Whenever the AI Agent framework and standards are mentioned, many people likely feel a mix of confusion and excitement, akin to the duality of a devil and an angel. This is because the ceiling for creating frameworks is very high; one might quickly reach 300M in a short time, but if the framework fails to live up to its name and consensus collapses, the probability of falling into the abyss is also quite high. So, why has the AI Agent framework standard become a battleground for competition, and how can we assess whether a framework standard is worth investing in? Below, I share my personal understanding for reference:

1) The AI Agent itself is a product born purely from the web2 internet context. LLMs (Large Language Models) are trained on vast amounts of data, ultimately leading to interactive AIGC applications like ChatGPT, Claude, DeepSeek, and others.

Its overall focus is on the "application" logic. However, it inherently lacks answers to questions such as how Agents communicate and interact, how to establish a unified data exchange protocol among Agents, and how to build verifiable computational verification mechanisms.

The expansion of AI Agent frameworks and standards essentially represents an evolution from centralized servers to decentralized collaborative networks, from closed ecosystems to open unified standard protocols, and from single AI Agent applications to complex interconnected ecosystems in a web3 distributed architecture.

The core logic is simple: AI Agents must seek commercial prospects under the modular and chain-based thinking of web3. Starting with "framework standards," they need to build a distributed architecture that aligns with web3 frameworks; otherwise, it would merely be a web2 application market focused on computing power and user experience.

Thus, the AI Agent framework and standards have become a crucial battleground in this wave of AI + Crypto narrative, with immense imaginative potential.

2) The AI Agent framework and standards are still in a very early stage. It is not an exaggeration to say that hearing various developers talk about their technical visions and practical routes now is akin to @VitalikButerin seeking funding in China a decade ago. Imagine if Vitalik stood in front of you ten years ago; how would you assess him?

1. Look at the founder's charisma, which aligns with the logic of most first-round angel investments focused on "people." For instance, when @shawmakesmagic was criticized for being outspoken, if you saw his genuine engagement with the community, you would want to support ai16z. Similarly, @KyeGomezB from Swarms maintained a consistent technical discussion attitude despite various FUD and scams; would that resonate with you?

2. Assess the technical quality. While the facade can come from decoration, it also incurs costs. A project with good technical quality is worth FOMO, worth investing with a "donation" mindset, and worth the effort to follow up and research. For example: the quality of GitHub code, the reputation of the developer open-source community, whether the technical architecture is logically coherent, whether the technical framework has already been applied, and the rigor of the technical white paper content, etc.;

3. Evaluate the narrative logic. Currently, there is a narrative direction in the AI Agent space that is gradually becoming "chain-based." You will find that more and more old chains are embracing support for AI Agent narratives. Of course, the original frameworks like #ElizaOS, #arc, #Swarms, and #REI are also exploring the possibility of "chain-based" approaches. For instance, #Focai is a project built by the community exploring the "chain-based" construction of the ElizaOS framework. A good narrative logic carries inherent momentum, as it embodies the expectations of the entire Crypto market. If a project emerges claiming to solve AI problems that even web2 cannot address in the short term, would you believe it?

4. Look at ecological implementation. Framework standards are indeed very upstream. In most cases, it is best to abstract the framework standards after having a standalone AI Agent. For example, after #zerebro, zerePy was launched; the framework empowers the standalone AI, which is naturally stronger than launching a new framework token to split consensus cohesion. However, regardless of how grand a framework and standard are presented, the actual implementation of AI Agent engineering (the team's execution capability and iteration speed) and whether there is ecological implementation are crucial for the project's sustainable growth.

In summary, the current competition over frameworks and standards is about determining who will be the next EVM in the AI Agent narrative and who will be a high-performance SVM superior to EVM. Of course, during this process, a Cosmos IBC may emerge, along with a new DeFi paradigm based on Move, leading to a parallel EVM and real-time large-scale concurrent layer2… Just think about how long this road still is.

Frameworks and standards will continue to emerge, each stronger than the last, making it difficult to make judgments.

I only look at the developers' activity levels and the actual delivery results of the projects. If they cannot deliver results, a short-term surge is merely illusory. If I see "certainty," it is not too late to get on board. The valuation ceiling for AI Agents can reach "public chain" levels, with potential opportunities exceeding 10B, so there is no need to rush.

3) The boundaries of AI Agent frameworks and standards are quite blurred. For example, the ElizaOS framework standard can only be qualitatively defined as a spiritual totem of the developer community before platformization, and its value spillover can only be supported by #ai16z. Similarly, the #Game framework standard is still playing in a #Virtual closed-source mode, appearing somewhat alternative compared to mainstream open-source composite architectures.

Moreover, while the ElizaOS framework is indeed a hot topic, there is an independent #ELIZA that is unclear about how to establish a relationship with it. The fundamentals of the #arcRIG framework are solid, but applying Rust language to the AI Agent field to enhance performance feels overly advanced. The technical quality of #Swarms is not bad, but such a tumultuous start and the panic-inducing situation have caught many off guard. The compatibility of blockchain determinism and Agent execution probability that #REI aims to solve is very interesting, but the technical direction also seems too advanced, among other issues.

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

All of this indicates the current "no standard" characteristic of framework standards, akin to how Vitalik proposed expanding Ethereum into various exploratory directions like Plasma, Rollup, Validium, and Parallel, but ultimately only Rollup became mainstream.

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