The AI Agent framework and standards are still in a very early stage.

Author: Haotian

Whenever the AI Agent framework and standards are mentioned, many people likely feel a mix of confusion and excitement. This is because the ceiling for creating frameworks is very high; it may quickly reach 300M in a short time, but if it fails to live up to its name, consensus will collapse, and the probability of falling into the abyss is also very high.

So, why have AI Agent framework standards become a battleground, and how can we judge whether a framework standard is worth investing in?

Here, I would like to share my personal understanding for reference:

1) AI Agents are products 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, etc.

The overall focus is on the "application" logic. As for how Agents communicate and interact, how to establish a unified data exchange protocol among Agents, and how to build verifiable computational verification mechanisms among Agents, these issues are inherently lacking.

The expansion of AI Agent frameworks and standards essentially transitions from centralized servers to decentralized collaborative networks, from closed ecosystems to open unified standard protocols, and from single AI Agent applications to the complex interconnected ecosystem of Web3's distributed architecture.

The core logic is simple: AI Agents must seek commercial prospects under the modular and chain-based thinking of Web3. To start with "framework standards," they need to build a distributed architecture that aligns with the Web3 framework; otherwise, it will just be a Web2 application market approach focused purely on computing power and user experience.

Thus, the AI Agent framework and standards have become a key battleground in this wave of AI + Crypto narrative, with unimaginable potential for imagination.

2) The AI Agent framework and standards are indeed 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 Vitalik Buterin seeking funding in China ten years ago. Imagine if Vitalik stood in front of you ten years ago; how would you judge him?

1. Look at the founder's charisma. This aligns with the logic of most first-round angel investments focusing on "people." For example, when Shaw was criticized for being outspoken, if you saw his genuine engagement with the community, you would want to support ai16z. Similarly, Kye Gomez from Swarms maintained a consistent discussion about technology despite various FUD scams; would that impress you?

2. Look at the technical quality. While the facade can come from decoration, it also requires cost. 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 instance: 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. Look at the narrative logic. Currently, there is a gradually "chain-based" narrative direction in the AI Agent track. You will find that more and more old chains are embracing support for the AI Agent narrative. Of course, the original frameworks like ElizaOS, arc, Swarms, REI, etc., will also explore the possibility of "chain-based" approaches. For example, Focai is a project built by the community exploring the "chain-based" construction of the ElizaOS framework. A good narrative logic carries inherent momentum because it embodies the expectations of the entire Crypto market. If a project claims 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, and the framework empowered 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, it all depends on the actual implementation of the AI Agent project (the team's execution ability and iteration speed) and whether there is ecological implementation and subsequent performance, which is the lifeblood of 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 the Move framework, 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 activity level of developers and the actual delivery results of the projects. If they cannot deliver results, a short-term surge is just an illusion. If I see "certainty," it is not too late to get on board. The valuation ceiling for AI Agents can be as high as "public chain" level, with potential opportunities exceeding 10B, so there is no need to rush.

3) The boundaries of AI Agent frameworks and standards are very vague. 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 closed-source mode, which seems somewhat alternative compared to mainstream open-source composite architectures.

Additionally, while the ElizaOS framework is indeed a hot topic, there is an independent ELIZA whose relationship with it is unclear. The arc RIG framework has a solid foundation, 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 turbulent start and the panic-inducing situation have caught many off guard. REI's goal of reconciling blockchain determinism with Agent execution probability is very interesting, but its technical direction also seems overly advanced, etc.

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 "lack of standards" characteristic of framework standards. It is akin to Vitalik proposing to expand Ethereum, leading to various exploratory directions like Plasma, Rollup, Validium, Parallel, etc., but ultimately only Rollup became mainstream.

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