Mastering MCP and ACP

[mitsumi]

Published 8/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 1m | Size: 1.18 GB

Model Context Protocl (MCP) and Agent Communication Protocl (ACP) for AI-Driven Workflows


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)
(Optional) Experience with distributed systems
Description
Machine Learning and Artificial Intelligence are not new concepts. The concept of Machine Learning was introduced in the early 1950s, and AI was used for the first time at the Dartmouth Conference in 1956. However, as computers became more powerful and more accessible, more complex machine learning models have been developed in recent years. The advancement in the field of AI reached its peak in November 2022 when ChatGPT was introduced by OpenAI. ChatGPT allowed everybody to directly communicate with an AI system that was built upon a Large Language Model (LLM).LLMs are a network of machine learning models that have been trained on almost all data accessible on the Internet and have the capability to understand and respond in human language. This convenience brought AI into our daily lives and started a new wave of AI-driven systems and workflows.Systems that needed complex algorithms to work now use LLMs to understand their environment, make complex decisions, and interact with external systems. As complex AI-based systems emerged, the Model Context Protocol (MCP) was introduced as an early standard so developers and companies could use similar architecture and protocols to build tools that provide AI-based systems with environmental data (context).Agents also came to life to build complex workflows that are orchestrated by AI. We call this Agentic AI. Not only can Agents communicate with Large Language Models (or AI in general), they can communicate with each other too. Similar to Microservices, Agents are independent processes that require a protocol to exchange information with other Agents and with the AI. This protocol has been developed as Agent Communication Protocol or ACP.MCP and ACP are inseparable and are both used to develop AI-based systems. MCP provides the What, and ACP is about the How. As an enthusiast, you must know when and how MCP and ACP are used. That is why both MCP and ACP are taught in this course.The course is suitable for everyone at any proficiency level in AI. Whether you are a leader, a product manager, a solution architect, or a developer, you can learn so much from this course. If you are into programming, you will specifically enjoy this course because the development of MCP and ACP is explained in detail using Python and common Software Development Kits (SDKs).By the end of this course, you'll have the technical and conceptual foundation to build advanced AI systems that go beyond single-turn interactions: systems that can maintain context, coordinate across components, and adapt intelligently to evolving tasks. Whether building prototypes or scaling up enterprise-grade agentic applications, this course gives you the tools and confidence to lead the way in the next wave of 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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