Author: jolestar Source: ai16z’s event in Beijing was to see what AI Agent can actually do now and think about what it can do in the future.

The current situation of AI Agent reminds me of that meme, there is a person hidden in the vending machine. The AI ​​Agent that everyone has imagined has begun to have autonomous consciousness, but in fact, there is actually a developer hidden inside the AI ​​Agent. (Everyone is making up the picture here. I tried to let AI generate this picture and found that AI cannot understand "hiding")

Basic working method of AI Agent framework

AI Agent framework Currently it plays the role of a glue, gluing the client (Twitter, Discord, Telegram, etc.) and various plug-ins (chains, etc.), and then the framework provides a basic library (memory storage, session isolation, context generation), etc. Later, it will be connected to various AI platform interfaces.

How to integrate the AI ​​Agent framework with applications and business scenarios

Since AI became popular last year, various platforms and tools have emerged. The most important thing is to solve a problem, how does AI Integrate with applications. Some AI platforms try to provide plug-ins, some create workflow models, and some traditional applications embed AI within the application. But the key here is: 1. Where is the interactive entrance of the application? 2. How to integrate AI with existing business logic.

The interaction portal for applications provided by each AI platform to users is a dialog box similar to a chat window. Obviously everyone believes that the interaction method with AI applications should be A kind of "anthropomorphic" way. The smart thing about AI Agent in this regard is that it directly connects to all open IM and social systems, which is obviously easier to accept than building a new one.

How to integrate AI with existing business logic. The solution provided by AI Agent allows developers to integrate AI decision-making into business scenarios. Programming languages ​​need to be deterministic. The condition of if can only be true or false and cannot handle fuzzy business logic. Through AI, complex logic can be converted into precise conditions, which can then be seamlessly integrated intoGo to the business scene.

For example, the function of replying to messages in a group needs to be triggered by traditional IM Bot through some clear message instructions, but through AI, a method shouldReplyMessage can be implemented to give him context, it returns true or false.

The main role of AI in business logic scenarios is:

1. "Intention" Discovery: Through the description in the prompt word, let AI discover the "intention" in the user's text message based on the context, and map the intention to specific code.

2. Assist decision-making: use AI to convert fuzzy and complex conditions into definite true/false or enumerated types, and then combine them into business logic.

Seeing this, many people may be disappointed with AI Agent. Many people think that AI Agent is just to teach AI and it will know everything. In fact, due to the problem of context limitations of large models, there is no way (at least currently) to create a universal AI that can do anything. But the good news is that programmers don’t have to worry about losing their jobs. There still needs to be a large number of programmers hidden behind AI, and someone still needs to stack if elses. But the key difference is that the business boundaries that the program can handle are expanding.

Two kinds of AI Agent

At the event, I asked @shawmakesmagic a question. The market has two expectations for AI Agent. 1. AI Agent plays a role by itself. Have its own ID, brand, and provide services to users. 2. The user has a personal AI Agent, which is equivalent to a personal assistant and can assist the user in handling some business. Which of these two AI Agents will be more popular? He felt that both directions would be good, and it was possible to combine them.

Now the market is mainly exploring the first direction. This direction is similar to the service AI agent. In the future, there may be no app interface. Apps will become AI agents and anthropomorphic. The second direction is to agent the application client. In the future, the application client will be a plug-in of the assistant Agent. The application local data becomes part of the Agent memory library. At the same time, this plug-in is also responsible for communicating with cloud services.Agent communication. And this is a new application architecture model that will change the entire infrastructure.

AI Agent’s requirements for infrastructure

1. The infrastructure must be Permissionless, otherwise the AI ​​Agent will be restricted by various anti-attack strategies. Services should be cost-effective (gas) to prevent attacks. At this point, platforms with a relatively low degree of openness will face a greater impact, and the open platform craze in the early days of Web2 will be rekindled.

2. AI Agent needs to be able to operate funds to pay to solve the above problems.

In other words, future services, whether based on blockchain or not, need to support Crypto's private key mode authentication and Crypto-based payment .

The combination of AI Agent and chain

In addition to the two points mentioned above, how to combine AI Agent with chain is a direction that everyone is exploring. At the event, chatting with @Mikkke_acc about focEliza it's working on. Of the two AI Agents mentioned earlier, at least the first one requires a running or verification environment provided by the chain. Because once an AI Agent provides services to the outside world, there will be trust issues, and its role is actually the same as a smart contract.

There was a controversy at the time about the name "smart contract". It was just a piece of code. What was "intelligent" about it? AI can make smart contracts worthy of their name. The difficult problem is how to call the AI ​​interface in a smart contract environment. If it is still a long way to run large models in a verifiable environment, using a solution like Oracle is a more feasible path.

There will be a lot of demands surrounding AI Agent. How to obtain the public knowledge of AI Agent? How does AI Agent determine facts? How does AI Agent identify the same user on different platforms? How is "memory" stored in smart contracts? If I have multiple devices, each with an AI Agent installed, how do they share memory?

You will find that the "data on-chain", relationship on-chain, DID, P2P network, etc. that were previously done in Web3 have new meanings and scene.

Conclusion

Reusing the conclusion I shared about AI and blockchain once in 21 years, an Internet that is more friendly to AI is also an Internet that is more friendly to humans. At that time, it was just a thought, but now in the future Already arrived.