The tech world keeps creating a flood of new standards for how AI agents should talk to each other. It’s a pattern we’ve seen before—lots of competing protocols that make things more complicated instead of easier. Right now, the focus is on agent-to-agent communication in AI, which could be a game-changer for enterprise tech. But all these different standards might actually slow down progress instead of helping it along.

Many types of intelligent agents need to share information smoothly and securely. Think about big language models, bots that handle service requests, digital twins for IoT devices, or workflow managers. They all face the same big challenge: how to talk to each other without confusion. Ideally, a straightforward, practical protocol would do the trick. But instead, there’s a rush of new standards from different companies and groups, each claiming to be the future. This creates a confusing mess that no one can easily navigate.

The Growing List of AI Communication Protocols

There are quite a few protocols out there, each with its own twist. OpenAI has its Function Calling and the OpenAI Agent Protocol, aiming to make models interact better with external APIs. Microsoft offers its Semantic Kernel Extensions, designed to help agents coordinate across various tools and platforms. Meta has its Agent Communication Protocol, which focuses on decentralized trust and intent resolution at internet scale. LangChain’s protocol is built to enable interoperability among different agent systems, focusing on task chaining and switching. Stanford’s Autogen Protocol supports research-level collaboration and negotiation among AI agents. Anthropic’s Claude-Agent Protocol emphasizes message formatting and maintaining context in multi-agent conversations. Meanwhile, the W3C is proposing standards for agent discovery and message types to make agents as easy to find as web pages. IBM’s AgentSphere focuses on multi-modal communication across hybrid clouds, including policy negotiation.

This list isn’t complete. There are many more protocols popping up in online discussions, startup pitches, and industry forums. Each claims to be the one true standard for multi-agent coordination. But the truth is, this proliferation creates more confusion than clarity.

The Problems with Too Many Standards

Competition might seem good, but in reality, it leads to silos. When different protocols don’t work together, businesses face extra work, higher costs, and vendor lock-in. It’s a repeat of past tech failures. Remember CORBA and DCOM in the 90s? They promised seamless distributed computing but ultimately fell apart. The 2000s gave us the WS-* standards, which ended up being a tangled web of specifications that few still use today. Eventually, simpler protocols like REST and JSON became the norm because they were easy and flexible. But millions of dollars were wasted on incompatible systems first.

When vendors push their own protocols, they create barriers instead of bridges. Agents trained to speak one dialect can’t easily understand those using another. Companies are left choosing between locking into one vendor’s standard, building costly translation layers, or waiting for the market to settle. The core issue: producing dozens of standards for the same purpose results in nothing being standard at all. It’s confusion, not progress.

Less Is More: The Case for a Minimal Protocol

Most of the interaction between AI agents can be handled with just a few basic message types. Think request, response, notify, and error. Complex features like trust negotiation or context passing can be added gradually. The focus should be on making the core messaging interoperable, not on creating a perfect, all-encompassing protocol from the start.

The real problem isn’t technical—it’s marketing. Many standards are pushed forward more to gain market share than to solve actual problems. Everyone wants to be the TCP/IP of AI agents, but history shows that protocols gain dominance through grassroots adoption, not just white papers or big conferences.

The best approach is to agree on a minimum viable protocol. Something simple, like HTTP with JSON schemas, could cover most needs. It’s stable, widely understood, and easy to build upon. Over time, optional extensions can add more features as needed. This avoids the Tower of Babel chaos of competing, overly complex standards.

Business leaders and tech architects should push for real interoperability. They should ask: does this new standard solve a real problem? Or is it just a marketing move? When in doubt, build abstraction layers that prevent vendor lock-in and allow flexibility.

In the end, the industry needs open, simple protocols for AI communication. Without that, we risk another cycle of wasted effort, lost time, and missed opportunities. Let’s focus on what really matters—creating value, not vanity standards.

Inspired by

• https://www.infoworld.com/article/4033863/the-problem-with-ai-agent-to-agent-communication-protocols.html

Sources

• gmpg.org
• networkworld.com
• podigee.com
• podigee.com