Model Context Protocol (MCP) and A2A for Smarter AI Agents

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Model Context Protocol (MCP) and A2A for Smarter AI Agents
Last updated 5/2026
Created by Aref Karimi
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English + subtitle | Duration: 44 Lectures ( 3h 40m ) | Size: 1.67 GB

Learn Model Context Protocol (MCP) and Agent to Agent (A2A) Protocol an Skills to Build Powerful AI Systems
What you'll learn
⚡ Understand the fundamentals of MCP and ACP and their roles in AI systems
⚡ Learn to implement MCP for managing model context in AI workflows
⚡ Master ACP for enabling efficient communication between AI agents
⚡ Build practical projects using MCP and ACP in simulated environments
⚡ Explore best practices for designing scalable and secure AI communication systems
Requirements
❗ Basic understanding of AI and machine learning concepts
❗ Familiarity with Python Programming
❗ Knowledge of Networking Basics (e.g., APIs, sockets)
Description
Artificial Intelligence took a major leap with the rise of Large Language Models like ChatGPT. However, building truly intelligent AI systems goes far beyond having a conversation with an AI assistant such as ChatGPT. Modern AI applications rely on multiple autonomous AI agents that require shared context, coordination, and collaboration.
This is where Model Context Protocol (MCP) and Agent to Agent Protocol (A2A) become essential. Model Context Protocol enables AI agents to access structured, real-time context from external tools, services, and data sources, ensuring decisions are made with accurate and relevant information. Agent to Agent Protocol focuses on how multiple AI agents communicate, delegate tasks, and work together as a coordinated system rather than operating in isolation.
In this course, you will learn how MCP and A2A are used together to design smarter, interoperable AI systems. You will explore how these protocols fit into modern AI architectures and how they enable scalable multi-agent workflows built on top of Large Language Models.
The course combines clear conceptual explanations with practical, hands-on examples. Developers will implement Model Context Protocol and Agent to Agent interactions using Python and widely used SDKs. The course concludes with a SIM activation project, where you will build a website backed by an AI workflow that allows telco customers to activate their SIM cards.
If you are less hands-on, such as a leader, solution architect, or product manager, there is a dedicated section designed for non-technical roles. You will gain a solid understanding of how MCP tools, Skills and A2A support real-world AI applications and how they apply in business contexts.
By the end of the course, you will be able to build AI systems that maintain context, collaborate across agents, and adapt intelligently to changing Requirements. This course is suitable for all proficiency levels and equips you with both strategic insight and practical skills for modern AI development.
Who this course is for
⭐ AI developers and machine learning engineers
⭐ Data scientists interested in AI system integration
⭐ System architects designing agent-based AI solutions
⭐ Intermediate to advanced learners with basic knowledge of AI, Python, and networking concepts
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https://www.udemy.com/course/mcp-and-a2a-ai-agents

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