Leveraging the Google Agent Development Kit (ADK) and the underlying Gemini LLM to build cross-cloud apps with the Python programming language deployed to the Azure app service.
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Aren’t There a Billion Python Agent Demos?
Yes there are.
Python has traditionally been the main coding language for ML and AI tools. The goal of this article is to provide a minimal viable basic working MCP stdio server that can be run locally without any unneeded extra code or extensions.
What Is Python?
Python is an interpreted language that allows for rapid development and testing and has deep libraries for working with ML and AI:
Python Version Management
One of the downsides of the wide deployment of Python has been managing the language versions across platforms and maintaining a supported version.
The pyenv tool enables deploying consistent versions of Python:
GitHub - pyenv/pyenv: Simple Python version management
As of writing — the mainstream python version is 3.13. To validate your current Python:
admin@ip-172-31-70-211:~/gemini-cli-azure$ python --version
Python 3.13.12
Azure Kubernates Service
Azure Kubernetes Service (AKS) is a fully managed, serverless Kubernetes service on Microsoft Azure that simplifies deploying, scaling, and managing containerized applications. It handles critical tasks like health monitoring, maintenance, and automated upgrades, reducing operational complexity. AKS is used for microservices, DevOps, and cloud-native app development.
More details are available here:
https://azure.microsoft.com/en-us/products/kubernetes-service
Isn’t that Overkill? A whole Cluster Just for some Agents?!
An entire cluster is a large deployment for just a basic ADK server. The goal was to validate that ADK servers can be deployed — and that opens the door for more complex deployments that can take advantage of the full services in the cluster.
Why would I want Gemini CLI with Azure? Isn’t that a Google Thing?
Yes- Gemini CLI leverages the Google Cloud console and Gemini models but it is also open source and platform agnostic. Many applications are already cross-cloud so this enables familiar tools to be run natively on Microsoft Azure.
Gemini CLI
If not pre-installed you can download the Gemini CLI to interact with the source files and provide real-time assistance:
npm install -g @google/gemini-cli
Testing the Gemini CLI Environment
Once you have all the tools and the correct Node.js version in place- you can test the startup of Gemini CLI. You will need to authenticate with a Key or your Google Account:
gemini
admin@ip-172-31-70-211:~/gemini-cli-azure$ gemini
▝▜▄ Gemini CLI v0.33.1
▝▜▄
▗▟▀ Logged in with Google /auth
▝▀ Gemini Code Assist Standard /upgrade
? for shortcuts
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
shift+tab to accept edits 3 GEMINI.md files | 1 MCP server
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
> Type your message or @path/to/file
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
~/.../gemini-cli-azure (main*) no sandbox (see /docs) /model Auto (Gemini 3) | 239.8 MB
Node Version Management
Gemini CLI needs a consistent, up to date version of Node. The nvm command can be used to get a standard Node environment:
Docker Version Management
The Azure CLI tools need current version of Docker. If your environment does not provide a recent docker tool- the Docker Version Manager can be used to downlaod the latest supported Docker:
Azure CLI
The Azure CLI provides a command line tool to directly access Azure services from your current environment. Full details on the CLI are available here:
Azure Command-Line Interface (CLI) documentation
Agent Development Kit
The Google Agent Development Kit (ADK) is an open-source, Python-based framework designed to streamline the creation, deployment, and orchestration of sophisticated, multi-agent AI systems. It treats agent development like software engineering, offering modularity, state management, and built-in tools (like Google Search) to build autonomous agents.
The ADK can be installed from here:
This seems like a lot of Configuration!
Getting the key tools in place is the first step to working across Cloud environments. For a deeper dive- a project with a similar setup can be found here:
MCP Development with Python, and the Azure App Service
Where do I start?
The strategy for starting multimodal real time cross cloud agent development is a incremental step by step approach.
The agents in the demo are based on the original code lab:
Way Back Home - Building an ADK Bi-Directional Streaming Agent | Google Codelabs
First, the basic development environment is setup with the required system variables, and a working Gemini CLI configuration.
