GPTs, an emerging force in the field of artificial intelligence, have not dampened the enthusiasm for creating their own ChatGPT, despite all the problems and security controversies that have accompanied it since its launch. The infinite possibilities and innovation potential behind it make people look forward to it and hope that it will help usher in a new era of intelligence.
AI Agent Boom: Uncovering the current market landscape and future growth trends, a comprehensive understanding in one article
At the same time, domestic large language model manufacturers are also actively deploying the AI agent market. The Tiangong SkyAgents platform released by Kunlun provides users with a convenient way to build a personal assistant through natural language. ByteDance's bean package platform also followed suit and launched the function of creating AI agents. The intelligent Tars model also provides new development opportunities for enterprises. These platforms not only achieve deep integration with office and other products in their respective ecosystems, but also further expand the application scenarios of AI agents.
As an early product of AI agents, the emergence of a large number of "quasi-agent" GPTs indicates that LLMs will be applied in more scenarios. These agents not only improve the user experience, but also pave the way for the application of real AI agents, leading the development direction of the entire market.
Bill Gates once predicted that Agent will be able to help people handle almost all activities and all areas of life, and will have a profound impact on the software industry and society as a whole. Now, it seems, this prophecy is gradually becoming a reality. As a prelude to the era of AI agents, the wide application of GPTs heralds the coming of the era of autonomous agents.
In the future, the AI agent market will show an extremely large scale. Large model manufacturers, technology providers, enterprise service software vendors, start-ups, and large enterprises in various fields will actively participate in this feast of AI agents. With the continuous progress of technology and the continuous expansion of application scenarios, AI agents will play an increasingly important role in various fields and become an important force to promote social progress.
AI Agent市场前景展望
As a rising star of intelligent technology, AI Agent has a bright market prospect. According to MarketsandMarkets, the market size of autonomous agents has surged from $345 million in 2019 and is expected to reach $2.992 billion in 2024, with a compound annual growth rate of 54%. By 2028, the market is expected to exceed $28.5 billion in revenue, showing strong growth.
The factors driving market expansion are rich and diverse. Increased automation and agility enable companies to benefit from efficient task completion, reduced costs, and increased competitiveness. At the same time, customer experience has become a new focus of enterprise competition, and the role of autonomous agents in improving customer experience has become increasingly prominent. In addition, the increase in cost savings and return on investment is also driving companies to actively invest in autonomous agent technology.
The popularization of AI applications, the improved accessibility of computing resources, and advances in autonomous driving, healthcare, and other fields have provided strong support for the development of autonomous AI and agents. These technological advancements not only improve the performance of intelligent twins, but also expand their application fields to better serve various industries in society.
It is worth noting that in the future, the AI agent market will show a diversified development trend, and although autonomous agents will be the mainstream, non-autonomous agents and generative agents will also coexist. Different enterprises have different needs for agents due to different business attributes and market goals, so the AI agent market space is actually broader.
As a software or program that can operate and make decisions independently, autonomous agents have a wide range of applications in various fields. Chatbots, digital assistants and other forms of agents not only improve work efficiency, but also bring high-quality user experience.
Looking forward to the future, the development of AI Agent will be more in-depth and extensive. Technological advancements and the expansion of application scenarios will promote AI Agents to play a key role in more fields. With the growth of enterprise demand for intelligence, the market size of AI agent will continue to expand.
AI Agent Boom: Uncovering the current market landscape and future growth trends, a comprehensive understanding in one article
The market landscape of AI agents
Under the precise layout of the capital market, a clear three-tier structure has been formed in the field of autonomous agents: agent operation, program application and service layer.
The agent operation layer draws on the OpenAI architecture and is subdivided into seven modules, including intelligence, memory, and tool plug-ins, to jointly build the core functions of the agent. The agent operation layer, which borrows from OpenAI's official AI agent architecture, has become one of the most popular agent architectures.
