Recently, on chain AI agents seem to show signs of recovery. MCP, A2A, UnifAI and other protocol standards are complementary and interconnected to form a new Multi AI Agent interaction infrastructure, upgrading AI Agents from pure information push services to execution application tool service levels. 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 introduced by Anthropic, which essentially connects the "nervous system" of AI models and external tools, and solves the interoperability problem between agents and external tools. Google DeepMind has expressed its support for it, making MCP quickly 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, equivalent to the "HTTP protocol" of the Web3 AI world. However, it still has shortcomings in remote secure communication (@ SlowMist_Team @ evilcos has analyzed multiple security reports), especially after intensive interaction behaviors 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 to MCP, which focuses on AI tool connections, A2A focuses more on communication and interaction between agents. By utilizing the Agent Card mechanism to address capability discovery issues, cross platform and multimodal Agent collaboration has been achieved, with support from over 50 enterprises including Atlassian and Salesforce. From a functional perspective, A2A is more like a "social protocol" in the AI world, allowing different small AI to work together in a unified way. In my personal opinion, apart from agreements, the significance of Google endorsing AI agents through "accumulation" is even greater. 3) UnifAI: Positioned as an Agent collaboration network, attempting 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 an "intermediate layer", hoping to make the Agent ecosystem more efficient through a unified service discovery mechanism. However, compared to other protocols, UnifAI's market influence and ecological construction are still insufficient, and it may focus on a specific niche scenario in the future. @Darkresearchai is an MCP server application implementation based on the Solana blockchain, which provides security through a TEE trusted execution environment, allowing AI agents to directly interact with the Solana blockchain, such as querying account balances, issuing tokens, and other operations. The biggest highlight of this protocol is empowering DeFi path selection with AI agents, solving the problem of trusted execution of on chain operations. Its corresponding Ticker DARK has been quietly rising against the trend recently, but with a cautious attitude of being bitten by a snake once and afraid of drilling ropes for ten years, we do not recommend it here. But DARK's application layer expansion based on MCP has indeed opened up a new direction. The question is, what are the expansion directions and opportunities for on chain AI agents to leverage these standardized protocols? 1) Decentralized execution application capability: Dark's TEE based design solves a core problem - how to make AI models reliably 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 execute DeFi operations such as trading, token issuance, and LP management. Compared to the pure conceptual hype of Agent models in the past, this practical Agent ecosystem is where the real value lies. (However, Dark currently only has a limited number of 12 Actions on GitHub, which can only be considered a good start. There is still a long way to go from the conceptual stage to the large-scale application landing.) 2) Multi Agent Collaborative Blockchain Network: The exploration of multi-agent collaboration scenarios by A2A and UnifAI has brought new possibilities for network effects to the on chain agent ecosystem. Imagine a decentralized network composed of multiple professional agents that may break through the capability boundaries of a single LLM and form an autonomous collaborative decentralized market, which perfectly fits the characteristics of blockchain distributed networks. above. Anyway, the AI Agent race is getting rid of the "MEME" dilemma, and the development path of on chain AI may be to first solve cross platform standard problems (MCP, A2A), and then derive application layer innovation (such as Dark's attempts in the DeFi field). The decentralized Agent ecosystem will form a new hierarchical expansion architecture: the bottom layer is based on basic security guarantees such as TEE, the middle layer is based on protocol standards such as MCP/A2A, and the upper layer is based on specific vertical scenario applications. (This may be a disadvantage for the once pure web3 AI on chain standard protocol? Trembling.) For ordinary users, after experiencing the first wave of ups and downs in the AI Agent chain, the focus is no longer on who can make the largest market value foam, but who can really solve the core pain points of security, credibility, collaboration, etc. in the process of combining Web3 with AI. As for how to avoid falling into another foam trap, I personally think it would be better to observe whether the project progress can closely follow the AI technology innovation of web2. To summarize: 1. There will be a new wave of application layer extension hype opportunities for AI agents based on web2 AI standard protocols (MCP, A2A, etc.); 2. AI agents are no longer satisfied with single message delivery services, and multi AI agent interactive and collaborative execution tool services (DeFAI, GameFAI, etc.) will be a new focus.