The problem with AI agent-to-agent communication protocols

Once again, too many standards are vying for dominance in solving a relatively simple problem. The IT industry needs to break this counterproductive pattern. You know the routine: The IT industry, driven as much by vendor ambition as by necessity, develops many competing standards to solve a simple problem. Today’s culprit: agent-to-agent communication in AI. The recent rise of so-called “standards” for how intelligent agents should communicate echoes past issues with service-oriented architecture, web services, and various messaging middleware conflicts. The key difference is that now, this confusion could prevent one of the most promising areas in enterprise technology—agentic AI—from ever providing real value. Let’s set the scene. Intelligent agents, whether they are specialized large language models (LLMs), service-brokering bots, Internet of Things digital twins, or workflow managers, need to communicate efficiently, securely, and transparently. This is a typical interoperability issue. A well-established industry could, in theory, create a straightforward, practical protocol and move forward. Instead, we see a flood of emerging standards from too many “expert” voices with an underlying agenda, each accompanied by a white paper, a community call, a sponsored conference, and, of course, an ecosystem. This is the core problem.

The alphabet soup of protocols

Let’s look at a cross-section of just some of the technologies that are on offer or are in the works:

• OpenAI’s Function Calling and OpenAI Agent Protocol (OAP) is promoted as a way to enable their models to interact more flexibly with APIs, enhancing prompts with context and coordination logic. There’s talk of standardizing this into the “OAP Standard” but details remain unclear.
• Microsoft’s Semantic Kernel (SK) Extensions are designed to foster agent communication and coordination across various toolkits, including Microsoft’s own Copilot and external agents by using plug-in skills and manifest-driven connectors.
• Meta’s Agent Communication Protocol (Meta-ACP) focuses on graph-based intent resolution, message-passing semantics, and decentralized trust. The pitch: make agents modular and composable at internet scale.
• LangChain Agent Protocol (LCAP) builds on the open source LangChain framework with a focus on interoperability among various agent systems. Their protocol emphasizes chained tool invocation and task-switching, providing compatibility layers with OpenAI and Anthropic models.
• Stanford’s Autogen Protocol supports research-level coordination and negotiation among AI agents, particularly in collaborative planning and negotiation contexts.
• Anthropic’s Claude-Agent Protocol is less of a full-stack protocol and more of a set of message formatting and invocation best practices aimed at aligning with human intent and maintaining context across multi-agent dialogues.
• W3C Multi-Agent Protocol Community Group of the World Wide Web Consortium is proposing universal message types, schemas, and agent discovery mechanisms. They want to make “agents as discoverable as web pages.”
• IBM’s AgentSphere focuses on multi-modal agent communication across hybrid cloud environments, with specifications for policy negotiation and session transfer.
This list isn’t complete. There are dozens more protocols mentioned in Reddit posts, Substack essays, and well-funded stealth startups, each claiming to be the one true answer to multi-agent coordination.

Competition breeds silos

Some will say, “Competition breeds innovation.” That’s the party line. But for anyone who’s run a large IT organization, it means increased integration work, risk, cost, and vendor lock-in—all to achieve what should be the technical equivalent of exchanging a business card. Let’s not forget history. The 90s saw the rise and fall of CORBA and DCOM, each claiming to be the last word in distributed computing. The 2000s blessed us with WS-* (the asterisk is a wildcard because the number of specs was infinite), most of which are now forgotten. REST and JavaScript Object Notation communication finally won, mostly because they didn’t try too hard—but not before millions of dollars were wasted on false starts and incompatible ecosystems. The truth: When vendors promote their own communication protocols, they build silos instead of bridges. Agents trained on one protocol can’t interact seamlessly with those speaking another dialect. Businesses end up either locking into one vendor’s standard, writing costly translation layers, or waiting for the market to move on from this round of wheel reinvention.

