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Keep shopping6 Full Stack Real-World AI Agents-GCP, Gemini, ADK, MCP, A2A
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6 Full Stack Real-World AI Agents-GCP, Gemini, ADK, MCP, A2A
Build 6 Production-Ready, Full-Stack AI agents with Google ADK, Gen AI, MCP, A2A, RAG, Chainlit, Streamlit, Copilotkit
Last updated 7/2026
English
What you'll learn
- Build Production Ready, Professional Looking, Real World AI agents from scratch - from Develop to Deploy - All in One
- From Very Basic - Master the core concepts of modern AI agent development, including Google ADK, Gemini, MCP, A2A, RAG, tool calling and more
- Agent 1 - Build your first conversational AI agent using Gemini, Google Gen AI SDK, and Chainlit
- Agent 2 - Develop event-driven AI agents integrated using Google Gen AI SDK, Gemini with Google Cloud Storage, Cloud Functions, and other GCP services.
- Agent 3 - Build your First AI Agent using Google ADK powered by Gemini with Chainlit UI at the Front. Understand Artifacts and Reasoning
- Agent 4 - Develop Multi-Agent AI System with MCP integration. Agent to Database all in Natural Language, with a professional looking UI using Streamlit
- Agent 5 - Create Multi-Agent application with Agent-to-Agent (A2A) Integration. RAG based knowledge retrieval with GCP Datastore
- Agent 6 - Build modern AI applications with CopilotKit using the AG-UI Protocol for professional user experiences
- Deploy AI agents locally and to Google Cloud Run using production-ready deployment practices. Understand and use Docker based deployment practice
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Course content
10 sections • 86 lectures • 8h 35m total length
Requirements
- Very basic on GCP
- Python Basic
- Huge Amount on Interest in AI Agents
Description
Learn how to Develop & Deploy you Real World AI agents.
Master Google Agent Development Kit (ADK) and the Google Gen AI SDK by building and deploying production-ready AI agents from scratch.
This hands-on course is designed for developers who want practical experience with Google's latest agent framework. Instead of toy examples, you'll build 6 real-world AI agents that demonstrate modern agentic AI architectures and enterprise integration patterns.
Also the course teaches how to use polished UI framework like Chainlit, Streamlit, Copilotkit with AI Agents
What You'll Learn
Build AI agents using Google ADK
Develop applications with the Google Gen AI SDK and Gemini models
Create multi-agent systems and agent orchestration workflows
Implement Model Context Protocol (MCP) for tool and resource integration
Build Agent-to-Agent (A2A) communication workflows
Implement Retrieval-Augmented Generation (RAG) using enterprise knowledge sources
Integrate AI agents with SQL databases, APIs, and external services
Develop conversational agents with memory, state management, and streaming responses
Build user interfaces using Chainlit, Streamlit, and CopilotKit
Test and debug agents locally
Deploy production-ready applications to Google Cloud Run
Real-World Projects
Throughout the course, you'll build six end-to-end AI agent applications covering:
Multi-agent collaboration
Tool calling and function execution
MCP server integration
RAG-based knowledge assistants
Database-backed AI applications
Human-like conversational agents
Production deployment on Google Cloud
Prerequisites
Basic Python programming
Familiarity with REST APIs
Basic understanding of Generative AI concepts (helpful but not required)
Whether you're an AI Engineer, Python Developer, Cloud Engineer, or Software Architect, this course will provide the practical skills needed to design, build, integrate, and deploy production-grade AI agents using Google's latest AI technologies.
Who this course is for:
- Anyone and Everyone - If you’re fascinated with AI Agents and eager to create powerful Agentic AI applications – This is for you
- Google Cloud professionals interested in deploying AI agents on Cloud Run and integrating with GCP services
- Software engineers who want to learn modern Agentic AI concepts such as Multi-Agent Systems, MCP, A2A, and RAG.
- Backend and Full-Stack developers building AI-powered applications with databases, APIs, and external tools.
- Developers interested in integrating AI agents with modern UI frameworks such as Chainlit, Streamlit, and CopilotKit.
- Anyone who has experimented with LLMs or chatbots and wants to build real-world, production-grade AI agent applications.