Recently, there seems to be signs of a resurgence in on-chain AI Agents. Protocol standards like MCP, A2A, and UnifAI are complementing each other to form a new Multi-AI Agent interaction infrastructure, upgrading AI Agents from pure information push services to an execution application tool service level. The question arises: could this be the beginning of the second wave of on-chain AI Agent spring?
1) MCP (Model Context Protocol): An open standard protocol launched by Anthropic, essentially connecting AI models with external tools' "nervous system," solving the interoperability issue between Agents and external tools. Google DeepMind has also expressed support for it, allowing MCP to quickly become an industry-recognized protocol standard.
The technical value of MCP lies in standardizing function calls, enabling different LLMs to interact with external tools using a unified language, akin to the "HTTP protocol" in the Web3 AI world. However, it still has shortcomings in remote secure communication, especially when interactions involving assets become intensive (as analyzed in multiple security reports by @SlowMist_Team and @evilcos).
2) A2A (Agent-to-Agent Protocol): A communication protocol for Agents led by Google, similar to a "social network" framework for Agents. While MCP focuses on connecting AI tools, A2A is more centered on communication interactions between Agents. It addresses capability discovery issues through the Agent Card mechanism, enabling cross-platform, multi-modal Agent collaboration, and has gained support from over 50 companies, including Atlassian and Salesforce.
Functionally, A2A resembles a "social protocol" in the AI world, allowing different small AIs to collaborate in a unified manner. Personally, I feel that beyond the protocol itself, Google's role in "gathering support" for AI Agents is of greater significance.
3) UnifAI: Positioned as an Agent collaboration network, it attempts to integrate the advantages of MCP and A2A to provide cross-platform Agent collaboration solutions for small and medium enterprises. Its layout resembles a "middle layer," aiming to make the Agent ecosystem more efficient through a unified service discovery mechanism. However, compared to the other protocols, UnifAI's market influence and ecosystem development still seem insufficient, and it may focus on a specific niche in the future.
@darkresearchai: A realization of an MCP server application based on the Solana blockchain, providing security guarantees through a TEE (Trusted Execution Environment), allowing AI Agents to interact directly with the Solana blockchain, such as querying account balances, issuing tokens, and other operations.
The biggest highlight of this protocol is its path to empower DeFi with AI Agents, solving the issue of trusted execution for on-chain operations. Its corresponding ticker $DARK has recently been quietly rising against the trend, but with a cautious attitude of "once bitten, twice shy," I won't make any recommendations here. However, the application layer expansion of DARK based on MCP indeed opens up a new direction.
The question arises: what expansion directions and opportunities can on-chain AI Agents leverage through these standardized protocols?
1) Decentralized execution application capabilities: Dark's TEE-based design addresses a core issue—how to enable AI models to reliably execute on-chain operations. This provides technical support for the deployment of AI Agents in the DeFi field, suggesting that more AI Agents capable of autonomously executing transactions, issuing tokens, and managing LPs may emerge in the future.
Compared to past Agent models that were purely conceptual hype, this practical Agent ecosystem represents the true value. (However, Dark currently has only a limited 12 Actions on GitHub, which can only be considered a good start; there is still a distance to fully move beyond the conceptual stage to large-scale application deployment.)
2) Multi-Agent collaborative blockchain networks: The exploration of multi-Agent collaboration scenarios by A2A and UnifAI brings new possibilities for network effects to the on-chain Agent ecosystem. Imagine a decentralized network composed of various specialized Agents, which could break through the capability boundaries of a single LLM, forming a self-collaborative decentralized market, perfectly aligning with the distributed network characteristics of blockchain.
In conclusion, the AI Agent track is moving away from the "MEME-ification" dilemma, and the development path of on-chain AI may first address cross-platform standard issues (MCP, A2A), followed by derivative application layer innovations (such as Dark's attempts in the DeFi field).
The decentralized Agent ecosystem will form a new layered expansion architecture: the bottom layer consists of basic security guarantees like TEE, the middle layer includes protocol standards like MCP/A2A, and the upper layer comprises specific vertical scenario applications. (This might be a negative for previously pure web3 AI on-chain standard protocols? Shivers…)
For ordinary users, after experiencing the ups and downs of the first wave of on-chain AI Agents, the focus should no longer be on who can hype the largest market value bubble, but rather on who can truly address the core pain points of security, trust, and collaboration in the integration of Web3 and AI. As for how to avoid falling into another bubble trap, I personally think it suffices to observe whether project progress can closely follow the AI technological innovations of web2.
To summarize:
AI Agents will have new application layer extension hype opportunities based on web2 AI standard protocols (MCP, A2A, etc.);
AI Agents will no longer be satisfied with single-message push services; the execution tool services for multi-AI Agent interaction and collaboration (DeFAI, GameFAI, etc.) will be new highlights.
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