Understanding A2A, ACP, and MCP in the Agentic AI World Through the Lens of Human Communication

Introduction

Imagine a bustling city filled with people each person has their own knowledge, skills, and tools. Some are doctors, some are chefs, some are engineers. They solve problems, make decisions, and interact with others. Now, imagine these people are AI agents in a digital city. To understand how they operate and collaborate, we’ll explore three key protocols: MCP, ACP, and A2A through a human analogy.

MCP:

Model Context Protocol Like a Person Learning from the World
Think of MCP as the way an individual gathers knowledge from their environment to solve daily problems.

Just like a person reads books, checks calendars, uses calculators, or asks for directions, an AI agent uses MCP to access external tools, APIs, databases, and memory. MCP is the protocol that allows an agent to understand its context, remember past interactions, and use tools intelligently.

Human Analogy:

Imagine you're planning a trip. You check your calendar, look up flights, read reviews, and use a budgeting app. You’re gathering context from various sources to make informed decisions. That’s MCP in action it’s how an agent becomes “situationally aware.”

Key Features of MCP:

1. Structured access to tools and data
2. Persistent memory and context
3. Stateful interactions across sessions
4. Modular and dynamic context injection

ACP:

Agent Communication Protocol Like a Common Language Between People
ACP is the shared language that allows people (agents) to talk to each other, even if they come from different backgrounds or speak different dialects.

In the AI world, agents are often built using different frameworks (LangChain, CrewAI, BeeAI, etc.). Without a common protocol, they can’t easily collaborate. ACP solves this by providing a RESTful, open standard for agents to exchange messages, delegate tasks, and coordinate actions.

Human Analogy:

You meet someone from another country. You both speak English as a second language it’s not perfect, but it works. You can share ideas, ask for help, and collaborate. That’s ACP a universal communication bridge.

Key Features of ACP:

REST-based communication

1. Synchronous and asynchronous messaging
2. Streaming and multimodal interactions
3. Stateless and stateful operations
4. Agent discovery and coordination

A2A:

Agent-to-Agent Protocol Like Global Communication Between People
A2A is the protocol that enables people (agents) to communicate across cities, countries, or even continents — regardless of their local customs or technologies.

While ACP focuses on how agents talk, A2A focuses on who they talk to and how they collaborate. It’s designed for interoperability across ecosystems, allowing agents built by different vendors to discover each other, share capabilities, and work together on complex tasks.

Human Analogy:

You’re in Miami and want to coordinate a global conference. You call a hotel manager in Tokyo, a chef in Paris, and a translator in Nairobi. You don’t need to know their internal systems you just need a shared protocol to collaborate. That’s A2A.

Key Features of A2A:

1. Agent discovery via Agent Cards
2. JSON-RPC and HTTP-based communication
3. Secure, scalable collaboration
4. Support for long-running tasks and multimodal data
5. Vendor-neutral, open-source

Putting It All Together: The Agentic Ecosystem

Let’s revisit our city analogy:

MCP is how each person learns, remembers, and uses tools.
ACP is the shared language they use to talk to others nearby.
A2A is the global communication protocol that lets them collaborate across borders.

Together, these protocols form the foundation of the agentic AI world, enabling autonomous agents to reason, act, and collaborate just like humans in a connected society.

Thanks
Sreeni Ramadorai