Challenges in Adapting A2A and MCP Protocols for Web3 AI Agents

According to PANews, the potential adoption of Google's A2A and Anthropic's MCP protocols as communication standards for web3 AI Agents presents significant challenges due to the distinct differences between web2 and web3 ecosystems. The first challenge lies in the maturity of applications. While A2A and MCP have quickly gained traction in the web2 domain by enhancing already mature application scenarios, web3 AI Agents are still in the early stages of development, lacking deep application contexts such as DeFAI and GameFAI. This makes it difficult for these protocols to be directly applied and utilized effectively in the web3 environment. For instance, in web2, users can seamlessly update code on platforms like GitHub using the MCP protocol without leaving their current work environment. However, in a web3 setting, executing on-chain transactions with locally trained strategies can become confusing when analyzing on-chain data. Another significant hurdle is the absence of foundational infrastructure in the web3 space. To build a comprehensive ecosystem, web3 AI Agents must address the lack of essential components such as a unified data layer, Oracle layer, intent execution layer, and decentralized consensus layer. In web2, A2A protocols allow agents to easily collaborate using standardized APIs. In contrast, web3 environments pose substantial challenges even for simple cross-DEX arbitrage operations. Consider a scenario where a user instructs an AI Agent to buy ETH from Uniswap when the price drops below $1600 and sell when it rises. This seemingly straightforward task requires the agent to tackle web3-specific issues like real-time on-chain data analysis, dynamic gas fee optimization, slippage control, and MEV protection. In web2, such tasks are simplified by standardized API calls, highlighting the stark difference in infrastructure maturity between the two environments. Furthermore, web3 AI Agents must address unique demands that differ from web2 protocols and functionalities. For example, in web2, users can easily book the cheapest flight using A2A protocols. However, in web3, when a user wants to transfer USDC cross-chain to Solana for liquidity mining, the agent must understand user intent, balance security, atomicity, and cost efficiency, and execute complex on-chain operations. If these operations increase security risks, the perceived convenience becomes meaningless, rendering the demand a false need. In conclusion, while the value of A2A and MCP protocols is undeniable, expecting them to seamlessly adapt to the web3 AI Agent landscape without modifications is unrealistic. The gaps in infrastructure deployment present opportunities for builders to innovate and fill these voids.