Recently, on-chain AI agents seem to be showing signs of recovery. Protocol standards such as MCP, A2A, and UnifAI are complementing each other to form a new Multi-AI Agent interaction infrastructure, upgrading the 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) Model Context Protocol (MCP): An open standard protocol launched by Anthropic, which essentially opens up the "nervous system" between AI models and external tools, and solves the problem of interoperability between agents and external tools. Google's 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, so that different LLMs can interact with external tools in a unified language, which is equivalent to the "HTTP protocol" in the Web3 AI world, but it still has shortcomings in remote security communication (@SlowMist_Team @evilcos There are many security reports and analyses), especially after the interaction behavior involving assets is intensive; 2) A2A (Agent-to-Agent Protocol): Google-led inter-agent communication protocol, 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. The Agent Card mechanism solves the problem of capability discovery and realizes cross-platform and multi-modal agent collaboration, which has been supported by more than 50 enterprises 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. Personally, I feel that except for the agreement, it is more meaningful for Google to "save the game" to endorse the AI Agent. 3) UnifAI: Positioned as an agent collaboration network, it tries 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 segment scenario in the future. @darkresearchai: It is an MCP server application implementation based on the Solana blockchain, which provides security assurance through the TEE trusted execution environment, so that the AI agent can directly interact with the Solana blockchain, such as querying account balances, issuing tokens, and other operations. The biggest highlight of the protocol is that it empowers the path selection of DeFi with AI Agent, which solves 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 being bitten by a snake and being afraid of the well rope for ten years, I will not recommend it here. However, the application layer expansion of DARK based on MCP does open up a new direction. The question is, what expansion directions and opportunities can be generated by on-chain AI agents with the help of these standardized protocols? 1) Decentralized execution application capabilities: Based on TEE's design, Dark solves a core problem - how to make AI models credibly perform on-chain operations. This provides technical support for the implementation of AI Agents in the DeFi field, which means that in the future, there may be more AI Agents that independently perform DeFi operations such as trading, token issuance, and LP management. Compared with the Agent model that was purely conceptual hype in the past, this kind of Agent ecology with practical value is where the real value lies. (However, Dark currently has only a limited number of 12 Actions on github, which can only be regarded as a good start, and there is still a long way to go from completely leaving the concept 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 capability boundaries of a single LLM and form a decentralized market of autonomous collaboration, which happens to be a perfect fit with the characteristics of the distributed network of blockchain. 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 solve the cross-platform standard problem (MCP, A2A) first, and then derive application-layer innovation (such as Dark's attempt in the DeFi field). The decentralized agent ecosystem will form a new hierarchical expansion architecture: the bottom layer is basic security assurance such as TEE, the middle layer is MCP/A2A and other protocol standards, and the upper layer is specific vertical scenario applications. (This may be a negative for the once pure web3 AI on-chain standard protocol?) Shivering. ) For ordinary users, after experiencing the first wave of ups and downs on the AI Agent chain, it is no longer about who can hype up the biggest market value bubble, but who can truly solve the core pain points such as security, trustworthiness, and collaboration in the process of combining Web3 and AI. As for how to avoid falling into another bubble trap, I personally think it is good to observe whether the progress of the project 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 agents are no longer satisfied with monolithic message push services, and execution tool services (DeFAI, GameFAI, etc.) for multi-AI agent interaction and collaboration will be a new highlight.