The Three Core Protocols of AI Agents 2.0: MCP, A2A, and AG‑UI

Overview

In AI‑driven applications three roles frequently appear: the user, an AI Agent, and external tools. To let these entities cooperate smoothly a set of standardized protocols is required. The article introduces the three most widely adopted protocols—MCP, A2A, and AG‑UI—and shows how they compose the communication skeleton of contemporary AI systems.

MCP Protocol

MCP (Model Context Protocol) was open‑sourced by Anthropic in November 2024. It standardises how an AI Agent invokes external tools such as weather services or databases, acting like a universal remote control for AI models.

Historical background: OpenAI added Function Calling to GPT‑4 and GPT‑3.5 in June 2023, followed by similar features from Google and Anthropic. However, each vendor used a different request format, forcing developers to write adapter code for every model.

By providing a common interface, MCP reduces integration effort. The protocol can be implemented via Function Calling, JSON‑RPC, RESTful APIs, or any structured communication that the model can generate.

The growth of MCP on GitHub (star history) shows rapid community adoption since March 2023, indicating its increasing importance for developers and enterprises.

A2A Protocol

A2A (Agent‑to‑Agent) was introduced by Google in March 2025 as a companion to MCP. While MCP focuses on tool access, A2A enables multiple AI Agents to exchange messages, coordinate tasks, and share capabilities.

Key features:

Unified message format to avoid “chicken‑talk‑duck” problems.

Discovery mechanism for agents to find collaborators.

Task‑splitting and assignment to specialised agents.

Capability advertisement so agents can advertise what they can do.

Security controls to ensure only authorised agents communicate.

Roles defined by the protocol:

User : authentication and permission source.

Client Agent : initiates a request.

Server Agent : executes the request.

Example: a WeatherAgent provides weather data, and a TripAgent plans activities based on that data. The workflow diagram (image) illustrates the request‑response cycle.

Sample Python code demonstrates a simple A2A interaction using Flask:

from flask import Flask, request, jsonify
app = Flask(__name__)
weather_data = {
    "2025-07-15": {"temperature": 25, "condition": "Sunny"},
    "2025-07-16": {"temperature": 18, "condition": "Rainy"},
    "2025-07-17": {"temperature": 22, "condition": "Cloudy"}
}
@app.route('/weather', methods=['GET'])
def get_weather():
    date = request.args.get('date')
    return jsonify(weather_data.get(date, {"error": "No data"}))
if __name__ == '__main__':
    app.run(port=5000)

Running the TripAgent service and sending a POST request with curl yields a JSON response such as:

{"trip_plan": "Great weather for hiking on 2025-07-15!"}

The article notes that A2A is still mainly in research and has not reached the deployment breadth of MCP.

AG‑UI Protocol

AG‑UI (Agent‑to‑UI) was released by CopilotKit as an open‑source standard that bridges backend agents and frontend interfaces. It solves the problem of inconsistent UI‑side implementations across different Agent frameworks (LangGraph, CrewAI, etc.).

Motivation: developers struggle with streaming Agent thoughts, showing tool progress, synchronising large data payloads, and handling user interruptions. AG‑UI uses Server‑Sent Events (SSE) to push structured JSON events to the frontend, enabling real‑time, incremental UI updates.

Typical event types:

TEXT_MESSAGE_CONTENT : incremental text output.

TOOL_CALL_START : indicates a tool has begun execution.

STATE_DELTA : sends only changed data (e.g., a single line of code).

AGENT_HANDOFF : hands the task to another Agent, similar to a relay race.

The protocol can be adopted with provided TypeScript and Python SDKs, making integration as easy as assembling building blocks.

Demo project structure (image) shows:

agui-demo/
├── agent_server.py   # backend Agent service
├── ui_app.py         # frontend Flask app
└── templates/
    └── dashboard.html # UI page

The backend emits SSE streams (state updates, notifications) that the frontend consumes via EventSource, updating status indicators, progress bars, and logs in real time.

Key advantages of AG‑UI:

Unidirectional real‑time push eliminates polling.

All messages are standardised JSON events.

Clear event types simplify frontend handling.

State synchronisation keeps UI in lockstep with Agent progress.

Overall, the three protocols together enable developers to build complex, production‑grade AI applications with consistent communication, reduced integration overhead, and richer user experiences.