Why have AI Agent Framework Standards Become a Battleground, and How to Assess Whether They Are Worth Investing In?

Intermediate1/14/2025, 9:00:04 AM
Discussions about AI Agent framework standards often evoke mixed feelings—viewed both as potential breakthroughs and significant risks. Frameworks in this sector have the potential to rapidly appreciate, possibly reaching revenues of $300 million swiftly. However, if they fail to deliver on their promises, the loss of consensus can lead to dramatic declines. This article explores why AI Agent framework standards have become a critical competitive field and offers insights on evaluating their investment potential.

Discussions about AI Agent framework standards often evoke mixed feelings—viewed both as potential breakthroughs and significant risks. Frameworks in this sector have the potential to rapidly appreciate, possibly reaching revenues of $300 million swiftly. However, if they fail to deliver on their promises, the loss of consensus can lead to dramatic declines. This article explores why AI Agent framework standards have become a critical competitive field and offers insights on evaluating their investment potential. Below, I would like to share my personal understanding for reference:

1) AI Agents themselves originated in a purely web2 internet context, where large LLM models are trained on extensive data in a closed environment, ultimately producing interactive AI-generated content applications like ChatGPT, Claud, and DeepSeek.

Their focus is primarily on “application” logic. Aspects such as how agents communicate with each other, how they establish unified data exchange protocols, or how they build verifiable computational verification mechanisms are inherently lacking.

Expanding AI Agent frameworks and standards essentially involves moving from centralized servers to decentralized collaborative networks, from closed ecosystems to open unified standard protocols, and from single AI Agent applications to complex interconnected web3 distributed architectures.

The core logic is simple: AI Agents must seek commercial prospects under web3 modular, blockchain-based thinking, starting with “framework standards” to build a distributed architecture compatible with web3. Otherwise, it’s just a web2 approach focused solely on computing power and user experience.

Thus, AI Agent frameworks and standards have become the battleground in this round of AI +Crypto narratives, with unimaginable potential.

2) AI Agent frameworks and standards are in a very early stage. Frankly, listening to various developers talk about their technical visions and practical routes is akin to 10 years ago when @VitalikButerin was touring China seeking funding. Imagine, if Vitalik stood before you 10 years ago, how would you judge?

  1. Look at the charisma of the founders, which aligns with the logic of most early angel rounds of “investing in people.” For example, if you saw @shawmakesmagic‘s sincerity when he was criticized for being outspoken, you’d cling to ai16z; or, if you were moved by Swarms’ @KyeGomezB‘s consistent focus on technology despite various FUD and scams;

2.Evaluate the technical appearance, although facade can come from decoration, and decoration costs. A project with good technical appearance deserves Fomo, a “donation” mentality for investment, and effort spent on follow-up research. For instance: Github code quality, developer community reputation, whether the technical architecture is logically consistent, whether the technical framework has been applied, the technical depth of the white paper, etc.;

  1. Consider the narrative logic. The AI Agent track currently has a gradually “blockchainizing” narrative direction. You’ll find that more old chains are embracing support for the AI Agent narrative. Of course, originally big framework directions like #ElizaOS, #arc, #Swarms, #REI are also exploring the possibility of “blockchainization”; for example, #Focai is a community project exploring the “blockchainization” of the ElizaOS framework. Good narrative logic carries momentum because it bears the expectations of the entire Crypto market. If a project suddenly claims to solve AI problems that even web2 can’t solve in the short term, would you believe it?

  2. Observe ecosystem implementation. Framework standards are indeed upstream, and in most cases, it’s best to abstract the framework standards after having a single AI Agent. For example, #zerebro later launched zerePy, empowering single AI with a framework, naturally stronger than launching a new framework coin to split consensus. But an out-of-the-blue framework and standard, no matter how big the promise, needs to be evaluated based on actual AI Agent engineering implementation (team’s execution and iteration speed) and whether there is ecosystem implementation. This is vital for sustainable growth of the project.

In conclusion, today’s battle over frameworks and standards is about locking down who will be the next round’s EVM in the AI Agent narrative, who will be a better-performing SVM than EVM. And during this process, if a Cosmos IBC emerges, a new DeFi paradigm with Move, or parallel EVMs, real-time massive concurrency layer2… Just think how long this road is?

Frameworks and standards will keep emerging, each generation stronger than the last. It’s hard to make a choice.

