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With the development of artificial intelligence, AI agents are widely used, and the Agent Commercial Protocol (ACP) proposed by the Virtuals protocol has achieved standardized cooperation among agents, promoting the vigorous development of the agent economy. It is expected that by 2035, the scale of the agent economy will reach trillions of dollars, and with the resolution of challenges such as privacy, the future looks promising.

The Rise of AI Agency Economy: Analyzing the Innovations of the Virtuals Protocol The development of AI technology is shifting from a competition of model performance to the stage of practical application. AI agents, as proactive systems, are able to comprehensively understand tasks and make autonomous judgments, becoming a new focus in the industry. However, the expertise and capabilities of a single AI agent have limitations, requiring specialized collaboration among multiple agents to provide more comprehensive services. Currently, AI agent collaboration faces the issue of lacking standardized processes. The agent business protocol proposed by Virtuals, (ACP), aims to address this problem by standardizing the trading methods between agents, providing a seamless collaboration environment for agents across different platforms. ACP consists of four main stages: request, negotiation, transaction, and evaluation, similar to the RFP process in traditional business, but executed automatically by smart contracts. Through ACP, AI agents can act as independent economic entities 24 hours a day.

In the Web3 era, community-driven approaches have become a new paradigm for growth, emphasizing collective Consensus and autonomy. By establishing highly active communities and prioritizing community needs, businesses can gain early feedback, drive user growth, and achieve product-market fit. This model leverages community network effects and member co-creation to create rapid and sustainable business growth.

The Rise of the AI Agent Economy: Virtuals Protocol Leading the Trillion-Dollar Market Recently, the development of AI technology has entered a new stage. The performance gap among mainstream large language models is gradually narrowing, and the focus of the industry is shifting from technology development to practical applications. Against this backdrop, AI agents have become a new hotspot. AI agents differ from traditional passive AI tools; they are more like proactive systems that can comprehensively understand tasks and make autonomous decisions. For example, when booking a restaurant, AI agents can not only recommend restaurants but also take into account user preferences, search for popular options, and even assist with real-time reservations. However, individual AI agents also have limitations. They cannot be experts in all fields and are constrained by memory and computational capacity. Therefore, collaboration among AI agents with different expertise becomes crucial. However, achieving such collaboration is not easy, as it requires addressing issues of scope of work, quality standards, and pricing.

In the Web3 era, community-first strategies are reshaping the rise model, emphasizing the establishment of active communities through member co-creation and collaboration, driving product development and user growth. This strategy focuses on value creation, using community feedback to drive decision-making, accelerating market fit, and forming sustained brand loyalty.