Overview

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This comprehensive Agentic AI with Model Context Protocol Training course builds foundational skills in AI connectivity, interoperability, and Agentic AI systems using MCP. Learn how hosts, clients, servers, and communication layers connect AI applications with external tools, data sources, and services. Explore MCP architecture, function calling, session management, Google A2A, Python SDK implementation, and risk management through practical examples. Build secure, connected, and scalable AI applications powered by Model Context Protocol. By the end of this course, you will be able to: - Understand MCP Foundations: Explore AI connectivity and interoperability - Analyze MCP Architecture: Understand hosts, clients, and servers - Implement Function Calling: Connect AI with external tools and services - Manage MCP Sessions: Control communication lifecycles and connections - Explore Communication Protocols: Compare MCP and Google A2A - Implement MCP with Python SDKs: Build connected AI applications - Analyze MCP Risks: Understand security and operational considerations - Build Agentic AI Solutions: Create scalable AI systems Ideal for beginners, aspiring AI professionals, students, developers, and technology enthusiasts looking to build connected and scalable Agentic AI systems using Model Context Protocol.

Syllabus

Foundations of MCP

This module introduces Model Context Protocol (MCP) and its role in enabling AI applications to securely access external tools, data, and services. Explore the challenges of traditional AI integrations, including the NxM connectivity problem, and understand how MCP simplifies communication between AI systems and external resources. Gain a strong foundation in MCP concepts, architecture, and use cases to support scalable, interoperable, and efficient Agentic AI solutions.

MCP and Its Architecture

This module explores Model Context Protocol (MCP) and the architecture that enables seamless communication between AI applications, tools, and external data sources. Learn how MCP solves the NxM integration challenge, improves interoperability, and simplifies AI connectivity. Explore key MCP components, including hosts and clients, their roles, and client types, while building a foundation for scalable, efficient, and connected Agentic AI systems.

Function Calling and Sessions

This module explores MCP architecture, function calling, and session management for Agentic AI systems. Learn the role of MCP servers, server types, and communication layers that power intelligent AI assistants. Understand how MCP improves function calling for seamless interaction with external tools and services. Explore connection lifecycles and session management concepts to build scalable, reliable, and context aware AI applications.

Communication Protocols and Risk

This module explores communication protocols and risk management in MCP ecosystems. Learn how Google A2A and MCP enable communication between AI agents, applications, and external systems. Compare their architectures, explore MCP implementation using Python SDKs, and understand interoperability concepts. Analyze security, reliability, and operational risks to build secure, scalable, and resilient Agentic AI solutions.