AI Agents Engineering with Google ADK, RAG & MCP

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AI Agents Engineering with Google ADK, RAG & MCP
Published 10/2026
Created by Ramesh Karimi
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 82 Lectures ( 4h 12m ) | Size: 3.8 GB

Build AI agents with Google ADK, RAG, MCP & LangChain through hands-on projects, multi-agent systems and deployment

What you'll learn
⚡ Build AI agents with Python using Google ADK, tools, memory, and real-world agent patterns.
⚡ Apply sequential, hierarchical, routing, multi-agent, and autonomous agent patterns to practical problems.
⚡ Build RAG applications that use embeddings, vector databases, retrieval, and your own documents.
⚡ Use MCP to connect AI agents with external tools and understand the client-server architecture.
⚡ Build and deploy a complete multi-agent AI application using a spec-driven development workflow.
⚡ Apply prompt and context engineering, guardrails, debugging, and practical techniques for building reliable AI agents.

Requirements
❗ Basic Python knowledge such as variables, functions, and loops is recommended.
❗ No prior AI or Machine Learning experience is required.
❗ A Windows or Mac computer is required to follow the hands-on coding exercises.
❗ A free Google account is required to create a Gemini API key.
❗ Be ready to code along and build the projects step by step.

Description

Build practical AI agents and learn the engineering concepts behind modern agentic applications.

Build and Deploy AI Agents Using Modern Agent Engineering Techniques
In this hands-on course, you will learn how to build AI agents with Python and Google ADK, connect agents to tools and memory, work with RAG and MCP, and build multi-agent applications from the ground up.

You will start with the fundamentals of AI agents and learn how the agent loop works, how tools are used, and how different agent patterns can solve different types of problems. You will then build five important patterns: sequential, hierarchical, routing, multi-agent, and autonomous agents.

From there, you will work with Google ADK and build a practical StudyBuddy application. You will learn how to give an agent tools, add session memory, debug agent behavior, and understand how an agent SDK can simplify development.

The course also covers prompt engineering and context engineering, including few-shot prompting, structured outputs, prompt injection concepts, and techniques for providing better context to agents.

You will then learn LangChain fundamentals and move into Retrieval-Augmented Generation (RAG). You will understand embeddings, similarity search, vector databases, chunking, retrievers, and how to build a complete RAG application using your own documents.

Next, you will learn Model Context Protocol (MCP), including MCP clients and servers, the handshake process, tools, and how to connect an MCP server to an AI agent.

You will also learn how to deploy AI applications, work with secrets, add basic guardrails, and troubleshoot common problems during development and deployment.

Finally, you will buildResolveAI, the course's flagship capstone project. This multi-agent application is developed using a spec-driven workflow and deployed to Google Cloud, giving you an opportunity to bring together concepts from across the course.

The course also includes quizzes, complete source code, practical exercises, and an Interview Mastery module with AI-agent interview questions and answers.

By the end of the course, you will have practical experience building AI agents, RAG applications, MCP integrations, and multi-agent systems-and you will have a complete project that brings these concepts together.
This course is designed for learners who have basic Python knowledge and want to move into practical AI agent development. You do not need previous AI or Machine Learning experience.

Who this course is for
⭐ Software engineering students who want to learn practical AI agent development with Python.
⭐ Python developers who want to build applications using Google ADK, RAG, MCP, and multi-agent systems.
⭐ Software developers transitioning into AI engineering and looking for hands-on agent development experience.
⭐ Beginners with basic Python knowledge who want to build and deploy real AI agent projects.

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