Behind the battle over AI Agent frameworks and standards: How to judge the quality of projects?

Whenever AI Agent frameworks and standards are mentioned, I believe many people have a sense of confusion between demons and angels. Because the ceiling of the framework is very high, it may reach 300M in a short time, but once it is not worthy of its reputation, the consensus collapses, and the probability of falling into the abyss is also very high. So, why has the AI ​​Agent framework standard become a battleground for strategists, and how to judge whether the framework standard is worth investing in? Below, I would like to share my personal understanding for reference:

1) AI Agent itself is a product of the pure web2 Internet context. The LLMs large model is trained through a large amount of data closure, and finally runs interactive AIGC applications such as ChatGPT, Claud, and DeepSeek.

Its overall focus is on the "application" logic. As for how agents communicate and interact with each other, how to establish a unified data exchange protocol between agents, and how to build a verifiable calculation verification mechanism between agents, etc., there is an inherent lack of such issues.

The AI ​​Agent expansion framework and standards are essentially the evolution of the web3 distributed architecture from centralized servers to decentralized collaborative networks, from closed ecosystems to open unified standard protocols, and from single AI Agent applications to complex linkage ecosystems.

The core logic is this: AI Agents must seek commercial prospects under the modularization and chaining ideas of web3, and must build a distributed architecture that conforms to the web3 framework with the "framework standard" as the starting goal, otherwise it will be a pure web2 application market idea of ​​competing in computing power and user experience.

Therefore, the AI ​​Agent framework and standards have become a battleground for this round of AI + Crypto narrative craze, and the imagination space is really beyond words.

2) AI Agent frameworks and standards are in a very early stage. It is no exaggeration to say that listening to developers talk about their technical vision and practice routes now is no different from @VitalikButerin coming to China for a roadshow to seek financing 10 years ago. Imagine that Vitalik 10 years ago stood in front of you, how would you judge him?

1. Look at the charm of the founder. This is consistent with the logic of most first-level angel round "investors". For example, when @shawmakesmagic was called a big mouth, if you saw his sincerity in laughing and scolding and being close to the community, you would hold ai16z's thigh tightly; for example, when Swarms' @KyeGomezB was defrauded by various FUD scams, his consistent attitude of exploring technology was impressive, etc.;

2. Look at the technical appearance. Although the facade can come from decoration, decoration also requires costs. A project with good technical appearance is worthy of Fomo, worthy of "donation" mentality investment, and worthy of energy follow-up research. For example: Github code quality, developer open source community Reputation, whether the technical architecture logic is self-consistent, whether the technical framework has been applied, the hard-core content of the technical white paper, etc.;