
Explore how AI agents with Google ADK outperform traditional LMs by using a brain-like decision process, coordinating weather checks, flight and hotel searches, and bookings via RAG operations and tools.
Explore how Google ADK enables AI agents within the Google cloud ecosystem, offering multi-modal support, tool integrations, and deployment via Vertex AI, Cloud Run, or GKE.
Explore the SDK documentation and GitHub examples to learn agent development, with API references for Python, Go, and Java, and steps to clone and run your agent from your laptop.
Define the first agent with the LM agent class, name route agent, and configure model, instruction, and description, then use history agent to produce bullet-point summaries and a pipeline summary.
Test your agent locally with sdk web, the run command, and sdk api server before deployment. Use the edq web interface to interact and debug the history agent.
Test your agent with SDK run, deploying a command line interface to interact with the agent using World War Two as an example, and compare CLI to the web interface.
Set up a local desktop agent project with PyCharm, install Google SDK, and configure a history agent using a Gemini API key as backend, running via SDK web on localhost.
Learn to authenticate an agent with a Google service account for Vertex AI, assign the Vertex AI user role, and apply workload identity federation and secret manager best practices.
Discover the preview of agent designer in Google Agent Development Kit (ADK), a UI-driven tool to create a root agent with sub agents, add tools, generate code, and test interactively.
Explore agent deployment options in Google Cloud, including Vertex AI agent engine, Cloud Run, and GKE cluster, and learn when to use each for production, with memory bank support.
Deploy your ADK agent to Vertex AI Agent Engine via edk deploy using a staging bucket, and name the directory with underscores (not hyphens); monitor logs for issues.
Test a deployed agent with curl by creating a session and sending rest queries, using the agent engine console, query URLs, and stream queries, while noting IAM access in GCP.
Learn how to access an agent from a Python program using REST URLs, including authentication, session creation, and optional streaming responses, demonstrated in Colab Enterprise.
Artificial Intelligence agents are transforming how modern applications are built. In this beginner-friendly course, you will learn how to create your very first AI agent using the Google Agent Development Kit (ADK) and deploy it using Agent Engine on Google Cloud.
This course is designed for developers, cloud engineers, and AI enthusiasts who want a practical introduction to building intelligent agents. Instead of focusing only on theory, we will walk through a hands-on example where you will build an AI agent step by step and see how it can be deployed to run in the cloud.
By the end of this course, you will understand how AI agents work, how to build one using Google ADK, and how to deploy it using Google Agent Engine so it can run as a real application. This course is perfect if you want to quickly get started with modern AI agent development on Google Cloud.
If you are curious about the future of AI-driven applications and want to learn how to build and deploy your first AI agent, this course will give you the perfect starting point.
Build your AI Agents with Google Agent Development Kit (ADK).
A mini course on Google ADK with step by step process on how to develop and deploy your Agent in Google Cloud.
Environment Setup ( Google Cloud / Local Desktop - laptop)
Authenticate using Gemini Key / Vertex AI / 3rd Party ( Open AI / Anthropic)
Learn to test your agent locally with adk web, adk run and adk api_server options
Use built-in tools, custom tools, 3rd party tools
Use CrewAI and Langchain tools with ADK AI Agents