If you’re building or exploring AI agents, you’ve likely hit a wall: agents built on different frameworks can’t talk to each other, and connecting them to real-world tools is messy and unreliable. Enter A2A (Agent-to-Agent) and MCP (Model Context Protocol)—two emerging open standard AI agent protocols, designed to solve these exact problems. A2A lets agents interoperate across ecosystems, while MCP connects them to the data and tools they need to function in real-world workflows. Together, they form the backbone of scalable, autonomous, and intelligent agentic systems.
What is A2A?
A2A stands for Agent-to-Agent protocol. The big issue with AI agents today is fragmentation—they’re built using different frameworks, APIs, tools, and by different companies. A2A fixes that by creating a universal language for AI agents to talk to each other, no matter who built them.
Think of A2A like English for AI agents. Just as English allows people from different countries to communicate, A2A enables AI agents from different platforms to interoperate securely and easily.
How It Works – 4 Key Concepts
- Agent Card
Think of this as the agent’s digital business card in JSON format. It tells others what the agent can do and how to interact with it. - A2A Server
This is the live bot running in the background, listening for tasks, doing the work, and returning results. It handles execution. - A2A Client
This can be a user-facing app or another agent. It reads the Agent Card, packages the task, sends it to the server, and receives the result. It acts as the bridge. - A2A Task
This is a single unit of work that gets passed between agents. It has a lifecycle—submitted, in-progress, completed—and provides a clean way to track the job.
Key Takeaways
- A2A is the future of scalable, interoperable AI agents.
- You can use any framework—LangGraph, Crew AI, Semantic Kernel, OpenAI SDK, etc.—as long as it supports A2A.
- This is month zero. Everything will get better: UIs, tooling, frameworks. What matters is understanding the foundations now.
What Are MCPs?
As AI assistants move from novelty to necessity in the modern workplace, one challenge continues to limit their full potential: access to real-world data. Even the most powerful language models struggle when siloed from the systems and information users rely on every day.
Model Context Protocol (MCP) is an open-source new universal standard that makes it easy to connect AI assistants to the systems where data lives, from content repositories to business tools to development environments.
Think about REST APIs—those are standardized ways for systems to talk to one another. Standards are what allow systems to scale and communicate efficiently.
LLMs, like ChatGPT, on their own, can’t do much. Sure, they can write poems or tell you about historical events, but they can’t take action. If you ask it to send an email, it’ll say it can’t.
The next evolution was connecting LLMs with tools (e.g., APIs, databases, web search). That’s when things started to get interesting—now LLMs could look things up, summarize emails, or even interact with spreadsheets using services like Zapier.
But here’s the problem: As you stack more tools, everything breaks down.
- Tools speak different “languages” (think: one’s in English, another in Spanish).
- APIs differ across providers.
- Updates or changes in one service can break your whole stack.
Enter MCP
MCP is a unifying layer between the LLM and all the tools or services it needs access to. It simplifies the chaos. It gives the LLM a single standard language to work with across tools and APIs.
Instead of manually integrating 5 tools in 5 ways, MCP becomes the bridge that standardizes communication between the LLM and everything else—databases, APIs, workflows, and more.
With MCP:
- You can ask the LLM to create a database entry, and it just knows how.
- It eliminates most of the boilerplate and edge-case handling.
- It’s scalable and developer-friendly.
How the MCP Ecosystem Works
- MCP Client
This is the LLM-facing app (e.g., Tempo, Cursor, Windsurf). - MCP Server
This is hosted by the service provider (e.g., a database company), and it knows how to expose the tool’s capabilities in a standardized way. - MCP Protocol
The glue that connects the client and server—a shared language they both understand.
Anthropic’s brilliance lies in how they’ve architected responsibility—it’s now up to service providers to make their APIs compatible by building and maintaining MCP servers. That’s why companies are scrambling to build and publish their own MCP repositories.
A2A vs MCP
As per official Google stance:
Agentic applications need both A2A and MCP. We recommend MCP for tools and A2A for agents.
Let’s clarify the difference:
- MCP (Model Context Protocol): Helps your AI agent connect to tools and data.
- A2A (Agent-to-Agent Protocol): Helps your AI agent connect with other agents.
These are not competitors—they work together. A2A helps agents communicate, while MCP helps them access the tools they need.
Ready to power AI agents with real-time, unified data? Fragmented systems are the biggest blocker to scalable AI. Knowi provides the enterprise-grade foundation agents need—a unified layer connecting structured, unstructured, and API-driven sources. Book a personalized demo to see how you can bring all your data together in one place.
Frequently Asked Questions (FAQs)
What is the A2A protocol in AI agents?
A2A (Agent-to-Agent) is a communication protocol that allows AI agents built on different frameworks to talk to one another. It acts as a universal language for agents, enabling interoperability regardless of platform or tooling.
How does A2A improve AI agent collaboration?
A2A introduces key building blocks like Agent Cards, Tasks, Clients, and Servers to standardize how agents discover each other, share tasks, and track progress—making agent collaboration scalable and secure.
What is the MCP protocol in AI?
MCP (Model Context Protocol) is an open standard that allows AI models and agents to connect with tools, APIs, and real-world data systems. It acts as a bridge between Agents and the environments they need to interact with.
Why do AI agents need both A2A and MCP?
A2A helps agents talk to other agents, while MCP helps agents interact with tools and data. Together, they enable AI agents to operate autonomously and cooperatively in complex, real-world environments.
Can I use A2A and MCP with existing frameworks like LangGraph or OpenAI SDK?
Yes. A2A and MCP are designed to be framework-agnostic, meaning you can implement them with popular agent-building tools like LangGraph, Crew AI, Semantic Kernel, and more—as long as they support the protocol.
What’s the difference between A2A and MCP?
A2A is for agent-to-agent communication, while MCP connects agents to external systems and tools. A2A focuses on interoperability between agents, and MCP focuses on access to capabilities and context from external sources.
