MCP and ACP for Smarter AI Agents

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 giant leap in 2022 with the rise of ChatGPT, bringing powerful Large Language Models (LLMs) into everyday life. But building truly intelligent systems goes far beyond a single AI conversation.That is where Model Context Protocol (MCP) and Agent Communication Protocol (ACP) come in. MCP gives AI the context it needs - the "what" - while ACP enables agents to coordinate and act - the "how". Together, they form the backbone of agentic AI: systems where AI agents think, decide and work together to solve complex problems.These protocols are not just technical standards; they are enablers of the next generation of AI-driven applications. MCP provides AI models with relevant, real-time information from external sources, ensuring decisions are made with the right context. ACP allows multiple agents, and even different AI systems, to communicate effectively and collaborate on tasks. Combined, they make it possible to build AI ecosystems that are more reliable, scalable and adaptable than ever before.In this course, you will explore both the concepts and the practical skills needed to design and build MCP and ACP-powered systems. We will cover the fundamentals, their role in modern AI architectures, and how they are applied in real-world projects. Leaders, solution architects, product managers and developers will all find value in the lessons. If you are a developer, you will particularly enjoy the hands-on sections, where we implement MCP and ACP using Python and widely used Software Development Kits (SDKs).By the end of the course, you will be able to design and build AI systems that maintain context, coordinate across multiple components, and adapt intelligently to changing requirements. Whether you are building a prototype or scaling an enterprise-grade solution, you will have the knowledge and confidence to leverage MCP and ACP to create innovative, high-performing AI applications.This course is suitable for learners of all proficiency levels and is designed to give you both the strategic understanding and the technical skills to lead the way in 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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