MCP, A2A, ACP, or UTCP? It seems like every other day, orgs add yet another AI protocol to the agentic alphabet soup, making it all the more confusing. Below, we'll share what all these abbreviations actually mean and share why they are important for the future of AI.

On the surface, all the protocols serve a similar purpose. They are all trying to standardize how AI agents communicate, with the main distinction often being what exactly they're trying to talk to.

While by no means a comprehensive accounting of all the agentic protocols competing for industry adoption, most can be divided into four or five buckets: agent-to-tool, agent-to-agent, agent-to-user, domain-specific agent protocols, and all the frameworks that glue them together.

Agent-to-tool protocols: MCP, UTCP

The category that's gotten the most attention over the past year is tool-calling protocols.

Of these, the open source [1]Model Context Protocol (MCP) has emerged as the de facto standard. Originally developed by OpenAI rival Anthropic in late 2024, MCP is billed as the USB-C of agentic systems.

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The protocol uses the classic client-server architecture. Tools and data sources either run inside or are connected via API to the MCP server, which advertises its capabilities via stdio, HTTP, or server-sent events (SSE) to an MCP client.

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Since we first [5]looked at MCP last spring, the protocol has seen widespread adoption by essentially all of the AI heavyweights, including OpenAI and Google.

While MCP has won the popularity contest, it's far from perfect. As we recently reported, security vulnerabilities continue to [6]dog the protocol . Part of the problem is MCP servers are often little more than wrappers around code interpreters, which can lead to remote code execution attacks if not properly locked down.

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Moreover, not everyone agrees that MCP is the right path forward for agentic tool calling. Introduced last year in response to MCP, the [8]Universal Tool Calling Protocol (UTCP) is a fair bit simpler in its execution.

Rather than MCP's client-server architecture, UTCP exposes tools and data sources to the model using the tool's native endpoint. In other words, it tells the model how to interact with the tool the same way a human would.

The main argument for UTCP is that, if tools or data are already exposed via an API, the model shouldn't need another API wrapper in the form of an MCP server just to talk to it; the model should be able to call the tool directly.

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UTCP's developers argue this approach is more performant and secure as it eliminates overhead and the attack surface associated with MCP's client-server architecture. Despite this, UTCP remains a niche protocol.

Agent-to-agent protocols: A2A, ANP, NLIP

Much like the industry has gravitated to MCP for agentic tool calling, the [10]Agent-to-Agent protocol , A2A for short, is quickly becoming the de facto standard for how agents should talk to one another.

Originally [11]developed by Google, A2A also uses a client-server architecture along with many of the same messaging and transport protocols as MCP. But rather than talking to tools or data stores, A2A is designed explicitly to facilitate the discovery of other agents, and the communication between them.

The idea here is that agents may work as a team to solve a problem or perform a task. As we've previously discussed, depending on the complexity of the task, it may make more sense to have multiple agents that each perform a smaller piece of it, rather than one monolithic agent trying to do it all.

Protocols like A2A ensure all of these agents are speaking the same language so they can collaborate on tasks. And just like protocols we see elsewhere in the industry, A2A doesn't care whether agents are using MCP for tool calling or something else.

Since its introduction last year, Google has contributed A2A to the Linux Foundation, where it's seen adoption by some of the largest and most influential tech titans including Microsoft and AWS.

Last summer, the protocol was merged with IBM's Agent Communication Protocol, which was originally developed to power its BeeAI platform before it was also contributed to the Linux Foundation. So, if you were wondering where Big Blue's ACP fits into all of this, now you know.

However, A2A is only one of several emerging protocols for agent-to-agent communications. The [12]Agent Network Protocol (ANP) is another.

Rather than a client-server architecture, ANP enables peer-to-peer communications between agents on the same network. And while ANP shares many of the same goals as A2A, its implementation differs on several levels. A2A focuses heavily on multi-agent collaboration, while ANP aims to answer the question of what an internet of agents would look like.

Another more recent entry in the agent-to-agent protocol space is the [13]Natural Language Interaction Protocol (NLIP).

Introduced by Ecma International last month, NLIP is an application-layer protocol for exchanging information between agents running locally on devices or remotely on servers using natural language. However, compared to either A2A or ANP, NLIP isn't nearly as mature.

Agent-to-user protocols: A2UI, AG-UI

None of the protocols we've discussed up to this point addresses how end-users should interact with agents. Chatbots may be the way we interact with most language models today, but that doesn't mean they're the right surface for every application. So, of course, we have to have a protocol for this too.

Alongside A2A, Google is working on an [14]Agent to UI protocol, called A2UI. The idea behind the open source protocol is that rather than text-only responses, agents should be able to generate interfaces dynamically on request. For example, if you want help booking a flight, instead of a chat interface, the agent would generate a point and click interface to walk you through the booking.

It works a bit like this: a request is sent to an agent, which generates messages describing what the UI should look like and sends them back to the client, where they're rendered using established frameworks like Flutter or React. When the user interacts with the interface, new UI elements are generated in response.

For the moment, A2UI is still in preview with Google warning early adopters to expect changes over time.

A2UI isn't the only protocol attempting to standardize how agents interact with users. The [15]Agent User Interaction (AG-UI) protocol is approaching the problem from a slightly lower level.

While A2UI focuses on generating interactive user interfaces for agentic systems, AG-UI is attempting to define the way agents talk securely to front-end clients, like a smartphone app or webpage.

In fact, CopilotKit, the developer of AG-UI, emphasizes that the two protocols aren't mutually exclusive and can be used together.

Domain-specific protocols: UCP, AP2

Alongside protocols for agentic collaboration, client communications, and tool calling, we're also starting to see domain specific protocols for things like e-commerce.

