Author: Haotian
Recently, on-chain AI Agent seems to be showing signs of recovery. MCP, A2A, UnifAI and other protocol standards are complementing and connecting to form a new Multi-AI Agent interaction infrastructure, upgrading AI Agent from a pure information push service to an execution application tool service level. The question is, will this be the beginning of the second wave of AI Agent on-chain spring?
1) MCP (Model Context Protocol): An open standard protocol launched by Anthropic, which essentially connects the "nervous system" of AI models and external tools, solving the interoperability problem between Agent and external tools. Google DeepMind has expressed its support for it, making MCP quickly become an industry-recognized protocol standard.
The technical value of MCP lies in standardizing function calls, allowing different LLMs to interact with external tools in a unified language, which is equivalent to the "HTTP protocol" in the Web3 AI world. However, it still has shortcomings in remote secure communication (@SlowMist_Team @evilcos has published multiple security reports and analyses), especially when the interactive behaviors involving assets are intensive;
2) A2A (Agent-to-Agent Protocol): an inter-Agent communication protocol led by Google, similar to the protocol framework of "Agent social network". Compared with MCP's focus on AI tool connection, A2A focuses more on communication and interaction between agents. It solves the problem of capability discovery through the Agent Card mechanism and realizes cross-platform and multi-modal Agent collaboration, and has been supported by more than 50 companies such as Atlassian and Salesforce.
From a functional point of view, A2A is more like a "social protocol" in the AI world, allowing different small AIs to work together in a unified way. I personally feel that, apart from the protocol, it is more meaningful for Google to "gather" and endorse AI Agent.
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-sized enterprises. Its layout is similar to a "middle layer", hoping to make the Agent ecosystem more efficient through a unified service discovery mechanism. However, compared with several other protocols, UnifAI's market influence and ecological construction are still insufficient, and it may focus on a certain segmented scenario in the future.
@darkresearchai: It is an MCP server application implementation based on the Solana blockchain, which provides security through the TEE trusted execution environment, allowing AI Agent to interact directly with the Solana blockchain, such as querying account balances, issuing tokens, and other operations.
The biggest highlight of this protocol is that it enables AI Agent to enable DeFi's path selection, solving the problem of trusted execution of on-chain operations. Its corresponding Ticker $DARK has been quietly rising against the trend recently, but with the cautious attitude of "once bitten by a snake, you will be afraid of the rope for ten years", it is not recommended here. However, DARK's application layer landing expansion based on MCP has indeed opened up a new direction.
The question is, what expansion directions and opportunities can the on-chain AI Agent generate with the help of these standardized protocols?
1) Decentralized execution application capabilities: Dark's TEE-based design solves a core problem-how to make the AI model reliably execute on-chain operations. This provides technical support for the implementation of AI Agent in the DeFi field, which means that more AI Agents that independently execute DeFi operations such as transactions, token issuance, and LP management may appear in the future.
Compared with the purely conceptual Agent models in the past, this kind of Agent ecology with practical value is the real value. (However, Dark currently has only 12 actions on github, which can only be considered a good start. There is still a long way to go from completely leaving the conceptual stage to large-scale application)
2) Multi-Agent Collaborative Blockchain Network: A2A and UnifAI's exploration of multi-agent collaboration scenarios has brought new network effect possibilities to the on-chain Agent ecosystem. Imagine a decentralized network composed of multiple professional agents, which may break through the capabilities of a single LLM and form an autonomous and collaborative decentralized market, which happens to be a perfect match with the distributed network characteristics of blockchain.
The above.
In any case, the AI Agent track is getting rid of the "MEME" dilemma, and the development path of on-chain AI may be to first solve the cross-platform standard problem (MCP, A2A), and then derive application layer innovation (such as Dark's attempt in the DeFi field).
The decentralized Agent ecosystem will form a new layered expansion architecture: the bottom layer is TEE and other basic security guarantees, the middle layer is MCP/A2A and other protocol standards, and the upper layer is specific vertical scenario applications. (Maybe this is a negative for the standard protocol on the pure web3 AI chain? Shivering..)
For ordinary users, after experiencing the ups and downs of the first wave of AI Agent chains, the focus is no longer on who can hype the biggest market value bubble, but who can truly solve the core pain points of security, trust, and collaboration in the process of combining Web3 and AI. As for how to avoid falling into another bubble trap, I personally think it’s better to observe whether the project progress can keep up with the AI technology innovation of web2.
To sum up:
1. AI Agent will have a new wave of application layer extension hype opportunities based on web2 AI standard protocols (MCP, A2A, etc.);
2. AI Agent is no longer satisfied with single message push services, and multi-AI Agent interactive and collaborative execution tool services (DeFAI, GameFAI, etc.) will be a new highlight.
Weatherly
Kikyo
Anais
Catherine
Alex