Recently, there seems to be a sign of recovery in the on-chain AI Agent. 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 agents 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 there are intensive interactions involving assets.
2) A2A (Agent-to-Agent Protocol): A communication protocol between agents led by Google, similar to the protocol framework of "agent social network". Compared with MCP, which focuses on the connection of AI tools, A2A focuses more on the 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. It has been supported by more than 50 companies including 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 "group together" 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 ecosystem construction are still insufficient, and it may focus on a certain niche scenario in the future.
@darkresearchai: It is an MCP server application implementation based on the Solana blockchain. It 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 choose the path of DeFi, solving the problem of trusted execution of on-chain operations. Its corresponding Ticker $DARK has been quietly rising against the trend recently, but in line with the cautious attitude of "once bitten, ten years of fear", 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 on-chain AI Agents 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 AI models reliably execute on-chain operations. This provides technical support for the implementation of AI Agents in the DeFi field, which means that more AI Agents that can autonomously execute DeFi operations such as transactions, token issuance, and LP management may appear in the future.
Compared to the purely conceptual Agent models of the past, this Agent ecosystem 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 before it can be fully applied in large scale.)
2) Multi-agent collaborative blockchain network: A2A and UnifAI's exploration of multi-agent collaborative 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.
above.
In any case, the AI Agent track is getting rid of the "MEME" dilemma. The development path of on-chain AI may be to first solve the cross-platform standard issues (MCP, A2A), and then derive 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 is the basic security guarantee such as TEE, the middle layer is the protocol standards such as MCP/A2A, and the upper layer is the specific vertical scenario application. (This may be a negative for the once pure web3 AI chain standard protocol? 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 create the biggest market value bubble, but on who can truly solve the core pain points of security, trustworthiness, and collaboration in the process of combining Web3 with AI. As for how to avoid falling into another bubble trap, I personally think it is enough to observe whether the project progress can keep up with the AI technology innovation of web2.
To summarize:
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 service. The execution tool service (DeFAI, GameFAI, etc.) of multiple AI Agents interacting and collaborating will be a new highlight.
