人工智慧代理的演進：從聊天機器人到專家助理

隨著人工智慧代理領域的發展，市場已經從最初只專注於個人化的代理商發生了巨大轉變。

As the field of AI agents has evolved, the market has shifted dramatically from agents that were initially focused on personalization alone. In the early days, people were attracted to agents that could entertain, tell jokes, or "create a buzz" on social media. These agents did generate buzz and attention, but as the market evolved, it became clear that utility value was far more important than personalization.

隨著人工智慧代理領域的發展，市場已經從最初只專注於個人化的代理商發生了巨大轉變。在早期，人們被那些可以娛樂、講笑話或在社群媒體上「引起轟動」的經紀人所吸引。這些代理商確實引起了轟動和關注，但隨著市場的發展，很明顯實用價值比個人化更重要。

Many agents that focused on personalization generated huge attention when they were launched, but eventually faded into obscurity because they failed to provide value beyond surface-level interactions. This trend highlights a key lesson: in the Web3 space, substantive value takes precedence over superficial effects, and practicality trumps novelty.

許多專注於個人化的智慧體在推出時引起了巨大的關注，但最終因為無法提供超越表面互動的價值而變得默默無聞。這趨勢凸顯了一個重要的教訓：在Web3領域，實質價值優先於表面效果，實用性勝過新穎性。

This evolution is in line with the transformation of the Web2 AI field. Specialized large language models (LLMs) are being developed to meet the specific needs of niche areas such as finance, law, and real estate. These models focus more on accuracy and reliability, making up for the shortcomings of general AI.

這種演變與Web2 AI領域的變革是一致的。專門的大語言模型 (LLM) 正在開發中，以滿足金融、法律和房地產等利基領域的特定需求。這些模型更注重準確性和可靠性，彌補了通用人工智慧的缺點。

The limitation of general AI is that it can often only provide "almost" answers, which is unacceptable in some scenarios. For example, a popular model may only be 70% accurate on a specific professional problem. This may be sufficient for daily use, but it can have disastrous consequences in high-stakes scenarios such as court decisions or major financial decisions. This is why professional LLMs that are finely tuned to achieve 98-99% accuracy are becoming increasingly important.

通用人工智慧的限制在於它往往只能提供「幾乎」的答案，這在某些場景下是不可接受的。例如，一個流行的模型對於特定的專業問題可能只有 70% 的準確率。這可能足以滿足日常使用，但在法院判決或重大財務決策等高​​風險場景中可能會產生災難性後果。這就是為什麼經過精心調整以達到 98-99% 準確率的專業法學碩士變得越來越重要。

So the question is: Why choose Web3? Why not let Web2 dominate the professional AI field?

那麼問題來了：為什麼選擇Web3？為什麼不讓Web2主導專業AI領域呢？

Web3 has several significant advantages over traditional Web2 AI:

與傳統的Web2 AI相比，Web3有幾個顯著的優點：

Web3 AI EcosystemIn the Web3 AI agent ecosystem, we see that each ecosystem improves its capabilities by integrating new features and opening up new application scenarios. From Bittensor subnets to Olas, Pond, and Flock, these ecosystems are building more interoperable and functional agents. At the same time, easy-to-use tools such as SendAI's Solana Agent Kit or Coinbase CDP SDK are also emerging.

Web3 AI生態系統在Web3 AI代理生態系統中，我們看到每個生態系統都透過整合新功能、開闢新應用場景來提升自身能力。從 Bittensor 子網路到 Olas、Pond 和 Flock，這些生態系統正在建立更具互通性和功能性的代理。同時，SendAI 的 Solana Agent Kit 或 Coinbase CDP SDK 等易於使用的工具也不斷出現。

These ecosystems are building AI applications that prioritize utility:

這些生態系統正在建立優先考慮實用性的人工智慧應用程式：

Individual agents focused on real use casesOutside the ecosystem, individual agents in specialized fields are also emerging. For example:

專注於真實用例的個體代理在生態系統之外，專業領域的個體代理也不斷出現。例如：

This shift from “chatbots chatting away on social media” to “experts sharing professional insights” is here to stay.

這種從「聊天機器人在社群媒體上聊天」到「專家分享專業見解」的轉變將會持續下去。

The future of AI agents lies not in chatbots that chat casually, but in expert agents in various professional fields that deliver value and insights in an engaging way. These agents will continue to create mindshare and guide users to actual products, whether it is a trading terminal, tax calculator or productivity tool.

人工智慧代理的未來不在於隨意聊天的聊天機器人，而是各個專業領域的專家代理，以引人入勝的方式提供價值和見解。這些代理商將繼續創造思想共享並引導用戶使用實際產品，無論是交易終端、稅務計算器還是生產力工具。

Where will value be concentrated?The biggest beneficiaries will be the proxy L1 and coordination layers.

價值會集中在哪裡？

Final ThoughtsThe narrative of AI applications that prioritize practicality has just begun. Web3 has a unique opportunity to carve out a space where AI agents can not only entertain, but also solve practical problems, automate complex tasks, and create value for users. 2025 will witness the transition from chatbots to collaborative assistants, and specialized LLMs and multi-agent orchestration will redefine the perception of AI.

最後的想法 以實用性為優先的人工智慧應用的敘述才剛開始。 Web3 有一個獨特的機會來開闢一個空間，讓人工智慧代理不僅可以娛樂，還可以解決實際問題、自動化複雜任務並為用戶創造價值。 2025 年將見證從聊天機器人到協作助理的轉變，專業的法學碩士和多代理編排將重新定義人工智慧的認知。

While Web2 and Web3 will gradually merge, the open, collaborative nature of Web3 will lay the foundation for the most innovative breakthroughs. It is no longer about "AI agents with personality", but about agents that can provide practical value and create meaningful impact. It is worth paying attention to agentic L1, coordination layer, and emerging AI applications. The era of agency has arrived, and this is just the beginning.

雖然 Web2 和 Web3 將逐漸融合，但 Web3 的開放、協作性質將為最具創新性的突破奠定基礎。它不再是“有個性的人工智慧代理”，而是能夠提供實用價值並產生有意義影響的代理。值得關注的是代理 L1、協調層和新興的人工智慧應用。代理時代已經到來，而這只是開始。