This layer can be divided into seven modules, each of which plays an integral role. Intelligence, as the "brain" of agents, is powered by large language models (LLMs) that understand and produce natural language, have extensive knowledge of the world, and have the ability to learn. Memory memory, on the other hand, is responsible for acquiring, storing, retaining, and retrieving data, providing memory functions for agents. The Tools & Plug-ins module provides a wealth of external tools and plug-ins to extend the functionality and application range of the agent. The multi-agent playground and protocol module solves the communication protocol problem between agent networks and provides a basis for the collaborative work of agents. The multi-agent communication model focuses on how to make multiple agents interact in the best way to improve the efficiency and effectiveness of multi-agent learning. The Monitoring, Security, and Budgeting module is responsible for overseeing the operation of the agent and ensuring that it works in a safe and compliant manner. Finally, as a release platform for the Agent Framework products, the Agent Operation Market provides strong support for the commercialization of Agents.
This three-tier structure not only helps us better understand the development status and trend of the autonomous agent industry, but also provides investors and practitioners with a clear investment and development direction. With the continuous progress of technology and the continuous expansion of application scenarios, it is believed that the autonomous agent industry will usher in a broader development space and richer application scenarios.
The agent application layer covers two major applications: general and industry. General applications such as GitWit, GPT-Engineer, etc., bring new intelligent experiences to individual users, and seize the market with virality and unique use cases. Industry applications target vertical fields such as programming and marketing, and reduce costs and improve performance through business rules or data fine-tuning. At the service layer, users can use low-code or no-code platforms to build personalized agents, and platforms such as GitHub and Fiverr have begun to emerge, and agent transactions will be more active in the future. Multi-agent monitoring is an emerging field that is in demand by both enterprises and individuals, and the competition for integration and API capabilities is becoming increasingly fierce.
Domestic agent projects emerge one after another, indicating that AI Agent will usher in an explosive period. The real agent (agent) launched by R&M has powerful intelligent capabilities, which can understand and analyze complex business scenarios like a human, make flexible decisions based on the context, and automatically perform corresponding operations. This capability enables RPA to no longer be limited to simple process operations, but can go deep into the core of the business and achieve true intelligence.
In addition, the real agent can also convert the instructions of business users into automated process execution, and the user only needs simple instructions, it can autonomously disassemble tasks, and become a "generative intelligent digital employee who understands business", reducing work burden and improving work efficiency and accuracy.
In practical applications, the combination of AI Agent and RPA provides strong support for enterprises to achieve business intelligence and automation, and the intelligent TARS-RPA-Agent has been successfully applied to multiple industry scenarios, realizing the transformation from simple manual operation to intelligent automation. Combined with specific cases, the Agent digital employee created by the AGI large model + hyperautomation will effectively help Shandong Mastercard Holdings Co., Ltd., the "leader" of Shandong Construction Steel Market, to automate various business processes, so that business personnel can devote more time and energy to work that brings higher value to the company and is also more meaningful for personal growth and career development.
实在智能的TARS-RPA-Agent
In the field of agent building, developers face multiple challenges, so there is diversity in the choice of build paradigms.
Existing agent solutions range from proven tools to in-house developed and purpose-built for agents, and most of them are in the early stage or in testing. Traditional software solutions are difficult to deal with the agent problems caused by LLMs, such as the variability of debugging. As a result, developers tend to redesign with new frameworks and SDKs, rather than simply overlaying existing technologies. Some vendors are moving away from traditional logic to build an infrastructure designed for agents, including proprietary clouds for agents, to provide an ideal sandbox for programming and testing.
At the same time, a large number of SDKs, frameworks, libraries, and tools have emerged to support a full range of needs, from monitoring and analysis to front-end and large language model runtimes. Well-known frameworks such as OpenAI's Assistants API and Langchain have attracted attention for their functionality and ease of use. Despite fierce competition, some of the frameworks that have been questioned have shown strong vitality and demonstrated their unique value and potential in the field of agent building. With the advancement of technology and the expansion of applications, the AI Agent SDK, framework, and library will continue to evolve to provide developers with efficient and reliable solutions and promote the vigorous development of the intelligent twins industry.
开源Agent和闭源Agent
Open source agents are prominent in programming projects due to their openness and customizability, while closed source agents are leading in specific areas such as productivity and business intelligence due to their stability and professional support.
Agent products have penetrated into multiple industries, from programming frameworks to data analysis, highlighting the wide applicability and market potential of Agent technology. It is worth noting that the vertical field agent is still to be developed, which provides a broad space for entrepreneurs. With the agentization of traditional software, market competition will intensify, but it will also bring unlimited opportunities for developers. The panorama shows the vigorous development and diversification of the agent market, indicating that it will play an important role in more fields.