Why has the AI Agent framework standard become a battleground for competing interests, and how can one evaluate whether a framework standard is worth investing in?
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Why has the AI Agent framework standard become a battleground for competing interests, and how can one evaluate whether a framework standard is worth investing in?
The AI Agent frameworks and standards are still in a very early stage.
Navigating Web3 tides with focused insightsAuthor: Haotian
When it comes to AI Agent frameworks and standards, many people likely feel a chaotic mix of angelic hope and demonic skepticism. The ceiling for building such frameworks is extremely high—some might rapidly scale up to $300M in valuation. But if the substance doesn't match the hype, consensus can collapse just as quickly, plunging projects into the abyss.
So why has the AI Agent framework standard become a battleground for strategic positioning, and how should we evaluate whether a given framework standard is worth investing in?
Below are my personal insights for your reference:
1) AI Agents are inherently products of the pure Web2 internet paradigm. Large language models (LLMs) are trained on massive closed datasets, ultimately producing interactive AIGC applications like ChatGPT, Claude, and DeepSeek.
The core focus here follows an "application-first" logic. Critical issues such as how agents communicate with one another, how they establish unified data exchange protocols, or how verifiable computation verification mechanisms can be built between agents—are fundamentally underdeveloped from the start.
Extending AI Agent frameworks and standards represents a shift—from centralized servers to decentralized collaborative networks, from closed ecosystems to open, unified protocol standards, and most importantly, a Web3-style architectural evolution from standalone AI Agent applications toward complex, interconnected ecosystems.
The key logic is simple: if AI Agents aim to achieve commercial viability under the modular, chain-native philosophy of Web3, then their starting point must be building a distributed architecture aligned with Web3 principles. Otherwise, you're merely competing in the Web2 arena—where success depends solely on raw compute power and user experience.
Hence, AI Agent frameworks and standards have become the contested frontier in this wave of AI + Crypto narratives, offering unimaginable potential.
2) AI Agent frameworks and standards are still in their earliest stages. To put it without exaggeration, listening today to developers pitch their technical visions and roadmaps feels much like when Vitalik Buterin toured China ten years ago seeking funding. Imagine standing before the young Vitalik back then—how would you assess him?
1. Evaluate the founder’s charisma. This aligns with the typical early-stage angel investment principle of "betting on the team." For instance, when Shaw was criticized for being too outspoken, did you recognize his genuine, community-engaged authenticity—and thus confidently rally behind ai16z? Similarly, when Kye Gomez of Swarms faced relentless FUD and fraud accusations, did his consistent dedication to technical discourse resonate with you?
2. Assess technical quality. While appearances can be decorated, decoration itself costs resources. A project with strong technical fundamentals deserves FOMO, justifies a “donation mindset” investment, and warrants deep research. Key indicators include: GitHub code quality, developer reputation within open-source communities, internal consistency of the technical architecture, whether the framework already powers real-world applications, and the depth and rigor of its technical whitepaper.
3. Examine narrative coherence. The AI Agent space is gradually evolving along a “chain-native” storytelling trajectory. You’ll notice more established blockchain projects embracing and supporting AI Agent narratives. Likewise, major framework initiatives like ElizaOS, arc, Swarms, and REI are increasingly exploring possibilities for “chain integration.” For example, Focai emerged from the community's effort to make ElizaOS “chain-native.” Strong narratives carry inherent momentum because they embody market-wide expectations across Crypto. Would you believe a project claiming to solve AI challenges that even Web2 hasn’t cracked yet?
4. Observe ecosystem execution. Frameworks and standards sit at the top of the stack. Ideally, a standard should emerge *after* successful single-agent implementations—like zerebro launching zerePy afterward. When a framework empowers individual agents, it builds stronger consensus than simply issuing a new “framework token” to fragment attention. However, when a framework appears out of nowhere, no matter how grand the vision, what matters is actual engineering progress—the team’s execution capability, iteration speed, and tangible ecosystem development. These determine long-term sustainability.
In sum, today’s battle over frameworks and standards is really about identifying who will become the next EVM in the AI Agent narrative—or perhaps even surpass EVM with a high-performance SVM alternative. Along the way, we might see something like Cosmos IBC emerge, or an entirely new Move-based DeFi paradigm, or a parallel-EVM with real-time, large-scale concurrency Layer2… Just imagine how long this journey truly is.
New frameworks and standards will keep emerging, each seemingly superior to the last, making choices incredibly difficult.
For me, I focus only on developer activity and actual delivery results. If a team fails to deliver, any short-term surge is meaningless. But if I spot genuine “certainty,” there’s no rush—I can still get on board later. The valuation ceiling for AI Agents could reach public blockchain levels, potentially unlocking billion-dollar+ opportunities. There’s absolutely no need to hurry.
3) The boundary between AI Agent frameworks and standards remains blurry. For example, ElizaOS can only be seen as a spiritual icon within developer communities before becoming a full platform; its value spillover currently relies entirely on ai16z. Meanwhile, the Game framework standard still operates in Virtual’s closed-source, proprietary mode—an outlier compared to mainstream open-source architectural patterns.
ElizaOS may indeed be the leading framework today, yet there exists a separate ELIZA token whose relationship with ElizaOS remains unclear. Arc RIG has solid fundamentals, but applying Rust to AI Agents for performance gains feels overly ambitious. Swarms technically isn’t bad, but its turbulent start filled with FUD and extreme price pumps caught everyone off guard. REI tackles the fascinating challenge of reconciling blockchain determinism with probabilistic agent execution—but again, the direction seems ahead of its time.
And these are just some of the market-recognized frameworks considered to have “technical merit.” There are countless others like Nexus, LangGraph, Haystack, AgentFlow—all branding themselves as framework or standard leaders, promoting everything from low-code deployment ease, native multi-chain inheritance, enterprise customization potential, to visions of AI Metaverse.
All this underscores the current lack of true standardization in framework standards—much like when Vitalik proposed Ethereum scalability, giving rise to Plasma, Rollup, Validium, Parallel, and other exploratory paths, yet only Rollup eventually became dominant.
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Haotian | CryptoInsight