I only look at the activity level of developers and the actual implementation results of the project. If they can’t deliver, short-term spikes are just voids. If “certainty” is seen, it’s not too late to get on board. The ceiling valuation of AI Agents can be as high as “public chain” levels, possibly over $10 billion in big opportunities, so there’s no need to rush.

3) The boundaries between AI Agent frameworks and standards are very blurry. For instance, the ElizaOS framework standard can only be qualitatively described as a spiritual totem of the developer community before it becomes platformized, with its value spill-over only supported by #ai16z; or #Game framework standards still play under the closed-source model of #Virtual, somewhat atypical compared to mainstream open-source combined architectures;

Moreover, while ElizaOS framework is undeniably a top player, it has an independent #ELIZA, and it’s unclear how it’s tied together; #arc RIG framework has a solid foundation, but applying Rust language to the AI Agent field to boost performance feels too advanced; #Swarms‘s technical appearance is actually not bad, but such a FUD-ridden and turbulent start and a panic-inducing situation was unexpected; #REI‘s exploration of blockchain determinism and Agent execution probability compatibility is very interesting but also too advanced, etc.

These are still some of the “technically appealing” frameworks and standards recognized by the market, not to mention many such as Nexus, LangGraph, Haystack, AgentFlow, etc., claiming to be framework standards, but whether it’s about low-code convenient deployment, native multi-chain inheritance, or other enterprise-level customized business potential, even AI Metaverse, etc.

This all illustrates the current “no standards” nature of framework standards, much like Vitalik’s proposal to expand Ethereum led to various explorations like Plasma, Rollup, Validium, Parallel, but in the end, only Rollup became mainstream.

Disclaimer:

  1. This article is reproduced from [Haotian | CryptoInsight]. The copyright belongs to the original author [@tmel0211]. If there are any objections to the reproduction, please contact the Gate Learn team, and they will handle it promptly.
  2. Disclaimer: The views and opinions expressed in this article are solely those of the author and do not constitute any investment advice.
  3. The Gate Learn team translated the article into other languages. Copying, distributing, or plagiarizing the translated articles is prohibited unless mentioned.

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Why have AI Agent Framework Standards Become a Battleground, and How to Assess Whether They Are Worth Investing In?

Intermediate1/14/2025, 9:00:04 AM
Discussions about AI Agent framework standards often evoke mixed feelings—viewed both as potential breakthroughs and significant risks. Frameworks in this sector have the potential to rapidly appreciate, possibly reaching revenues of $300 million swiftly. However, if they fail to deliver on their promises, the loss of consensus can lead to dramatic declines. This article explores why AI Agent framework standards have become a critical competitive field and offers insights on evaluating their investment potential.

Discussions about AI Agent framework standards often evoke mixed feelings—viewed both as potential breakthroughs and significant risks. Frameworks in this sector have the potential to rapidly appreciate, possibly reaching revenues of $300 million swiftly. However, if they fail to deliver on their promises, the loss of consensus can lead to dramatic declines. This article explores why AI Agent framework standards have become a critical competitive field and offers insights on evaluating their investment potential. Below, I would like to share my personal understanding for reference:

1) AI Agents themselves originated in a purely web2 internet context, where large LLM models are trained on extensive data in a closed environment, ultimately producing interactive AI-generated content applications like ChatGPT, Claud, and DeepSeek.

Their focus is primarily on “application” logic. Aspects such as how agents communicate with each other, how they establish unified data exchange protocols, or how they build verifiable computational verification mechanisms are inherently lacking.

Expanding AI Agent frameworks and standards essentially involves moving from centralized servers to decentralized collaborative networks, from closed ecosystems to open unified standard protocols, and from single AI Agent applications to complex interconnected web3 distributed architectures.

The core logic is simple: AI Agents must seek commercial prospects under web3 modular, blockchain-based thinking, starting with “framework standards” to build a distributed architecture compatible with web3. Otherwise, it’s just a web2 approach focused solely on computing power and user experience.

Thus, AI Agent frameworks and standards have become the battleground in this round of AI +Crypto narratives, with unimaginable potential.

2) AI Agent frameworks and standards are in a very early stage. Frankly, listening to various developers talk about their technical visions and practical routes is akin to 10 years ago when @VitalikButerin was touring China seeking funding. Imagine, if Vitalik stood before you 10 years ago, how would you judge?

