The competition for ecological discourse power, AI Agent framework standards have become a battleground

Whenever AI Agent frameworks and standards are mentioned, I believe many people have a confused feeling of being both devil and angel. Because the ceiling of making a framework is very high, it may reach 300M in a short period of 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, 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 closed data, and eventually runs interactive AIGC applications such as ChatGPT, Claud, DeepSeek, etc.

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, how to build a verifiable computational verification mechanism between agents, etc., these issues are inherently lacking.

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 Agent 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 web2 application market idea that relies purely on computing power and user experience.

Therefore, AI Agent frameworks and standards have become a battleground in this round of AI + Crypto narrative craze, and the imagination space is truly 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 listening to @VitalikButerin 10 years ago

Just like coming to China for a roadshow to seek financing. Imagine that Vitalik 10 years ago stood in front of you, how would you judge him?

1. Look at the charisma of the founder. This is consistent with the logic of most first-tier angel rounds. For example, when @shawmakesmagic was criticized for being a big mouth, if you saw his sincerity in being close to the community, you would cling to ai16z. For another example, when @KyeGomezB of Swarms was scammed by various FUDs, his consistent attitude of exploring technology impressed you.

2. Look at the technical quality. Although the facade can come from decoration, decoration also requires cost. A project with good technical quality is worthy of Fomo, worthy of "donation" mentality investment, and worthy of 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.;

3. Looking at the narrative logic, the AI Agent track currently has a narrative direction that is gradually "chaining". You will find that more and more old chains are embracing and supporting AI Agent narratives. Of course, the original major frameworks such as #ElizaOS, #arc, #Swarms, and #REI will also basically explore the possibility of "chaining"; for example, #Focai is a project built by the community to explore the "chaining" of the ElizaOS framework. Good narrative logic has its own potential, because it carries the expectations of the entire Crypto market. If a project jumps out to solve AI problems that even web2 cannot solve in the short term, do you dare to believe it?

4. Looking at the implementation of the ecosystem, the framework standard is indeed upstream. In most cases, it is best to have a single AI Agent and then abstractly propose a framework standard. For example, after #zerebro launched zerePy, the framework will empower the single AI, which is naturally stronger than issuing a new framework coin to split the consensus cohesion. However, no matter how big the pie is, it depends on the actual implementation of the AI Agent project (the team's execution and iteration speed), whether there is any subsequent performance such as ecosystem implementation, which is the lifeblood of the project's sustainable growth.

In short, the current battle between frameworks and standards is about who will be the EVM in the next round of AI Agent narrative and who will be the high-performance SVM that is better than EVM. Of course, in this process, a Cosmos IBC will emerge, and then a new DeFi paradigm of the Move system will be developed, and a parallel EVM and real-time large-scale concurrent layer2 will emerge... Think about how long this road will be?

Frameworks and standards will emerge in an endless stream, and each generation will be stronger than the previous one, making it difficult to make a decision.

I only look at the activity of developers and the actual delivery results of the project. If you can't deliver the results, the short-term surge is just empty talk. If you see "certainty", it's not too late to get on board. The ceiling valuation of AI Agent can be as high as the "public chain" level, and there may be a big opportunity of more than 10B, so there is no need to rush.

3) The boundaries between Ai Agent framework and standards are very vague. For example, the ElizaOS framework standard can only be defined as a spiritual totem of the developer community before it is platformized, and its value spillover can only be undertaken by #ai16z; for example, the #Game framework standard is still in the closed-source mode of #Virtual, which is slightly different from the mainstream open source combination architecture;

In addition, it is true that the ElizaOS framework is the most popular one, but there is an independent #ELIZA, and I don’t know how to bind it; the fundamentals of the #arcRIG framework are very good, but the application of the Rust language to the field of AI Agent to promote performance always feels too advanced; the technical quality of #Swarms is actually not bad, but the FUD and turbulent start and the panic-inducing situation are unexpected; the compatibility of blockchain determinism and Agent execution probability that #REI wants to solve is very interesting, but the technical direction is also too advanced, etc.

The above are some of the frameworks and standards with "technical quality" recognized by the market. There are many other projects such as Nexus, LangGraph, Haystack, AgentFlow, etc. There are too many projects claiming to be framework standards, but whether they focus on low-code convenient deployment, native multi-chain inheritance, and other enterprise-level customized business potential, or even AI Metaverse, etc.

All of these illustrate the "non-standard" nature of the current framework standards. For example, when Vitalik proposed to expand Ethereum, he explored various directions such as Plasma, Rollup, Validium, and Parallel, but in the end only Rollup became mainstream.