AI Agents Engineering with Google ADK, RAG & MCP

Course Overview

This intermediate-level course teaches practical AI agent development using Python and Google ADK. It covers building AI agents with tools, memory, and real-world agent patterns, including sequential, hierarchical, routing, multi-agent, and autonomous agents. The course also explores Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), LangChain, prompt and context engineering, debugging, guardrails, deployment, and multi-agent systems through hands-on projects and a capstone application.

Key Takeaways

• 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 using embeddings, vector databases, retrieval, and custom documents.
• Use MCP to connect AI agents with external tools and understand 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 reliable AI agents.

Prerequisites

• 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 for hands-on coding exercises.
• A free Google account is required to create a Gemini API key.
• Readiness to code along and build projects step by step.

Target Learners

• Software engineering students interested in practical AI agent development with Python.
• Python developers aiming to build applications using Google ADK, RAG, MCP, and multi-agent systems.
• Software developers transitioning into AI engineering seeking hands-on agent development experience.
• Beginners with basic Python knowledge who want to build and deploy real AI agent projects.