当前人工智能发展面临的巨大挑战是如何让不同AI系统进行有效沟通，如同数字巴别塔阻碍了AI潜能的发挥。为了打破这一困境，业界正积极探索通用沟通协议，以实现AI之间的协同工作。Anthropic的MCP协议侧重于单个AI与外部工具的交互，而IBM的ACP和Google的A2A协议则更关注AI代理间的分布式协作。MCP设想的是一个以强大AI为中心的工具集成模式，ACP和A2A则致力于构建一个由多个专业AI组成的智能网络，共同解决复杂问题。虽然AI通用语言的实现将开启无限可能，但“协议大战”的风险也可能导致进一步的碎片化。最终的解决方案可能并非单一通用协议，而是根据不同场景需求采用最适合的协议，AI间的有效沟通无疑是该领域亟待解决的关键问题。

🌐 **AI沟通的“数字巴别塔”困境：** 当前AI模型虽然强大，但它们各自为政，缺乏有效的沟通机制，这严重阻碍了AI协同工作的潜力发挥，如同古代的巴别塔故事，限制了人类的共同进步。

💡 **通用沟通协议的探索与演进：** 为解决AI沟通难题，业界提出了多种协议。Anthropic的MCP（Model Context Protocol）旨在安全地组织AI使用外部工具，但主要面向单AI多工具场景。IBM的ACP（Agent Communication Protocol）作为开源项目，利用成熟的Web技术，支持AI代理间的点对点通信，强调去中心化协作。Google的A2A（Agent-to-Agent Protocol）则与MCP协同，侧重于AI团队如何通过“Agent Cards”等机制进行复杂任务的交接与信息传递。

🚀 **两种AI协作愿景的对比：** MCP代表了一种由单一强大AI驱动，调用多种工具的中心化模式。而ACP和A2A则勾画了分布式智能的蓝图，即由多个专精AI组成的团队协同解决问题，例如在一个产品设计流程中，不同AI分别负责市场调研、设计和制造等环节。

🔮 **未来AI沟通的挑战与展望：** AI通用语言的实现预示着巨大的应用前景，如医疗AI协同分析患者数据制定个性化治疗方案。然而，“协议大战”可能导致AI生态的进一步碎片化。未来AI沟通很可能不是单一通用协议，而是多种协议并存，各自发挥优势。如何让AI有效沟通，是当前AI领域面临的核心挑战之一。

While the development of increasingly powerful AI models grabs headlines, the big challenge is getting intelligent agents to communicate.

Right now, we have all these capable systems, but they’re all speaking different languages. It’s a digital Tower of Babel, and it’s holding back the true potential of what AI can achieve.

To move forward, we need a common tongue; a universal translator that will allow these different systems to connect and collaborate. Several contenders have stepped up to the plate, each with their own ideas about how to solve this communication puzzle.

Anthropic’s Model Context Protocol, or MCP, is one of the big names in the ring. It attempts to create a secure and organised way for AI models to use external tools and data. MCP has become popular because it’s relatively simple and has the backing of a major AI player. However, it’s really designed for a single AI to use different tools, not for a team of AIs to work together.

And that’s where other protocols like the Agent Communication Protocol (ACP) and the Agent-to-Agent Protocol (A2A) come in.

ACP, an open-source project from IBM, is all about enabling AI agents to communicate as peers. It’s built on familiar web technologies that developers are already comfortable with, which makes it easy to adopt. It’s a flexible and powerful solution that allows for a more decentralised and collaborative approach to AI.

Google’s A2A protocol, meanwhile, takes a slightly different tack. It’s designed to work alongside MCP, rather than replace it. A2A is focused on how a team of AIs can work together on complex tasks, passing information and responsibilities back and forth. It uses a system of ‘Agent Cards,’ like digital business cards, to help AIs find and understand each other.

The real difference between these protocols is their vision for the future of how AI agents communicate. MCP is for a world where a single, powerful AI is at the centre, using a variety of tools to get things done. ACP and A2A are designed for distributed intelligence, where teams of specialised AIs work together to solve problems.

A universal language for AI would open the door to a whole new world of possibilities. Imagine a team of AIs working together to design a new product, with one agent handling the market research, another the design, and a third the manufacturing process. Or a network of medical AIs collaborating to analyse patient data and develop personalised treatment plans.

But we’re not there yet. The “protocol wars” are in full swing, and there’s a real risk that we could end up with even more fragmentation than we have now.

It’s likely that the future of how AI communicates won’t be a one-size-fits-all solution. We may see different protocols, each used for what it does best. One thing is for sure: figuring out how to get AIs to talk to each other is among the next great challenges in the field.

(Photo by Theodore Poncet)

See also: Anthropic deploys AI agents to audit models for safety

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