As the field of AI agents evolves, the market has undergone a significant transformation from initially focusing solely on personalized agents. In the early days, people were drawn to agents that could entertain, tell jokes, or "set the mood" on social media. These agents indeed sparked discussions and interest, but as the market evolved, one fact became increasingly clear: practical value far outweighs personalization.
Many agents that emphasized personalization generated substantial attention upon launch but eventually faded from view due to their inability to provide value beyond superficial interaction. This trend highlights a key lesson: in the Web3 space, substantive value takes precedence over superficial effects, and practicality outweighs novelty.
This evolution mirrors the transformation in the Web2 AI space. Specialized large language models (LLMs) are continually being developed to address the specific needs of sectors like finance, law, and real estate. These models emphasize accuracy and reliability, compensating for the shortcomings of general AI.
The limitation of general AI is that it often can only provide "approximately" correct answers, which is unacceptable in certain scenarios. For example, a popular model might have an accuracy rate of only 70% on specific professional issues. This may be sufficient for everyday use, but it could lead to catastrophic consequences in high-risk scenarios such as court judgments or major financial decisions. This is why specialized LLMs that can achieve 98-99% accuracy through fine-tuning are becoming increasingly important.
So the question arises: why choose Web3? Why not let Web2 dominate the professional AI space?
Web3 has several significant advantages over traditional Web2 AI:


First is global liquidity. Web3 allows teams to raise funds more efficiently. Through token issuance, AI projects can directly access global liquidity, avoiding time-consuming VC meetings and negotiations. This approach democratizes funding, allowing developers to acquire the necessary resources more quickly.


Second is value accumulation through token economics. Tokens enable teams to reward early adopters, incentivize holders, and sustain the ecosystem's development. For example, Virtuals allocates 1% of transaction fees to cover inference costs, ensuring its agents remain functional and competitive without relying on external funding.


Third is decentralized AI infrastructure. Web3 provides open-source models, decentralized computing resources (such as Hyperbolic and Aethir), and vast open data pipelines (like Cookie DAO and Vana), offering developers a collaborative and cost-effective platform that is difficult to replicate in Web2. More importantly, it fosters a passionate developer community driving innovation.


Web3 AI Ecosystem
In the Web3 AI agent ecosystem, we see various ecosystems enhancing their capabilities by integrating new features, unlocking new application scenarios. From the Bittensor subnet to Olas, Pond, and Flock, these ecosystems are creating more interoperable and functional agents. Meanwhile, user-friendly tools like SendAI's Solana Agent Kit or Coinbase CDP SDK are continuously emerging.
The following ecosystems are building utility-first AI applications:


ALCHEMIST AI has developed a no-code AI application building platform.


MyShell has created an AI application store focused on image generation, visual novels, and virtual character simulation.



Questflow has launched a Multi-Agent Orchestration Protocol (MAOP) aimed at enhancing productivity application scenarios, with its integration with Virtuals creating a gamified airdrop and incentive management Santa Claus agent.



Capx AI has launched a utility-first AI application store on Telegram.



Individual agents focused on practical use cases
Outside the ecosystem, individual agents in specialized fields are also emerging. For example:

Corporate Audit AI serves as a financial analysis AI agent dedicated to reviewing reports and identifying market opportunities.




$CPA Agent, developed by Tj Dunham, focuses on calculating cryptocurrency taxes and generating reports for users.



This shift from "chatbots that chat idly on social media" to "experts sharing professional insights" will continue.
The future of AI agents lies not in chatbots that engage in casual conversation, but in expert agents in various professional fields that convey value and insights in engaging ways. These agents will continue to create shared thinking and guide users to practical products, whether they are trading terminals, tax calculators, or productivity tools.
Where will value concentrate?
The biggest beneficiaries will be agent-oriented L1s and coordination layers.


In terms of agent-oriented L1s, platforms like Virtuals and ai16z are raising industry standards, ensuring their ecosystems prioritize quality. Virtuals remains the top L1 platform in the agent space, while ai16z's launch platform will soon enter the competition. Purely personalized agents are fading away, replaced by agents that are both practical and appealing.


On the coordination layer front, platforms like Theoriq will orchestrate the collaboration of numerous agents, integrating their strengths to provide seamless and powerful solutions for users. Imagine integrating agents like aixbt, gekko, and CPA together to achieve alpha acquisition, trade execution, and tax processing in a unified workflow. Theoriq's task-based discovery framework is moving toward unleashing this collective intelligence.



Final thoughts
The narrative of utility-first AI applications is just beginning. Web3 has a unique opportunity to carve out a space where AI agents can not only entertain but also solve real problems, automate complex tasks, and create value for users. By 2025, we will witness a transition from chatbots to collaborative assistants, where specialized LLMs and multi-agent orchestration will redefine our perception of AI.
While Web2 and Web3 will gradually merge, the open and collaborative nature of Web3 will lay the foundation for the most innovative breakthroughs. It is no longer about "having a personality in AI agents," but about agents that can deliver practical value and create meaningful impact. Attention should be focused on agent-oriented L1s, coordination layers, and emerging AI applications. The era of agents has arrived, and this is just the beginning.

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