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From Chatbots to Colleagues: How MCP, ACP, and A2A Are Powering the Agentic AI Revolution

Author

NISHANT KUMAR
April 23, 2025

What if your AI could not only understand instructions — but delegate, collaborate, and execute across tools

Welcome to the future of automation: where autonomous agents, not just models, drive real work. At the core of this evolution are three emerging protocols:

  • MCP (Model Context Protocol)

  • ACP (Agent Communication Protocol)

  • A2A (Agent-to-Agent Interactions)

These protocols are transforming how agents think, talk, and act — creating a foundation for scalable, collaborative, goal-oriented AI systems.

Why Agentic AI Needs Protocols

Large Language Models (LLMs) are powerful — but they weren’t designed to:

  • Coordinate tasks,

  • Share long-term memory,

  • Access external tools, or

  • Work with other agents.

Agentic AI aims to solve that — by wrapping LLMs in persistent, tool-using, goal-driven agents. But without common standards, every system ends up reinventing the wheel.

That’s where MCP, ACP, and A2A come in — each solving a piece of the coordination puzzle.

Protocol Attribution: Who's Behind MCP, ACP, and A2A?

Protocol

Originally Created / Promoted By

Remarks

MCP (Model Context Protocol)

Cursor (a dev-focused AI IDE) + OpenPipe, with OSS collaboration

While Google has internal context-formatting tools (e.g. for Gemini), MCP as it's known in the open-source GenAI space was proposed and implemented by Cursor, and is being adopted across projects like LangChain, OpenPipe, and others. Google may have similar internal formats but they haven’t open-sourced something called “MCP” yet.

ACP (Agent Communication Protocol)

No single company; inspired by FIPA ACL, implemented by projects like LangGraph, CrewAI, AutoGen

ACP is more of a design pattern than a formal standard right now. IBM and academic orgs contributed to early ACLs in the 2000s. Today, open-source LLM-based frameworks are adapting it for modern agent use.

A2A (Agent-to-Agent)

Popularised by Anthropic in their research on “agent swarms” and long-term planning — also explored by AutoGPT, LangGraph

You're right that Anthropic has been a key influence in A2A thinking, especially in papers and demos on multi-agent collaboration and internal assistant-to-assistant communication. But the term “A2A” is now broadly used by many projects building open agent stacks (e.g., LangGraph).

What is MCP (Model Context Protocol)?

MCP is the “language” of context. It standardises how information is passed to a model: tools, memory, roles, metadata — all in a clean, structured format.

Why it matters:
Instead of hardcoding how a model should interact with a Slack thread or file system, MCP provides a unified context schema.

Think of MCP as the “JSON API” for prompting agents with real-world context.

Who’s behind it:

  • Cursor (code editor using AI for navigation)

  • OpenPipe, LangChain, OSS contributors

What is A2A (Agent-to-Agent)?

A2A handles the actual interactions between agents — like a messaging bus or runtime layer. This is where agents:

  • Discover each other,

  • Pass tasks or data,

  • Synchronise their state.

A2A often runs on top of ACP, enabling live coordination, retries, and event triggers across systems.

A2A is the runtime “internet” of autonomous agents.

What is ACP (Agent Communication Protocol)?

ACP defines how agents talk to each other — not just sending data, but expressing intention, requests, and trust.

Core concepts include:

  • Structured message types: ask, tell, propose, delegate

  • Embedded context and reasoning

  • Role negotiation and error handling

ACP enables agents to collaborate like a team, not just pass messages.

ACP Architecture - From IBM BeeAI

Examples:

  • LangGraph for stateful workflows

  • AutoGen for multi-agent collaboration

  • Open-source orchestrators and custom agent hubs

How These Protocols Automate Real Tasks Together

Here’s how MCP, ACP, and A2A combine into a powerful automation stack:

Example: An AI Coding Assistant

Imagine a dev agent that can:

  1. Receive a message in Slack (A2A)

  2. Parse it as a task with intent (ACP)

  3. Look into local files + GitHub issues (MCP)

  4. Generate a plan and propose a solution (ACP)

  5. Ask another agent to commit code + reply in Slack (A2A + MCP)

This creates a multi-agent workflow where:

  • MCP handles input/output context

  • ACP manages coordination

  • A2A enables live orchestration

The result? - End-to-end task automation powered by reasoning agents.

Comparison Snapshot these protocols

Feature / Purpose

MCP (Model Context Protocol)

ACP (Agent Communication Protocol)

A2A (Agent-to-Agent)

Primary Role

Standardises how context is sent to models

Defines how agents communicate with intention + structure

Enables runtime agent invocation and coordination

Focus

Data & context structure

Message semantics & intent handling

Agent discovery, invocation, scheduling

Typical Use

Inject tool state, memory, tasks into model input

Request/response between agents (e.g., delegate task)

Runtime for triggering & connecting agents

Who Uses It

Cursor, OpenPipe, LangChain

CrewAI, AutoGen, LangGraph, some open agents

LangGraph, AutoGPT, custom orchestrators

Spec Level

JSON/Schema-driven, focused on structure

Messaging protocol (verbs like ask, tell, propose)

Implementation pattern / communication channel

Interoperability

Allows plug-and-play tools

Enables multi-agent systems

Powers workflows and orchestration

Agent Role

Helps agents understand their environment

Helps agents talk and collaborate

Helps agents find and activate each other

Example

Unified API for accessing Slack, GitHub, files

Agent A delegates task to Agent B via ask message

Agent X calls Agent Y during a workflow

Outcome

Better model performance, cleaner tool interfaces

Reasoning & planning across agents

Scalable, autonomous workflows across time & tools

In the next blog:

We’ll walk through building a real MCP Client + Server:

  • Create a structured model context payload,

  • Connect to tools like file systems or apps,

  • Build a local server that serves context to agents or models.

This will show you how to build agent interfaces that scale.

Stay tuned and subscribe — you won’t want to miss it!

Final Thoughts: Protocols Make Agents Real

MCP, ACP, and A2A aren't just buzzwords. They're the missing glue that turns smart models into smart systems. Together, they enable:

✅ Seamless tool use
✅ Inter-agent collaboration
✅ Modular automation at scale

If you’re building an agent framework, AI product, or toolchain — these are the foundations you want to build on.

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