Recently, on-chain AI Agents seem to be showing signs of revival. 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 application execution tool service layers. 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 serving as the "nervous system" connecting AI models with external tools, solving the interoperability issues between Agents and external tools. Google DeepMind has expressed support for it, quickly making MCP a recognized industry 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" of the Web3 AI world. However, it still has shortcomings in remote secure communication (as analyzed in multiple security reports by @SlowMist_Team and @evilcos), especially when intensive asset-related interactions occur. 2) A2A (Agent-to-Agent Protocol): A communication protocol between Agents led by Google, similar to a "social network protocol" framework for Agents. Compared to MCP, which focuses on connecting AI tools, A2A emphasizes communication and interaction between Agents. It solves 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-scale AIs to collaborate in a unified manner. Personally, I feel that beyond the protocol itself, Google's endorsement of AI Agents carries greater significance. 3) UnifAI: Positioned as an Agent collaboration network, it aims to integrate the advantages of MCP and A2A to provide cross-platform Agent collaboration solutions for small and medium-sized enterprises. Its layout resembles a "middle layer," striving to make the Agent ecosystem more efficient through a unified service discovery mechanism. However, compared to other protocols, UnifAI's market influence and ecosystem development are still lacking, and it may focus on specific niche scenarios in the future. @darkresearchai: An MCP server application implementation based on the Solana blockchain, providing security guarantees through TEE (Trusted Execution Environment), enabling AI Agents to directly interact with the Solana blockchain, such as querying account balances, issuing tokens, and other operations. The protocol's biggest highlight is its path to empower AI Agents in DeFi, solving the trusted execution problem for on-chain operations. Its corresponding ticker $DARK has been quietly rising against the trend recently, but with caution stemming from past experiences, no recommendations are made here. However, DARK's application-layer expansion 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 application execution capabilities: DARK's TEE-based design solves a core issue—how to enable AI models to execute on-chain operations in a trusted manner. This provides technical support for the deployment of AI Agents in the DeFi field, potentially leading to more autonomous execution of transactions, token issuance, LP management, and other DeFi operations by AI Agents in the future. Compared to past Agent models that were purely speculative concepts, this practical Agent ecosystem represents true value. (However, DARK currently has only 12 limited actions on GitHub, which is just a good start, and it is still far from transitioning from the conceptual stage to large-scale application deployment.) 2) Multi-Agent collaborative blockchain networks: A2A and UnifAI's exploration of multi-Agent collaboration scenarios brings new network effect possibilities to the on-chain Agent ecosystem. Imagine a decentralized network composed of various specialized Agents, potentially breaking the capability boundaries of single LLMs and forming a decentralized market of autonomous collaboration, 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) before deriving application-layer innovations (such as DARK's attempts in the DeFi field). A decentralized Agent ecosystem will form a new layered expansion architecture: the bottom layer provides foundational security guarantees like TEE, the middle layer consists of protocol standards like MCP/A2A, and the top layer focuses on specific vertical scenario applications. (This might be bearish news for the once purely Web3 AI on-chain standard protocols. Shivering...) 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 cap bubble, but on who can truly address the core pain points of security, trustworthiness, and collaboration in the integration of Web3 and AI. As for avoiding another bubble trap, I personally think observing whether project progress closely aligns with Web2 AI technological innovations is a good approach. To summarize: 1. AI Agents will have new application-layer extension and speculative opportunities based on Web2 AI standard protocols (MCP, A2A, etc.). 2. AI Agents will no longer be satisfied with single-message push services; multi-AI Agent interactive collaboration as execution tool services (DeFAI, GameFAI, etc.) will be the new focus.