Author: jolestar
Last week, I tinkered with AI Agents and attended the ai16z event in Beijing the day before yesterday. I wanted to see what AI Agents can actually do now and think about what they might be able to do in the future.
The current state of AI Agents reminds me of that meme where a person is hidden inside a vending machine. People have already imagined AI Agents as having autonomous consciousness, but in reality, there’s just a developer hidden inside the AI Agent. (Here, everyone can visualize the scene; I tried to get AI to generate this image but found that AI couldn't understand "hidden.")
Basic Working Mechanism of AI Agent Framework
The AI Agent framework currently acts as an adhesive, connecting clients (like Twitter, Discord, Telegram, etc.) with various plugins (across different chains), and then the framework provides a basic library (memory storage, session isolation, context generation, etc.), which later interfaces with various AI platform APIs.
How AI Agent Framework Integrates with Applications and Business Scenarios
Since the AI boom last year, various platforms and tools have emerged, and the key issue is how AI can integrate with applications. Some AI platforms attempt to provide plugins, some create workflow models, and some traditional applications embed AI within them. But the critical questions are: 1. Where is the interaction entry point for the application? 2. How does AI integrate with existing business logic?
The interaction entry point provided by various AI platforms for users is a chat window-like dialog box, and it’s clear that everyone believes the interaction with AI applications should be a "human-like" approach. The clever aspect of AI Agents is that they directly connect to all open IM and social systems, which is evidently more acceptable than creating a new one.
How does AI integrate with existing business logic? The solution provided by AI Agents is to allow developers to incorporate AI decision-making into business scenarios. Programming languages require determinism; the conditions in an if statement can only be true or false, and cannot handle ambiguous business logic. However, AI can convert complex logic into precise conditions, which can then be seamlessly integrated into business scenarios.
For example, the feature of replying to messages in a group chat traditionally requires explicit message commands to trigger, whereas with AI, a method like shouldReplyMessage can be implemented, providing it with context, and it returns true or false.
The role of AI in business logic scenarios mainly includes:
"Intent" Discovery: By using descriptions in prompts, AI can discover the "intent" in user text messages based on context and map that intent to specific code.
Assisting Decision-Making: AI can convert vague complex conditions into definite true/false or enumerated types, which can then be integrated into business logic.
At this point, many people might be disappointed with AI Agents, thinking that an AI Agent is just about teaching AI to do everything. In reality, due to the contextual limitations of large models, it’s impossible (at least currently) to create a universal AI that can do anything. But the good news is that programmers don’t need to worry about unemployment; AI will still require a large number of programmers behind the scenes, and someone will still need to write if-else statements, but the key difference is that the business boundaries that programs can handle are expanding.
Two Types of AI Agents
At the event, I asked Shaw a question about the market's expectations for AI Agents, which can be categorized into two types: 1. AI Agents acting as their own entities, with their own IDs and brands, providing services to users. 2. Users having personal AI Agents, akin to personal assistants, that can help users handle certain tasks. Which of these two types of AI Agents would be more popular? He believes both directions could be good and might even combine.
Currently, the main exploration in the market is still the first direction. This direction is similar to service AI Agentification; in the future, there may be no app interfaces, as all apps become AI Agents, anthropomorphized. The second direction involves the agentification of application clients, where future application clients will be plugins for assistant Agents, turning local application data into part of the Agent's memory, while this plugin also communicates with cloud service Agents. This represents a new application architecture model that will change the entire infrastructure.
Requirements for AI Agents in Infrastructure
The infrastructure must achieve permissionless access; otherwise, AI Agents will be restricted by various anti-attack strategies, and services should use economic costs (Gas) to prevent attacks. Platforms with lower levels of openness will face significant impacts, and the enthusiasm for open platforms from the early days of Web2 will be reignited.
AI Agents need to be able to operate funds for payment to address the above issues.
In other words, future services, whether based on blockchain or not, will need to support identity verification using Crypto's private key model and Crypto-based payments.
Integration of AI Agents with Chains
In addition to the two points mentioned above, how AI Agents integrate with chains is another direction that everyone is exploring. At the event, I chatted with Mikkke about the focEliza he is working on. The two types of AI Agents mentioned earlier, at least the first type, require a running or verification environment provided by the chain. Once an AI Agent offers services externally, trust issues arise, and the role it plays is essentially similar to that of a smart contract.
There was a debate about the name "smart contract" back in the day; it’s just a piece of code, so where is the "intelligence"? AI can make smart contracts live up to their name. The challenge is how to call AI interfaces within a smart contract environment. While running large models in a verifiable environment is still a long way off, using solutions similar to Oracle is a more practical path.
Moreover, many demands will arise around AI Agents. How can AI Agents access public knowledge? How do AI Agents determine facts? How do AI Agents recognize the same user across different platforms? How is "memory" stored in smart contracts? If I have multiple devices, each with an AI Agent, how do they share memory?
You will find that concepts like "data on-chain," relationship on-chain, DID, P2P networks, etc., that were explored in Web3, have new meanings and scenarios.
Conclusion
Reusing my conclusion from a 2021 talk about AI and blockchain: an internet that is more friendly to AI is also an internet that is more friendly to humanity. Back then, it was just a brainwave, but now the future has arrived.
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