Build Your First Agent with Gemini Enterprise

This course is part of Google Cloud AI Agents: From Foundations to Enterprise Scale Professional Certificate

Instructor: Google Cloud Training

Access provided by Deogiri Institute of Eng and Management

5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

No prior experience required

1 hour to complete

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

No prior experience required

1 hour to complete

Flexible schedule

Learn at your own pace

What you'll learn

ADK installation with virtual environments. The four core parameters: model, name, description, and instruction.
The root_agent naming convention for ADK tools. Configuring .env files with GOOGLE_API_KEY after adk create.
Three execution modes: adk web, adk run, and adk api_server. Programmatic execution with Runner and sessions.
YAML-based Agent Config for code-free agent definition. When to use YAML versus Python for agents.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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Build your Cloud Computing expertise

This course is part of the Google Cloud AI Agents: From Foundations to Enterprise Scale Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Google Cloud

There are 5 modules in this course

Turn your understanding of agents into practical reality by building, configuring, and running your first AI agent using Google’s Agent Development Kit (ADK).

This module transforms theory into practice by setting up your complete development environment. You’ll install Python, configure the Gemini Enterprise, obtain API credentials, and create your first agent project, laying the essential foundation needed to start building agents.

What's included

1 reading1 assignment

This module decodes the anatomy of an agent via the four core parameters that define its identity and behavior. You’ll learn to distinguish between description (what other agents see) and instruction (how this agent acts) and to write instructions that shape agent personality and capabilities.

What's included

2 readings1 assignment

This module expands your agent execution toolkit beyond the web interface, introducing three distinct deployment methods: Terminal commands for quick testing, API servers for production deployments, and programmatic execution for integration into larger applications.

What's included

1 reading1 assignment

This module introduces a code-free alternative to defining agents through YAML configuration files. You’ll learn when declarative YAML configurations provide advantages over Python code for agent definition and how to create and manage YAML-based agents.

What's included

1 reading1 assignment

In just 75 minutes, you’ve transformed from understanding agent concepts to actually building and running your own AI agents. You’ve set up a complete development environment, created agents from scratch, experimented with different execution modes, and learned both Python code and YAML configuration approaches. This is no small feat. You've taken the critical step from theory to practice.