Then, a minimal ADK Agent is built with the visual builder. Next — the entire solution is deployed to Azure AKS.
Setup the Basic Environment
At this point you should have a working Python environment and a working Gemini CLI installation. All of the relevant code examples and documentation is available in GitHub. This repo has a wide variety of samples- but this lab will focus on the ‘gemini31-aks’ setup.
The next step is to clone the GitHub repository to your local environment:
cd ~
git clone https://github.com/xbill9/gemini-cli-azure
cd gemini31-aks
Then run init.sh from the cloned directory.
The script will attempt to determine your shell environment and set the correct variables:
source init.sh
If your session times out or you need to re-authenticate- you can run the set_env.sh script to reset your environment variables:
source set_env.sh
Variables like PROJECT_ID need to be setup for use in the various build scripts- so the set_env script can be used to reset the environment if you time-out.
Verify The ADK Installation
To verify the setup, run the ADK CLI locally with Agent1:
xbill@penguin:~/gemini-cli-azure/gemini31-aks/backend/app$ adk run biometric_agent/
/home/xbill/.local/lib/python3.13/site-packages/google/adk/features/_feature_decorator.py:72: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.
check_feature_enabled()
Log setup complete: /tmp/agents_log/agent.20260412_171150.log
To access latest log: tail -F /tmp/agents_log/agent.latest.log
/home/xbill/.local/lib/python3.13/site-packages/google/adk/cli/cli.py:204: UserWarning: [EXPERIMENTAL] InMemoryCredentialService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.
credential_service = InMemoryCredentialService()
/home/xbill/.local/lib/python3.13/site-packages/google/adk/auth/credential_service/in_memory_credential_service.py:33: UserWarning: [EXPERIMENTAL] BaseCredentialService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.
super(). __init__ ()
Running agent biometric_agent, type exit to exit.
[biometric_agent]: Scanner Online.
Deploying to Azure Kubernates Service
The first step is to refresh the Azure credentials in the current build environment:
xbill@penguin:~/gemini-cli-azure/gemini31-aks$ az login
Run the deploy version on the local system:
xbill@penguin:~/gemini-cli-azure/gemini31-aks$ make deploy
./deploy.sh
Ensuring Resource Group exists...
{
"id": "/subscriptions/3db3ce66-50b6-4d11-91ef-5950cf4039ed/resourceGroups/aca",
"location": "canadaeast",
"managedBy": null,
"name": "aca",
"properties": {
"provisioningState": "Succeeded"
},
"tags": null,
"type": "Microsoft.Resources/resourceGroups"
}
Ensuring ACR exists...
Creating ACR biometricacrpenguinv3... 0.0s 0.0s
You can validate the final result by checking the messages:
✦ The AKS deployment is healthy and HTTPS-enabled.
Status Summary:
- App Pods: Running (1/1 ready)
- Ingress: biometric-scout-ingress is active for biometric-scout-penguinv3.eastus.cloudapp.azure.com.
- Ingress Controller: Running with External IP 20.81.113.127.
- Endpoint: https://biometric-scout-penguinv3.eastus.cloudapp.azure.com (https://biometric-scout-penguinv3.eastus.cloudapp.azure.com)
Note: Since we are using a self-signed certificate, you will need to bypass the "Your connection is not private" warning in your browser to access
the site.
make endpoint
✦ The endpoint command has been updated to reflect the new HTTPS URL:
https://biometric-scout-penguinv3.eastus.cloudapp.azure.com (https://biometric-scout-penguinv3.eastus.cloudapp.azure.com)
The service will be visible in the Azure console:
Running the Web Interface
Start a connection to the deployed app:
https://biometric-scout-penguinv3.eastus.cloudapp.azure.com
Then use the Live model to process audio and video:
Finally — complete the sequence:
Summary
The Agent Development Kit was used to enable a multi-modal agent using the Gemini Live Model. This Agent was tested locally with the CLI and then deployed to Azure Kubernates Service (AKS). This approach validates that cross cloud tools can be used — even with more complex agents.
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