The Difference Between MCP, A2A, and ACP Protocols in AI

June 25, 2025

Illustration of three AI protocols, MCP, A2A, and ACP, represented by colored cubes connected in a digital network, symbolizing their roles in AI agent communication and coordination.

As AI systems grow more modular and dynamic, a new generation of protocols is emerging, each playing a distinct role in how agents access tools, communicate with each other, and operate in complex environments. The most talked-about are MCP (Model Context Protocol), A2A (Agent-to-Agent), and ACP (Agent Communication Protocol).

They’re often lumped together, but they solve very different problems. In this article, we break down what each protocol is really for, how they differ, and why understanding them matters for anyone building next-gen AI systems.

MCP: Giving Models the Right Context, at the Right Time

Developed by Anthropic, the Model Context Protocol (MCP) is an open standard designed to connect AI assistants to external tools, APIs, and data sources, including content repositories, business systems, and developer environments. Instead of overloading prompts with static context, MCP enables models to dynamically pull in relevant data from external sources, assemble prompts on the fly, and invoke tools as needed.

It’s how LLMs stop being black boxes and start becoming context-aware systems that interact with your world.

What MCP enables:

Real-time context injection: pull from APIs, databases, files, whatever the model needs.
Tool invocation: let models trigger actions like fetchCustomerProfile or sendInvoice through standardized interfaces.
Modular prompt building: construct input dynamically based on session state or workflow logic.

Designed to be lightweight and flexible, MCP:

Works over HTTP(S) using JSON-based descriptors.
It is model-agnostic and compatible with any runtime that supports the spec.
Plays nicely with enterprise standards like OAuth2 and mTLS.

Common use cases include LLMs securely interacting with internal systems, generating outputs based on real-time data, or serving as intelligent intermediaries within enterprise workflows.

A2A: Teaching Agents to Collaborate

While MCP connects models to the world, A2A, the Agent-to-Agent, introduced by Google, connects models to each other.

A2A is an open protocol that complements MCP, which provides helpful tools and context to agents. It defines how agents discover, describe, and collaborate across platforms. Think of Agent-to-Agent as the protocol that gives your agents a common language, no matter who built them or where they’re running.

How it works:

Agent Cards: JSON documents describing what an agent can do, how to reach it, and how to authenticate.
Task delegation: agents negotiate roles and pass responsibilities fluidly.
Streamed communication: agents can share updates, outputs, and artifacts in real time.

A2A runs on standard web infrastructure — HTTPS, JSON-RPC — and supports things like scoped capabilities, secure messaging, and dynamic coordination.

Where it shines:

Multi-agent workflows that involve multiple departments, systems, or vendors.
Enterprise orchestration across HR, finance, CRM, and IT agents.
Research environments where agents co-create or analyze collaboratively.

ACP: Turning Siloed Agents into Interoperable Teammates

The Agent Communication Protocol (ACP), introduced by IBM’s BeeAI, is an open standard for agent-to-agent communication, designed to help AI agents collaborate freely across teams, technologies, and organizations. It addresses one of the biggest challenges in today’s AI ecosystem: fragmentation.

While MCP provides access to tools and data, ACP goes a step further by defining how agents interact with each other and operate collectively. It enables agents to exchange information, coordinate tasks, and communicate across different frameworks — all through a shared, universal language.

At its core, ACP is about interoperability and scale. It’s designed for modular, multi-agent environments where different components need to work together in real time, whether they’re built on different tech stacks or deployed across disparate systems.

Key features include:

Agent-to-agent communication via shared protocols and open governance.
Cross-framework compatibility, promoting reuse and reducing siloed architectures.
Support for both autonomous execution and collaborative workflows, with agents acting on behalf of users or other systems.

In multi-agent systems — like a suite of AI agents coordinating logistics, customer service, and analytics — ACP acts as the connective tissue. It ensures that agents can understand each other, share goals, and execute workflows with minimal friction.

It's particularly powerful in scenarios where:

Teams need to integrate agents built with different frameworks.
Enterprise systems require scalable, modular automation.
Human-AI collaboration and agent orchestration are key to business workflows.

ACP is about making AI agents interoperable and collaborative at any scale. And with its open standard and growing adoption, it’s shaping up to be a key layer in the future of agent-based architectures.

When to Use What?

While each protocol serves a unique purpose, they’re most powerful when seen as complementary parts of a broader architecture. Here’s how they compare side by side:

A comparison table highlighting the core function, communication method, best fit, and strengths of MCP, A2A, and ACP protocols in AI systems.

These protocols aren’t mutually exclusive. A robust AI system might combine them to cover different needs:

MCP feeds models with real-time business data and access to tools.
A2A coordinates actions among distributed agents.
ACP enables structured communication across diverse agent environments.

Together, they enable a new generation of intelligent systems — modular, composable, and built for collaboration at every layer.

What Comes Next?

As AI adoption expands across different industries and infrastructures, these protocols are no longer seen in isolation. Their combined use reflects a shift toward more integrated, agent-based architectures, where context, coordination, and communication work in sync.

Instead of choosing one over the others, forward-thinking teams are designing systems where MCP, A2A, and ACP coexist — each supporting a different layer of intelligence, from decision-making to execution.

At Elint, we see this layered approach as a foundation for building scalable, collaborative AI ecosystems that are ready for real-world complexity.

Do you want to architect smarter agent systems?

Let’s talk. Our team helps companies design and deploy AI-native applications built on modular, interoperable foundations.