamic AI Agent deployment hinges on addressing these challenges while continuing to innovate and improve user experience.

This dataset contains the raw data and R data file utilized for conducting an user study examining the effect of value similarity on Human-AI agent trust.

Market Insights

The AI Agents in eCommerce Market is projected to expand from USD 3.6 billion in 2024 to approximately USD 282.6 billion by 2034, growing at a CAGR of 54.7% during the forecast period from 2025 to 2034. This rapid growth is driven by the increasing adoption of AI-powered virtual assistants, chatbots, personalized recommendation engines, and automated customer service solutions. AI agents are revolutionizing the eCommerce sector by enhancing customer experience, streamlining operations, and improving sales conversion rates through data-driven insights.

In 2024, North America held a dominant market position, capturing more than a 38.5% share, with USD 1.3 billion in revenue. The region's leadership is attributed to strong investments in AI technology, high eCommerce penetration, and the presence of key market players. The widespread adoption of AI-driven automation by major eCommerce platforms and retailers is further fueling regional growth. The U.S., in particular, is at the forefront, leveraging AI to enhance customer engagement, supply chain optimization, and fraud detection in online retail.

The market for AI agents in eCommerce is experiencing rapid growth, driven by the increasing demand for enhanced customer experience and improved operational efficiency. As of recent estimates, the market is projected to escalate significantly in value over the next few years, reflecting a growing adoption of AI technologies in retail and commerce sectors. This growth is attributed to the ability of AI agents to streamline eCommerce operations, reduce costs, and provide a personalized shopping experience, which in turn boosts sales and customer satisfaction​

The primary driving factors for the adoption of AI agents in eCommerce include the need for advanced customer service solutions, the ability to handle large volumes of data, and the demand for operational efficiency. The integration of AI agents helps businesses manage customer interactions more effectively, optimize inventory and pricing strategies, and personalize marketing efforts, which are crucial for staying competitive in the digital marketplace​.

In the year 2024, the market value of agentic artificial intelligence (AI) stood at 5.1 billion U.S.dollars. It is anticipated that this market value will surpass 47 billion U.S.dollars, with a compound annual growth rate of over 44 percent, as reported by Capgemini. This tremendous growth demonstrates the potential of agentic AI to transform industries through autonomous action and decision-making.

The repository data corresponds partially to the manuscript titled "Can LLM-Augmented Autonomous Agents Cooperate? An Evaluation of Their Cooperative Capabilities through Melting Pot," submitted to IEEE Transactions on Artificial Intelligence. The dataset comprises experiments conducted with Large Language Model-Augmented Autonomous Agents (LAAs), as implemented in the ["Cooperative Agents" repository](https://github.com/Cooperative-IA/CooperativeGPT/tree/main), using substrates from the Melting Pot framework.

Dataset Scope

This dataset is divided into two main experiment categories:

1. Personality_experiments:

   • These focus on a single scenario (Commons Harvest) to assess various agent personalities and their cooperative dynamics.

2. Comparison_baselines_experiments:

   • These experiments include three distinct scenarios designed by Melting Pot:
     • Commons Harvest Open
     • Externally Mushrooms
     • Coins

These scenarios evaluate different cooperative and competitive behaviors among agents and are used to compare decision-making architectures of LAAs against reinforcement learning (RL) baselines. Unlike the Personality_experiments, these comparisons do not involve bots but exclusively analyze RL and LAA architectures.

Scenarios and Metrics

The metrics and indicators extracted from the experiments depend on the scenario being evaluated:

1. Commons Harvest Open:

   • Focus: Resource consumption and environmental impact.
   • Metrics include:
     • Number of apples consumed.
     • Devastation of trees (i.e., depletion of resources).

2. Externally Mushrooms:

   • Focus: Self-interest vs. collective benefit.
   • Agents consume mushrooms with different outcomes:
     • Mushrooms that benefit the individual.
     • Mushrooms that benefit everyone.
     • Mushrooms that benefit only others.
     • Mushrooms that benefit the individual but penalize others.
   • Metrics evaluate trade-offs between individual gain and collective welfare.

3. Coins:

   • Focus: Reciprocity and fairness.
   • Agents collect coins with two options:
     • Collect their own color coin for a reward.
     • Collect a different color coin, which grants a reward to the agent but penalizes the other.
   • Metrics include reciprocity rates and the balance of mutual benefits.

Objectives of Comparison Experiments

The Comparison_baselines_experiments aim to:

1. Assess how LAAs compare to RL baselines in cooperative and competitive tasks across diverse scenarios.
2. Compare decision-making architectures within LAAs, including chain-of-thought and generative approaches.

These experiments help evaluate the robustness of LAAs in scenarios with varying complexity and social dilemmas, providing insights into their potential applications in real-world cooperative systems.

Simulation Details (Applicable to All Experiments)

In each simulation:

1. Participants:

   • Experiments involve predefined numbers of LAAs or RL agents.
   • No bots are included in Comparison_baselines_experiments.

2. Action Dynamics:

   • Each agent performs high-level actions sequentially.
   • Simulations conclude either after reaching a preset maximum number of rounds (typically 100) or prematurely if the scenario's resources are fully depleted.

3. Metrics and Indicators:

   • Extracted metrics depend on the scenario and include measures of individual performance, collective outcomes, and agent reciprocity.

This repository enables reproducibility and serves as a benchmark for advancing research into cooperative and competitive behaviors in LLM-based agents.

The Visual AI Agents market is rapidly evolving, harnessing sophisticated technologies to transform industries by automating visual tasks that traditionally required human intervention. These AI-powered agents are designed to analyze, understand, and interpret visual data, thus offering solutions that enhance effici

Success.ai’s Commercial Real Estate Data and B2B Contact Data for Global Real Estate Professionals is a comprehensive dataset designed to connect businesses with industry leaders in real estate worldwide. With over 170M verified profiles, including work emails and direct phone numbers, this solution ensures precise outreach to agents, brokers, property developers, and key decision-makers in the real estate sector.

Utilizing advanced AI-driven validation, our data is continuously updated to maintain 99% accuracy, offering actionable insights that empower targeted marketing, streamlined sales strategies, and efficient recruitment efforts. Whether you’re engaging with top real estate executives or sourcing local property experts, Success.ai provides reliable and compliant data tailored to your needs.

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Success.ai’s B2B Contact Data for Global Real Estate Professionals delivers the tools you need to connect with the right people at the right time, driving efficiency and success in your business operations. From agents and brokers to property developers and executiv...

According to the source's projections, some 28 percent of clerical roles in advertising agencies were forecast to be lost because of generative artificial intelligence (AI) in the United States by 2030. This was followed by an expected 22 percent job loss of sales-related roles.

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