The Secret Language of AI Agents: Demystifying MCP, A2A, and Agentic Protocols

Introduction

As organizations race to harness the power of AI, we’re seeing a new era of “multi-agent” systems—pipelines where multiple specialized agents (retrievers, planners, reasoners, validators, and more) collaborate to solve complex problems.
But beneath the surface, there’s a secret language that lets these agents coordinate, cooperate, and build on each other’s work: protocols like MCP and A2A.

Yet, for many engineers, product managers, and even AI leaders, these terms can feel like a confusing blur.
What exactly is an MCP? What’s A2A? How do tools like LangGraph, OpenAI’s SDK, Crew AI, or AutoGen use them?
Let’s break it all down—with real-world analogies and zero jargon.

From Team Projects to Multi-Agent AI

Imagine you’re back in school, working with friends on a group assignment:

• Planner: Decides the steps and divides the tasks.

• Researcher: Finds the articles and information you’ll need.

• Writer: Uses those sources to create the final report.

• You all use a shared notebook to jot down your work, decisions, and sources as you go.

This notebook keeps everyone on the same page. Each friend only sees (and works on) the info that’s already in the notebook—not the whole library or all your personal notes. If a teacher wants to check your process, everything is documented—step by step.

Meet MCP: The Project Notebook for AI Agents

In the AI world, MCP (Model Context Protocol) is that shared notebook:

• It’s a structured context object that travels with the workflow, from agent to agent.

• Each agent reads what’s already in the context, adds its work/results, and passes it on.

• At the end, MCP tells the whole story: the original question, each step, what data was retrieved, and how the final answer was formed.

Why is this powerful?

• Focus: Agents only access relevant information—minimizing data exposure and boosting privacy.

• Traceability: You can always see who/what contributed to each step.

• Teamwork: Agents can pick up where others left off—even if built by different teams or running on different servers.

A2A: How Agents Talk

If MCP is the notebook, A2A (Agent-to-Agent protocols) are the conversations and hand-offs between teammates:

• “Hey, researcher, can you look up these articles and add them to the notebook?”

• “Writer, here’s the info—can you summarize it for the final report?”

In code, this is a message, function call, or API request that passes the context (MCP) from one agent to the next, along with instructions or questions.

Scaling Up: How Organizations Use MCP and A2A

For a single school project, one notebook is enough.
But imagine an entire organization running hundreds or thousands of AI workflows at once:

• Each project (customer query, business task) gets its own digital notebook (MCP context).

• A central MCP server keeps all notebooks organized, up-to-date, and accessible to the agents that need them.

• Agents only see and use the info that’s in their project’s notebook—never the company’s entire database.

Managers and auditors can later review any notebook to see what happened, when, and why.

Where Do Other Protocols Fit?

Beyond MCP and A2A, modern agentic AI systems use a variety of protocols:

• Function Calling: Lets an AI agent call external tools or APIs—like a researcher asking a librarian for a specific book.

• Plugin Protocols: Standardized ways to plug in new tools or data sources—think “adding a new specialist to the team.”

• Transport Protocols: How agents communicate over the network (JSON, REST, gRPC)—like passing the notebook around digitally.

How Today’s Frameworks Make It Happen

Here’s how leading frameworks put these ideas into practice:

• LangGraph: Lets you build agent workflows as a directed graph, with MCP as the context flowing along the edges, and A2A as the communication between nodes.

• OpenAI SDK (Function Calling/Agents API): The model plans steps, uses MCP-like context to track state, and A2A (function calls) to coordinate actions.

• Crew AI: Organizes agents as a “crew” with defined roles, all working on a shared project memory (MCP), passing tasks to each other (A2A).

• AutoGen: Explicitly uses agent-to-agent messaging (A2A), with a shared context evolving as the workflow progresses.

Why Does This Matter?

• Security & Privacy: Agents only work with what’s in the context—not the full corporate data vault.

• Scalability: Hundreds of workflows can run in parallel, each with its own context, safely coordinated.

• Auditability & Compliance: Every decision, data point, and agent action is recorded and reviewable.

• Innovation: Teams can build new agents or tools that “plug in” to the shared context, enabling fast iteration and modular design.

The Bottom Line

MCP and A2A are the glue that makes multi-agent AI work—tracking every step, focusing each agent on what matters, and enabling robust teamwork, just like Git revolutionized code collaboration for developers.

As agentic AI goes mainstream, these protocols will become the “Git and GitHub” of the AI world—essential for anyone building scalable, transparent, and safe AI systems.

Ready to Build?

If your team is venturing into multi-agent AI, start by designing your project notebook (MCP schema), clarify your agent hand-off rules (A2A), and choose a framework that fits your scale and needs.
You’ll avoid confusion, unlock auditability, and empower true AI teamwork.

Got questions or want to see code samples, architectures, or more analogies? Drop me a comment or connect! Let’s make AI agent systems as clear and powerful as possible.

Happy collaborating!