In 1966, Joseph Weizenbaum, a computer scientist at MIT, created a program called ELIZA, which was able to mimic user input text through some rules and language structures to achieve simple human-computer interaction. Although ELIZA's functionality was very basic and it did not have true understanding, it sparked people's infinite imagination about the potential of human-computer interaction, and this pioneering experiment left a glimpse in the chapter of natural language processing, becoming one of the starting points of modern artificial intelligence development.
Fast forward to 2024, the name "Eliza" has reappeared in the context of Web3 as a hot topic of discussion. This time, it is no longer a small simulation dialogue tool, but the underlying framework that supports the construction and operation of intelligent agents in the Web3 world.
It allows developers to quickly create multi-functional AI agents, which can automatically complete transactions, execute governance tasks, and even analyze on-chain data in real-time, and is expected to fundamentally change the way people interact with blockchains.
Pic Source: https://elizaos.ai
Why did Eliza suddenly become popular?
The technology is an old trick, but the trend is a new story
The rise of AI agents is driven by the "century-old marriage" of AI and blockchain. From technical concepts to practical applications, it has become a hot spot in the crypto field. These agents are not only tools, but also "independent economic entities". Through autonomous task execution, they are redefining "participation" and "value" in Web3. Just as ELIZA opened the door to human-computer interaction in the past, today's Eliza framework is also reshaping the relationship between humans and on-chain networks.
A new decentralized participant
Another attraction of AI agents is their seamless integration with the decentralized economy. Relying on the Eliza framework, developers can build powerful agents, such as ai16z - a virtual venture capital fund that can analyze and summarize the information and files exchanged by users in special social channels in real-time, and make investment decisions based on different message weights to complete on-chain interactions.
This model shows a new possibility: humans are not the only economic participants, and agents can also become key nodes in the value chain.
Pic Source: https://elizaos.ai
From framework to function: the behind-the-scenes engine of AI agents
The AI Agent framework is the core tool for building and running AI agents, allowing developers to quickly deploy agents, while users can access unprecedented services and value through these agents. From investment assistance to content generation, the functions of agents are rapidly expanding, and the underlying framework has become a key driving force for innovation in the crypto industry. Compared to NFTs or memes, agents may exhibit stronger long-term value potential due to their functionality and sustainable business models.
An interesting example is how the Eliza framework enables ai16z to achieve automated investment. Based on the Eliza framework, ai16z created a virtual venture capital fund, using agents to drive investment decisions. It is not just simple buying and selling operations, but using machine learning models to analyze on-chain data and provide real-time insights to the community. Eliza, with its modular design and open-source ecosystem, covers areas such as social integration, asset issuance, and analytical insights, demonstrating the diverse potential of the AI Agent framework. This not only improves the efficiency of on-chain governance, but also has the potential to push the concept of decentralized autonomy to new heights.
The "Elizas" in Web3: What trends might they ignite?
Although the agent market is still in its early stages, with the widespread adoption of AI frameworks like Eliza, their potential cannot be ignored. As the AI Agent framework continues to evolve, we can foresee the following trends:
New economic forms of agents
These AI Agents not only can execute tasks, but also can dynamically adjust their operations based on real-time data, such as optimizing network resource allocation, saving money and making money at the same time.
Strengthening of on-chain communities
Agents can provide on-chain communities with real-time information integration and analysis support, helping users participate in on-chain governance and economic activities more quickly and accurately.
Cross-integration of technologies
With the integration of the AI Agent framework with decentralized storage and computing platforms, the functions of these agents will become more diversified. From financial services to on-chain games, agents may be ubiquitous.
The rise of agents not only changes the way users interact with technology, but also brings new asset categories. These digital entities are gradually forming a tradable and investable market form. Similar to NFTs, agents provide users with direct economic benefits through tokenization, while attracting more long-term investors through innovative functional services.
As with every wave of technological change in history, the AI Agent framework is transforming complex technologies into simple and easy-to-use tools, opening up new frontiers for Web3.
But even with a clear and focused direction, AI agents face considerable challenges:
Security issues: How to prevent agents from being exploited by hackers and avoid asset losses?
Centralization risks: Although agents are decentralized, the development and control of the framework is still in the hands of a few teams.
Ethical controversies: When agents make independent decisions, who is responsible if something goes wrong?
From observer to participant, why AI agents are worth your attention
AI agents are driving Web3 into a new stage, not only improving efficiency, but also redefining the concept of "participation". If DeFi was the protagonist of the last wave, then AI agents may become the core driving force of the next decade.
As AI expert Andrew Ng said, the value of AI lies in its "task-oriented" nature - focusing on completing high-value tasks, improving efficiency and benefiting users.AI agents are an extension of this idea:they simplify on-chain operations, allowing ordinary users to participate efficiently.
Rather than just being an observer, it's worth considering: when humans and agents coexist on the chain, how will you interact with them?
