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Agentic AI Protocols (MCP, A2A, ACP)

本课程是 Hands-on Agentic AI: Building Intelligent Agents 专项课程的一部分

位教师：Harshita Gulati

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

您将学到什么

A deeper understanding of the core principles underlying multi-agent communication and collaboration
Insight into the structural and functional aspects of MCP, A2A, and ACP protocols
Ability to critically analyze and refine information exchange formats and negotiation flows.

您将获得的技能

您将学习的工具

Model Context Protocol

要了解的详细信息

可分享的证书

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了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Hands-on Agentic AI: Building Intelligent Agents 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有3个模块

Navigating Multi-Agent Communication Protocols is an intermediate-level course designed for AI engineers and system architects who need to build sophisticated multi-agent systems where effective communication and coordination are critical. In today's AI landscape, isolated agents are obsolete—success depends on seamless collaboration between multiple intelligent agents working toward shared objectives.

This course provides comprehensive coverage of three essential communication protocols: Multi-Agent Communication Protocol (MCP) for standardized communication, Agent-to-Agent (A2A) for dynamic task coordination, and Agent Collaboration Protocol (ACP) for complex workflow orchestration. Through real-world case studies from organizations like Anthropic, Google, and IBM, hands-on implementation exercises, and practical design challenges, you'll learn to strategically select and integrate these protocols to solve complex coordination problems. Whether you're building autonomous systems, enterprise AI solutions, or collaborative AI applications, this course equips you with the knowledge and skills to transform chaotic agent interactions into orchestrated, efficient collaborations that deliver measurable business value.

单元详情

In this foundational lesson, learners will explore the architecture and components of the Multi-Agent Communication Protocol (MCP), examining how it facilitates effective information exchange between AI agents. Through real-world examples from Anthropic's implementation and industry case studies, learners will analyze MCP's structural elements, understand its role in standardizing agent communication, and practice identifying optimal scenarios for MCP deployment.

涵盖的内容

3个视频2篇阅读材料1个作业

3个视频总计18分钟

Introduction and Welcome5分钟
MCP Fundamentals: Architecture and Core Components8分钟
When to Use MCP: Scenarios and Decision Framework6分钟

2篇阅读材料总计19分钟

Welcome to the Course: Course Overview4分钟
MCP Implementation Strategies and Best Practices15分钟

1个作业总计15分钟

HOL: Analyze MCP Architecture for a Given Scenario15分钟

This lesson focuses on Agent-to-Agent (A2A) protocols and their application in coordinating tasks among AI agents. Learners will examine Google's implementation of A2A in autonomous systems, understand the strategic differences between A2A and MCP, and practice designing coordination mechanisms for complex multi-agent tasks. Through hands-on exercises and real-world case studies, learners will develop skills in task distribution, coordination patterns, and performance optimization in A2A environments.

涵盖的内容

3个视频1篇阅读材料1个作业

3个视频总计18分钟

A2A Protocol Fundamentals: Beyond Point-to-Point Communication7分钟
Task Coordination Strategies: When Agents Need to Work Together6分钟
Performance Optimization in A2A Systems5分钟

1篇阅读材料总计10分钟

A2A Implementation Patterns and Coordination Strategies10分钟

1个作业总计15分钟

HOL: Design A2A Task Coordination for Multi-Agent Scenario15分钟

In this final lesson, learners will examine the Agent Collaboration Protocol (ACP) and its application in enterprise environments for collaborative execution. They'll analyze IBM's implementation approach, understand ACP's unique strengths in managing complex collaborative workflows, and develop strategies for optimizing collaborative execution in diverse AI systems. The lesson culminates with a comprehensive capstone project where learners design a multi-protocol implementation plan, and a graded assessment that tests their understanding across all three protocols.