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Keep shoppingAgentic AI with Google ADK & MCP Servers
481 students
Agentic AI with Google ADK & MCP Servers
Mastering Production-Grade Agentic AI: Architect, Build, and Scale Autonomous Systems using Google ADK
Created byTechTrapture .
Last updated 2/2026
English
What you'll learn
- Design and Deploy Agentic Workflows: Successfully design, build, and deploy multi-step AI agents using the Google Agent Development Kit (ADK)
- Integrate with Google Cloud Ecosystem: Seamlessly connect and utilize Google Cloud services (like Vertex AI and Cloud Functions) to enhance agent capabilities a
- Master the Model Context Protocol (MCP): Implement the MCP standard to create MCP Servers that securely expose capabilities
- Achieve Real-World Agent Tool Use: Develop agents that can dynamically discover and invoke external business systems, databases, and APIs
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Course content
9 sections • 38 lectures • 6h 42m total length
Requirements
- Intermediate Python skills (including experience with relevant SDKs and data handling).
- Familiarity with the Google Cloud Platform (GCP) console is highly recommended.
Description
The Next Evolution of AI: Standardized Agentic Systems
Move beyond basic LLM calls and master the architecture required for production-grade Autonomous AI Agents. This hands-on course, led by a Google Cloud and AI expert, provides the definitive technical blueprint for engineering robust, multi-step agentic systems capable of driving enterprise automation.
Core Focus: The ADK and the MCP Standard
This curriculum is built around the two most critical components for next-generation AI infrastructure:
Google Agent Development Kit (ADK): Learn to use Google's cutting-edge framework for engineering reliable, scalable, and complex AI agents. You will master agentic design patterns, memory management, and task decomposition for large-scale deployments on Google Cloud.
Model Context Protocol (MCP): The MCP is the vital open standard that solves the "N × M" integration problem. You will learn to deploy MCP Servers that act as standardized, secure gateways, allowing your agents to dynamically discover and interact with all your external systems—from PostgreSQL and MongoDB to GitHub and private APIs—in a uniform, secure manner.
What You Will Master:
Agent Architecture & Deployment: Design and implement agent workflows that are resilient, observable, and ready for deployment using MLOps principles within the Google Cloud ecosystem.
Tool Use and RAG Standardization: Learn to structure external tools and data sources (RAG) behind the MCP standard, ensuring your agents are always grounded in real-time, accurate context, drastically reducing LLM hallucinations.
Secure Multi-System Orchestration: Implement the MCP client-server architecture to ensure agents can execute actions across multiple cloud environments or legacy systems securely, managing access and control centrally.
Practical Hands-On Implementation: Gain practical experience with the official SDKs to build both the ADK Client logic and custom MCP Server wrappers for your proprietary tools and data.
Enroll now to architect the future of autonomous systems and lead the implementation of standardized Agentic AI in your organization.
Who this course is for:
- Cloud Engineers and Architects looking to build cutting-edge, standardized agent-based solutions for multi-cloud or hybrid environments.
- Software Developers eager to build next-generation applications by embedding autonomous, decision-making agents using the Google ADK and the MCP standard.
- AI Enthusiasts and Tech Leads who want a practical, hands-on guide to implementing the newly adopted industry-standard for AI-to-tool integration.
- Chief Executive Officers (CEOs) & Vice Presidents (VPs): To understand the business case, strategic value, and required investment for Agentic AI.