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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