Multiple standards means no standards

It’s a fundamental principle: producing 20 standards for the same need essentially results in no standards. There is no network effect, only confusion. The time spent debating minor protocol differences, lobbying standards organizations, and launching compatibility initiatives is time not spent creating value or solving end-user business issues. We in IT love to make simple things complicated. The urge to create a universal, infinitely extensible, plug-and-play protocol is irresistible. But the real-world lesson is that 99% of enterprise agent interaction can be handled with a handful of message types: request, response, notify, error. The rest—trust negotiation, context passing, and the inevitable “unknown unknowns”—can be managed incrementally, so long as the basic messaging is interoperable.

InfoWorld Smart Answers

Let’s be honest. Most of the churn around standards is more about gaining mindshare and securing business development budgets than solving architecture issues. Announcing a standard protocol aims to foster an ecosystem rather than achieve consensus. Everyone aspires to be the TCP/IP of AI agents, but history shows that protocol dominance is mainly achieved through grassroots adoption rather than white papers or marketing efforts.

Go for the minimum

Here’s an unpopular truth: the industry would be best served by collectively deciding on the minimum viable protocol and iterating from there. Something as dead simple as HTTP+JSON with common schemas would meet 80% of use cases, with optional extensions as needs emerge. Today we have a Tower of Babel: overcomplex schemes, edge-case features no one will use, and competing vendor alliances. Business leaders and architects should resist jumping on every protocol bandwagon. Demand interoperability, evaluate whether a “standard” actually solves a real pain point, and when in doubt, build abstraction layers that prevent lock-in. We urgently need open protocols for AI agent communication. Too many competing standards render them all essentially meaningless. The IT industry has gone through this cycle before. Unless we break free from it, agentic AI will just be another example of wasted time and effort. Let’s not allow protocol vanity to get in the way of creating real business value.

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Originally published at InfoWorld on Tue, 05 Aug 2025 04:50:01 GMT

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Frequently Asked Questions (FAQ)

Understanding AI Agent Communication

Q: What is the core problem with current AI agent-to-agent communication protocols? A: The core problem is the proliferation of too many competing standards, creating confusion and hindering interoperability, similar to past issues with SOA and web services. Q: Why is a unified standard for AI agent communication important? A: A unified standard is crucial to prevent fragmentation and ensure that emerging AI technologies, like agentic AI, can deliver real value by enabling seamless communication and coordination between different AI agents. Q: What are some examples of emerging AI agent communication protocols? A: Examples include OpenAI's Function Calling and OAP, Microsoft's Semantic Kernel Extensions, Meta's Agent Communication Protocol (Meta-ACP), LangChain Agent Protocol (LCAP), Stanford's Autogen Protocol, Anthropic's Claude-Agent Protocol, and proposals from the W3C Multi-Agent Protocol Community Group. Q: What is the risk of having too many competing standards? A: The risks include increased integration work, higher costs, vendor lock-in, and the potential for businesses to be stuck with incompatible ecosystems, ultimately slowing down the adoption and effectiveness of agentic AI. Q: What is the suggested "minimum viable protocol" for AI agent communication? A: The article suggests that something as simple as HTTP+JSON with common schemas could meet most use cases, allowing for optional extensions as needs arise, rather than overly complex, feature-rich protocols.

Crypto Market AI's Take

The proliferation of competing standards in AI agent communication mirrors the early, fragmented days of blockchain and cryptocurrency. Just as initial interoperability challenges were eventually addressed by industry-wide adoption of certain protocols (like ERC-20 for tokens), the AI agent space needs a similar consolidation to unlock its full potential. At Crypto Market AI, we leverage advanced AI agents for market analysis and trading strategies. Understanding the importance of standardization for scalable AI systems is key to our development. We believe that a focus on foundational, interoperable protocols will be critical for the widespread adoption of agentic AI, much like how standardized protocols have driven the growth and accessibility of the cryptocurrency market.

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• AI crypto coins drive 2025 innovation blockchain ai converge

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Originally published at InfoWorld on Tue, 05 Aug 2025 04:50:01 GMT