  1. Look at the charisma of the founders, which aligns with the logic of most early angel rounds of “investing in people.” For example, if you saw @shawmakesmagic‘s sincerity when he was criticized for being outspoken, you’d cling to ai16z; or, if you were moved by Swarms’ @KyeGomezB‘s consistent focus on technology despite various FUD and scams;

2.Evaluate the technical appearance, although facade can come from decoration, and decoration costs. A project with good technical appearance deserves Fomo, a “donation” mentality for investment, and effort spent on follow-up research. For instance: Github code quality, developer community reputation, whether the technical architecture is logically consistent, whether the technical framework has been applied, the technical depth of the white paper, etc.;

  1. Consider the narrative logic. The AI Agent track currently has a gradually “blockchainizing” narrative direction. You’ll find that more old chains are embracing support for the AI Agent narrative. Of course, originally big framework directions like #ElizaOS, #arc, #Swarms, #REI are also exploring the possibility of “blockchainization”; for example, #Focai is a community project exploring the “blockchainization” of the ElizaOS framework. Good narrative logic carries momentum because it bears the expectations of the entire Crypto market. If a project suddenly claims to solve AI problems that even web2 can’t solve in the short term, would you believe it?

  2. Observe ecosystem implementation. Framework standards are indeed upstream, and in most cases, it’s best to abstract the framework standards after having a single AI Agent. For example, #zerebro later launched zerePy, empowering single AI with a framework, naturally stronger than launching a new framework coin to split consensus. But an out-of-the-blue framework and standard, no matter how big the promise, needs to be evaluated based on actual AI Agent engineering implementation (team’s execution and iteration speed) and whether there is ecosystem implementation. This is vital for sustainable growth of the project.

In conclusion, today’s battle over frameworks and standards is about locking down who will be the next round’s EVM in the AI Agent narrative, who will be a better-performing SVM than EVM. And during this process, if a Cosmos IBC emerges, a new DeFi paradigm with Move, or parallel EVMs, real-time massive concurrency layer2… Just think how long this road is?

Frameworks and standards will keep emerging, each generation stronger than the last. It’s hard to make a choice.

I only look at the activity level of developers and the actual implementation results of the project. If they can’t deliver, short-term spikes are just voids. If “certainty” is seen, it’s not too late to get on board. The ceiling valuation of AI Agents can be as high as “public chain” levels, possibly over $10 billion in big opportunities, so there’s no need to rush.

3) The boundaries between AI Agent frameworks and standards are very blurry. For instance, the ElizaOS framework standard can only be qualitatively described as a spiritual totem of the developer community before it becomes platformized, with its value spill-over only supported by #ai16z; or #Game framework standards still play under the closed-source model of #Virtual, somewhat atypical compared to mainstream open-source combined architectures;

Moreover, while ElizaOS framework is undeniably a top player, it has an independent #ELIZA, and it’s unclear how it’s tied together; #arc RIG framework has a solid foundation, but applying Rust language to the AI Agent field to boost performance feels too advanced; #Swarms‘s technical appearance is actually not bad, but such a FUD-ridden and turbulent start and a panic-inducing situation was unexpected; #REI‘s exploration of blockchain determinism and Agent execution probability compatibility is very interesting but also too advanced, etc.

These are still some of the “technically appealing” frameworks and standards recognized by the market, not to mention many such as Nexus, LangGraph, Haystack, AgentFlow, etc., claiming to be framework standards, but whether it’s about low-code convenient deployment, native multi-chain inheritance, or other enterprise-level customized business potential, even AI Metaverse, etc.

This all illustrates the current “no standards” nature of framework standards, much like Vitalik’s proposal to expand Ethereum led to various explorations like Plasma, Rollup, Validium, Parallel, but in the end, only Rollup became mainstream.

Disclaimer:

  1. This article is reproduced from [Haotian | CryptoInsight]. The copyright belongs to the original author [@tmel0211]. If there are any objections to the reproduction, please contact the Gate Learn team, and they will handle it promptly.
  2. Disclaimer: The views and opinions expressed in this article are solely those of the author and do not constitute any investment advice.
  3. The Gate Learn team translated the article into other languages. Copying, distributing, or plagiarizing the translated articles is prohibited unless mentioned.
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