Once again, it's Google that's leading the charge here. Introduced earlier this month, the [16]Universal Commerce Protocol (UCP) aims to provide a common language for agents to interact with businesses and payment processors. The protocol also works with Google's previously announced [17]Agent Payments Protocol (AP2) , which is designed to work with both A2A and MCP servers to enable agent-made payments within a set of predefined guardrails.

Early attempts to give agents a credit card to make purchases haven't always worked out perfectly. In at least one scenario, OpenAI's shopping agent decided that spending $31 on a dozen eggs was a [18]totally reasonable expense.

And, as nice as it might be to have an agent that restocks your sundries when you're actually running low instead of on a set schedule, nobody is going to be happy when the agent decides ordering a lifetime supply of single ply toilet paper is a good idea. AP2 aims to prevent these kinds of missteps.

Everything else

Alongside a growing number of agentic protocols competing for industry adoption are all the other frameworks and tools used to actually build and deploy the agents on which they're based.

These frameworks span the gamut from open source tools like LangChain to enterprise-focused SaaS products like AWS' AgentCore, Microsoft's Copilot Studio, and Google's Vertex AI Agent Builder.

Many of these frameworks build on the protocols we've discussed so far, with MCP and A2A being more common.

[19]Agents gone wild! Companies give untrustworthy bots keys to the kingdom

[20]Claude Code's prying AIs read off-limits secret files

[21]Yes, you can build an AI agent - here's how, using LangFlow

[22]AI agent hype cools as enterprises struggle to get into production

Driving consensus

Establishing new standards is a time-consuming and often painful process. Ask any greybeard who lived through the VHS/Betamax wars or millennial who landed on HD-DVD over Blu-ray – nobody wants to invest in the losing side.

With regard to agentic protocols, the Linux Foundation is doing its best to keep everyone on the same page. Last month, LF [23]formed the Agentic AI Foundation (AAIF) to provide vendor neutral oversight over the development of agent protocols and frameworks.

MCP, A2A, ACP, along with a host of agentic tools and frameworks like Goose, Agents.md, BeeAI, and Docling, are all part of the Linux Foundation now.

Where appropriate, the Linux Foundation has already moved to merge overlapping protocols like A2A and ACP.

As more protocols reach maturity, we suspect we'll see even more contributions to the AAIF before long. ®

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[1] https://modelcontextprotocol.io/docs/getting-started/intro

[2] https://pubads.g.doubleclick.net/gampad/jump?co=1&iu=/6978/reg_specialfeatures/agenticai&sz=300x50%7C300x100%7C300x250%7C300x251%7C300x252%7C300x600%7C300x601&tile=2&c=2aXyPVqCBdMEen3oeUohl4QAAAQI&t=ct%3Dns%26unitnum%3D2%26raptor%3Dcondor%26pos%3Dtop%26test%3D0

[3] https://pubads.g.doubleclick.net/gampad/jump?co=1&iu=/6978/reg_specialfeatures/agenticai&sz=300x50%7C300x100%7C300x250%7C300x251%7C300x252%7C300x600%7C300x601&tile=4&c=44aXyPVqCBdMEen3oeUohl4QAAAQI&t=ct%3Dns%26unitnum%3D4%26raptor%3Dfalcon%26pos%3Dmid%26test%3D0

[4] https://pubads.g.doubleclick.net/gampad/jump?co=1&iu=/6978/reg_specialfeatures/agenticai&sz=300x50%7C300x100%7C300x250%7C300x251%7C300x252%7C300x600%7C300x601&tile=3&c=33aXyPVqCBdMEen3oeUohl4QAAAQI&t=ct%3Dns%26unitnum%3D3%26raptor%3Deagle%26pos%3Dmid%26test%3D0

[5] https://www.theregister.com/2025/04/21/mcp_guide/

[6] https://www.theregister.com/2026/01/20/anthropic_prompt_injection_flaws/

[7] https://pubads.g.doubleclick.net/gampad/jump?co=1&iu=/6978/reg_specialfeatures/agenticai&sz=300x50%7C300x100%7C300x250%7C300x251%7C300x252%7C300x600%7C300x601&tile=4&c=44aXyPVqCBdMEen3oeUohl4QAAAQI&t=ct%3Dns%26unitnum%3D4%26raptor%3Dfalcon%26pos%3Dmid%26test%3D0

[8] https://www.utcp.io/

[9] https://pubads.g.doubleclick.net/gampad/jump?co=1&iu=/6978/reg_specialfeatures/agenticai&sz=300x50%7C300x100%7C300x250%7C300x251%7C300x252%7C300x600%7C300x601&tile=3&c=33aXyPVqCBdMEen3oeUohl4QAAAQI&t=ct%3Dns%26unitnum%3D3%26raptor%3Deagle%26pos%3Dmid%26test%3D0

[10] https://a2a-protocol.org/latest/

[11] https://www.theregister.com/2025/07/12/ai_agent_protocols_mcp_a2a/

[12] https://agent-network-protocol.com/

[13] https://nlip-project.org/#/

[14] https://a2ui.org/

[15] https://docs.ag-ui.com/introduction

[16] https://ucp.dev/latest/#

[17] https://ap2-protocol.org/

[18] https://www.washingtonpost.com/technology/2025/02/07/openai-operator-ai-agent-chatgpt/

[19] https://www.theregister.com/2026/01/29/ai_agent_identity_security/

[20] https://www.theregister.com/2026/01/28/claude_code_ai_secrets_files/

[21] https://www.theregister.com/2026/01/28/a_beginners_guide_to_ai_agents/

[22] https://www.theregister.com/2026/01/28/ai_agents_redis/

[23] https://www.theregister.com/2025/12/09/linux_foundation_agentic_ai_foundation/

[24] https://whitepapers.theregister.com/