MCP, A2A, AGP, ACP: Making Sense of the New AI Protocols

Just a few months ago, it was all about LLMs and fine-tuning. Then came the AI agents. Now? It’s raining AI protocols—MCP, A2A, AGP, ACP. 😵‍💫 So… what’s the deal?

Are these the next big thing in AI, or just a pile of acronyms devised to melt your brain? 🫠 How do these protocols actually work? What problems are they solving? And why has everyone suddenly started talking about them?

📢 Good news: you’re not the only one asking!

This guide breaks down the chaos and helps you actually understand these new AI protocols (without needing a PhD in AI, ML, or distributed systems). Let’s dive into MCP, A2A, AGP, and ACP!

Why Is Everyone Suddenly Talking About AI Protocols?

Everyone’s suddenly buzzing about AI protocols—and for good reason... they’re the missing piece in the AI puzzle! 🧩 🧩 🧩

We’ve got powerful AI models, smart agents, and mind-blowing tech… but most of them are like islands 🌴, stuck doing their own thing, struggling to talk to each other.

That’s where AI protocols step in, working like a universal translator for artificial intelligence systems 🗣️ 🤝.

Protocols like MCP, A2A, AGP, and ACP are creating standardized languages for AI agents to communicate, collaborate, and connect with third-party tools.

Think HTTP, but for AI agents. And just like how HTTP made the web explode into what it is today, these AI protocols are setting the stage for a new generation of connected, feature-rich, interoperable AI systems. 🌐

Time to explore these new AI protocols! ⚡

MCP: Model Context Protocol

MCP (Model Context Protocol) is an open protocol developed by Anthropic that standardizes how applications provide context to LLMs and AI applications.

Technically speaking, MCP relies on a client-server architecture, where some MCP Hosts (AI tools, AI agents, Claude Desktop, IDEs, etc.) want to access data through MCP. To do so, they use MCP clients to connect to MCP Servers, which act as bridges to either local data (e.g., files, databases) or remote services (e.g., web APIs). Each MCP Server exposes a set of tools—like reading a file, querying a database, or calling a third-party API—through a standardized schema.

Think of MCP like USB-C, but for AI. Just like USB-C makes it effortless to connect your laptop to monitors, phones, or hard drives—MCP makes it just as easy for AI to connect to your calendar, local folders, CRM, or API-based tools. No duct-taping scripts or middleware. Just plug and prompt. 🔌

Some real-world applications for MCP:

• Access local calendars and emails to schedule meetings. 🗓️
• Fetch project-specific documentation and libraries for context-aware code suggestions. 💻
• Pass real-time, fresh, accurate data retrieved via a web scraping MCP server to your AI agent. 📊

For a real-world example, follow our step-by-step tutorial on integrating Google ADK with an MCP server. 🧠

A2A: Agent2Agent Protocol

A2A (Agent2Agent) is an open, standardized protocol developed by Google to make AI agents talk and collaborate seamlessly—no matter which vendor built them or what framework they use.

Under the hood, it’s built on existing tech like HTTP, JSON-RPC, and SSE (Server-Sent Events). It supports secure, long-running workflows, real-time updates, and multiple data types like audio and video streams. 🎙️ 📹

The core idea? Enable a “client” agent to discover, communicate, and coordinate tasks with one or more “remote” agents through a structured task lifecycle, exchanging messages, capabilities (via “Agent Cards” in JSON format), and user interface negotiations.

In simple terms? A2A is like Slack for AI agents—a shared workspace where bots can chat, assign tasks, and get things done 🏢 🤖.

✨ An example? A hiring manager tells their AI assistant to find dev candidates. That agent pings a sourcing agent (LinkedIn scraper), a screening agent (think automated tech interview), and a scheduling agent (calendar wizard). Each speaks A2A, so the whole workflow just flows.

Oh, and don’t worry… A2A can build on top of MCP, and does not compete with it. In fact, they work even better together:

AGP: Agent Gateway Protocol

AGP (Agent Gateway Protocol) is a communication standard from AGNTCY, purpose-built for AI agents running in distributed systems.

It’s built around gRPC using HTTP/2 and Protocol Buffers to deliver high-performance, low-latency, and secure agent interaction. Note that AGP supports diverse communication patterns: request-response, pub/sub, fire-and-forget, and streaming.

The two software components behind AGP are:

• Data plane: Handles message routing and delivery.
• Control plane: Manages config, auth, and access control.

These build upon security features like mTLS, RBAC, and end-to-end encryption, alongside observability tools like tracing and metrics 🛡️.

Wanna get AGP explained in plain English? 🤓

Think of AGP as a sophisticated postal service for AI agents 📬. Instead of just sending letters, agents can have real-time conversations or subscribe to specific information channels.

For example, a weather forecasting agent could publish alerts, and various smart home agents could subscribe to receive them. AGP ensures these messages are delivered quickly, securely, and to the right recipient. 🌤️ 🌧️ ☀️ ☁️

ACP: Agent Communication Protocol

ACP (Agent Communication Protocol) is an open standard developed under the Linux Foundation—alongside BeeAI—for agent interoperability.

In detail, ACP aims for AI agent interoperability via a standardized RESTful API interface. It supports synchronous, asynchronous, and streaming interactions, treating agents as services exchanging multimodal messages (text, image, JSON).

ACP doesn’t care how agents are built—it just needs a few basics to make them talk. Core concepts include:

• Agent Detail: Describes an agent’s name, capabilities, and metadata for discovery—no need to expose inner code.
• Run: A single agent task with inputs and outputs; supports sync, async, and streaming results.
• Message: The main communication format—a structured sequence of multimodal content parts.
• MessagePart: Pieces of a message like text, images, or JSON—stacked together to form rich, structured conversations.
• Await: It lets agents pause, ask for input, and resume—ideal for interactive or multi-step workflows.

Note: In ACTP, agents don’t need to be online to be found. Just pack their metadata into the distribution 📦. This makes them discoverable even in locked-down, offline, or scale-to-zero environments. 🚫 🌐

Think of ACP as a delivery service for AI agents 🗣️➡️📦. Imagine different specialist agents, one for writing code, another for generating images, and a third for analyzing data. ACP provides the common language (REST API) and delivery mechanism (multimodal messages) for them to collaborate, regardless of how they were built.

MCP vs A2A vs AGP vs ACP: Summary

Compare the four AI protocols in the MCP vs A2A vs AGP vs ACP summary table below:

Final Thoughts

Now you’re in the loop on MCP vs A2A vs AGP vs ACP—the fresh AI protocols powering smarter, faster, and stronger AI (just like how we do with our tech, initiatives, and hackathons 😉).

At Bright Data, we’re on a mission to make AI accessible for everyone, everywhere. That’s why we built our own MCP server to plug our web data solutions into any AI workflow.

Until next time—stay curious, stay bold, and keep exploring the wild frontier of AI with freedom. ✨