AI Agent Protocol Comparison: MCP vs A2A vs AG-UI — Who Will Become the Standard?

技术架构

Why Do AI Agents Need Protocols?

In 2026, AI Agents have evolved from single-machine toys to enterprise infrastructure. But a fundamental question remains: How should Agents communicate — with each other, with tools, and with users?

This is like the early internet — before TCP/IP unified everything, every network company used its own protocol. Today's AI Agent ecosystem is in the same "Babel" phase.

An Agent ecosystem without unified protocols is like phone chargers before USB-C — every brand is different.

Three major protocols have emerged, each solving communication at a different layer:

┌─────────────────────────────────────────────────────┐
│                    User Interface Layer               │
│                  ★ AG-UI Protocol ★                  │
│          (Standard for Agent-User interaction)       │
├─────────────────────────────────────────────────────┤
│                  Agent Collaboration Layer            │
│                  ★ A2A Protocol ★                    │
│          (Standard for Agent-Agent communication)    │
├─────────────────────────────────────────────────────┤
│                  Tool Invocation Layer                │
│                  ★ MCP Protocol ★                    │
│          (Standard for Agent-Tool communication)     │
└─────────────────────────────────────────────────────┘

MCP: The De Facto Standard for Tool Invocation

Protocol Positioning

MCP (Model Context Protocol) was released by Anthropic in November 2024 and transferred to Linux Foundation governance in late 2025. It solves the problem of how Agents call external tools and data sources.

Core Architecture

┌──────────────────┐     JSON-RPC     ┌──────────────────┐
│   MCP Client     │ ◄──────────────► │   MCP Server     │
│  (runs in Host)  │    stdio/SSE     │  (tool provider) │
└──────────────────┘                  └──────────────────┘

Three Primitives:
- Tools: Functions AI can call (e.g., query_database)
- Resources: Data AI can read (e.g., file contents)
- Prompts: Predefined prompt templates (e.g., code_review_prompt)

Key Features

const server = {
  name: "postgres-tools",
  version: "1.0.0",
  tools: [
    {
      name: "query",
      description: "Execute SQL query",
      inputSchema: {
        type: "object",
        properties: {
          sql: { type: "string" },
          limit: { type: "number", default: 100 }
        }
      }
    }
  ],
  resources: [
    {
      uri: "postgres://schemas/public",
      name: "Public Schema",
      mimeType: "application/json"
    }
  ]
};

Ecosystem Data (June 2026)

Metric Data
GitHub repositories 10,000+
Official MCP Servers 3,000+
Supported AI tools Claude Desktop, Cursor, Continue, Tongyi Lingma, etc.
SDK languages TypeScript, Python, Java, Go, Rust
Governance Linux Foundation (Agentic AI Foundation)

Strengths & Limitations

Dimension Assessment
Strength Most mature ecosystem, de facto standard established
Strength Complete SDKs with 5 official language support
Strength Linux Foundation governance ensures neutrality
Limitation Only solves Agent-Tool communication, not Agent-Agent
Limitation No built-in UI interaction protocol
Limitation Single-call model, lacks long-flow orchestration

A2A: Agent-to-Agent Communication Protocol

Protocol Positioning

A2A (Agent-to-Agent) was released by Google in April 2025, solving how Agents collaborate with each other. If MCP is the standard for "Agent calls tools," A2A is the standard for "Agent calls Agents."

Core Architecture

┌──────────┐     A2A Protocol     ┌──────────┐
│ Agent A  │ ◄──────────────────► │ Agent B  │
│(Data Analyst)│    HTTP+JSON     │(Report Gen)│
└──────────┘                      └──────────┘

Core Concepts:
- Agent Card: Agent's "business card" declaring capabilities
- Task: Unit of collaboration between Agents
- Message: Communication within a Task
- Artifact: Output files/data from a Task

Key Features

{
  "name": "data-analyst",
  "description": "Data analysis agent supporting SQL queries and statistical computation",
  "url": "https://agent.company.com/data-analyst",
  "capabilities": {
    "streaming": true,
    "pushNotifications": true
  },
  "skills": [
    {
      "id": "sql-query",
      "name": "SQL Query",
      "description": "Execute SQL query and return results",
      "input": { "type": "object", "properties": { "sql": { "type": "string" } } }
    }
  ]
}

Ecosystem Data (June 2026)

Metric Data
GitHub repositories 2,500+
Supported platforms Google Cloud, AWS Bedrock
SDK languages Python, TypeScript, Java
Governance Google-led (not transferred to foundation)

Strengths & Limitations

Dimension Assessment
Strength Fills the Agent-Agent communication gap
Strength Agent Card mechanism is elegant, strong service discovery
Strength Supports long flows and multi-round collaboration
Limitation Ecosystem far less mature than MCP
Limitation Google-led, neutrality concerns
Limitation Interoperability with MCP not yet standardized

AG-UI: Agent-UI Interaction Protocol

Protocol Positioning

AG-UI (Agent-UI Protocol) was proposed by CopilotKit in late 2025, solving how Agents interact with users through UI. Core idea: Agents shouldn't just return text — they should be able to manipulate UI components.

