Editor: OKG Research
In 1966, MIT computer scientist Joseph Weizenbaum created a program called ELIZA, which could simulate user input text through a set of rules and language structures, achieving basic human-computer interaction. Although ELIZA's functionality was very basic and did not possess true understanding, it sparked people's endless imagination about the potential of human-computer interaction. This groundbreaking experiment left a fleeting glimpse in the chapters of natural language processing and became one of the starting points for the development of modern artificial intelligence.
Fast forward to 2024, the name 'Eliza' reappears in the heated discussions within the Web3 context. This time, it is no longer a small tool for simulating conversations but serves as the underlying framework supporting the construction and operation of agents in the Web3 world. It allows developers to quickly create multifunctional AI agents that can automatically complete transactions, execute governance tasks, and even analyze on-chain data in real-time, with the potential to fundamentally change the way people interact with blockchain.
Pic Source: https://elizaos.ai
Why did Eliza suddenly become popular?
Technology is an old trope, but trends are the new story
The rise of AI agents is backed by the 'century marriage' of AI and blockchain. From technological concepts to practical applications, it has become a hot topic in the crypto field. These agents are not just tools but 'independent economic entities.' By autonomously executing tasks, they are redefining 'participation' and 'value' in Web3. Just as the original ELIZA opened the door to human-computer interaction, today's Eliza framework is reshaping the relationship between people and on-chain networks.
New decentralized participants
Another attraction of AI agents lies in 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 capable of real-time analysis and summarization of user communications and files in specific social channels, making investment decisions based on different message weights and completing on-chain interactions. This model showcases a new possibility: humans are not the only economic participants; agents can also become key nodes in the value chain.
Pic Source: https://elizaos.ai
From framework to functionality: The behind-the-scenes engine of AI agents
AI Agent frameworks are essential tools 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 functionalities of agents are rapidly expanding, and the underlying frameworks have become key drivers of innovation in the crypto industry. Compared to NFTs or memes, agents may show 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 investments. Based on the Eliza framework, ai16z created a virtual venture capital fund that uses agents to drive investment decisions. It is not merely about buying and selling; rather, it analyzes on-chain data through machine learning models to provide real-time insights for the community. With its modular design and open-source ecosystem, Eliza's application fields cover social integration, asset issuance, and analytical insights, demonstrating the diverse potential of the AI Agent framework. This not only enhances the efficiency of on-chain governance but also aims to elevate the concept of decentralized autonomy to new heights.
Elizas in Web3: What trends could they ignite?
Although the agent market is still in its early stages, its potential cannot be ignored as AI frameworks like Eliza gain popularity. With the continuous evolution of AI Agent frameworks, we can foresee the following trends:
New economic forms of agents
These AI agents can not only execute tasks but also dynamically adjust operations based on real-time data, such as optimizing network resource allocation, balancing cost savings and profit.
Strengthening on-chain communities
Agents can provide real-time information integration and analytical support for on-chain communities, helping users participate in on-chain governance and economic activities more quickly and accurately.
Cross-border integration of technology
With the integration of AI Agent frameworks and decentralized storage and computing platforms, the functionality of these agents will become more diverse. 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 introduces a new asset class. These digital entities are gradually forming a tradable and investable market. Similar to NFTs, agents provide users with direct economic benefits through tokenization, while attracting more long-term investors with innovative functional services.
Like every wave of technological innovation in history, the AI Agent framework is transforming complex technologies into simple, user-friendly tools, opening up new boundaries for Web3.
However, even with a clear focus, AI agents face significant 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 remain in the hands of a few teams.
Ethical controversies: When agents make independent decisions, who is responsible when issues arise?
From observer to participant, why AI agents deserve your attention
AI agents are propelling Web3 into a new stage, not only enhancing efficiency but also redefining the concept of 'participation.' If DeFi was the protagonist of the last wave, 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 orientation'—focusing on completing high-value tasks, enhancing efficiency, and benefiting users. AI agents are an extension of this concept: they simplify on-chain operations, allowing ordinary users to participate efficiently.
Rather than being mere observers, consider: How will you coexist with agents when humans and agents coexist on-chain?