Core Architecture

┌──────────────────┐     AG-UI Protocol     ┌──────────────────┐
│   Frontend (UI)  │ ◄────────────────────► │   Agent Backend  │
│  React/Vue/Svelte│    Event Stream        │  (LLM + Tools)   │
└──────────────────┘                        └──────────────────┘

Core Event Types:
- TextMessageEvent: Text messages
- StateSnapshotEvent: State snapshots
- ToolCallEvent: Tool calls (frontend can intercept/confirm)
- StepEvent: Execution steps
- ErrorEvent: Error handling

Ecosystem Data (June 2026)

Metric Data
GitHub repositories 800+
Supported frameworks React, Vue, Svelte
SDK languages TypeScript (primarily)
Governance CopilotKit community

Comprehensive Comparison

Architecture Comparison

Dimension MCP A2A AG-UI
Communication direction Client → Server Agent ↔ Agent UI ↔ Agent
Transport stdio / SSE HTTP + JSON Event Stream
Data format JSON-RPC 2.0 JSON (RESTful) SSE/WebSocket
Auth OAuth 2.0 / API Key OAuth 2.0 / Mutual TLS Session Token
State management Stateless Task state machine Frontend state sync
Streaming SSE SSE Native SSE

Ecosystem Maturity Comparison

Dimension MCP A2A AG-UI
Release date 2024.11 2025.04 2025.12
GitHub repos 10,000+ 2,500+ 800+
Official SDKs 5 languages 3 languages 1 language
Big-tech support Anthropic, Cursor, Tongyi Google, AWS CopilotKit
Governance Linux Foundation Google Community
Production cases Abundant Moderate Few

Use Case Comparison

Scenario Recommended Protocol Reason
Agent connects to DB/API MCP Tool invocation is MCP's core
Agent reads filesystem MCP Resources primitive natively supports
Multi-Agent collaboration A2A Agent Card + Task designed for collaboration
Long-flow orchestration A2A Task state machine supports multi-round
Agent-driven frontend UI AG-UI Streaming events fit frontend rendering
Building Copilot apps AG-UI Bidirectional interaction is core UX

Three-Protocol Synergy: Best Practice

The three protocols are complementary, not mutually exclusive. A complete enterprise Agent system typically uses all three:

┌───────────────────────────────────────────────────────┐
│                    User Interface (React)               │
│                  ← AG-UI Protocol →                    │
├───────────────────────────────────────────────────────┤
│                  Orchestrator Agent                     │
│                  ← A2A Protocol →                      │
├──────────┬──────────┬──────────┬──────────────────────┤
│ Agent A  │ Agent B  │ Agent C  │                      │
│ ← MCP → │ ← MCP → │ ← MCP → │                      │
│ DB Tools │ API Tools│ File Tools│                     │
└──────────┴──────────┴──────────┴──────────────────────┘

Spring Boot Integration Example

@Configuration
public class AgentProtocolConfig {

    @Bean
    public McpClient mcpClient() {
        return McpClient.builder()
            .transport(McpTransport.HTTP_SSE)
            .serverUrl("http://localhost:8081/mcp")
            .build();
    }

    @Bean
    public A2AClient a2aClient() {
        return A2AClient.builder()
            .agentCardUrl("http://localhost:8082/.well-known/agent.json")
            .auth(oAuth2Config())
            .build();
    }

    @Bean
    public AgUiHandler agUiHandler() {
        return AgUiHandler.builder()
            .streamType(AgUiStream.SERVER_SENT_EVENTS)
            .stateSync(true)
            .build();
    }
}

Who Will Become the Future Standard?

My Assessment

MCP is already the de facto standard — this position won't shake in 2026. A2A and AG-UI each have value in their niches, but are unlikely to replace MCP.

More likely future:

  1. MCP continues dominating tool invocation — Linux Foundation governance ensures neutrality
  2. A2A becomes the de facto standard for Agent collaboration — Google's push is significant
  3. AG-UI may be absorbed by MCP or A2A — as an extension of upper-layer protocols

Ultimate Prediction

2026 Q3: A2A-MCP interoperability spec released
2026 Q4: AG-UI core concepts incorporated into MCP spec extension
2027 H1: Three protocols merge into "Agentic Protocol Suite"
2027 H2: ISO/IEC starts Agent communication protocol international standardization

Bottom line: MCP is today's standard, A2A is tomorrow's supplement, AG-UI is the day-after's icing. Convergence is the endgame.

Advice for Developers

Your Scenario Learn Now Watch for Future
Building AI apps MCP (must master) A2A interoperability
Multi-Agent systems MCP + A2A Protocol convergence
Copilot/assistant apps MCP + AG-UI AG-UI maturity
Enterprise platforms All three Standardization progress

Learning path: MCP → A2A → AG-UI, ordered by ecosystem maturity — learn the most useful first.

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