Building a Multimodal Cross Cloud Live Agent with ADK, Azure AKS, and Gemini CLI

--- title: Building a Multimodal Cross Cloud Live Agent with ADK, Azure AKS, and Gemini CLI published: true series: Azure date: 2026-04-07 13:16:14 UTC tags: googleadk,azure,ak,geminicli canonical_url: https://xbill999.medium.com/building-a-multimodal-cross-cloud-live-agent-with-adk-azure-aks-and-gemini-cli-b7e8457fd853 --- 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.  #### Aren’t There a Billion Python MCP 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: [Welcome to Python.org](https://www.python.org/) #### 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](https://github.com/pyenv/pyenv) As of writing — the mainstream python version is 3.13. To validate your current Python: ```console 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](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: ```shell 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: ```console 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: [GitHub - nvm-sh/nvm: Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions](https://github.com/nvm-sh/nvm) #### 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: [Install](https://howtowhale.github.io/dvm/install.html) #### 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](https://learn.microsoft.com/en-us/cli/azure/?view=azure-cli-latest) #### Agent Development Kit The [Google Agent Development Kit](https://www.google.com/search?q=Google+Agent+Development+Kit&rlz=1CAIWTJ_enUS1114&oq=what+is+the+adk+google&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIICAEQABgWGB4yCAgCEAAYFhgeMggIAxAAGBYYHjIICAQQABgWGB4yCAgFEAAYFhgeMggIBhAAGBYYHjIKCAcQABgKGBYYHjINCAgQABiGAxiABBiKBTIKCAkQABiABBiiBNIBCDMxODlqMGo3qAIAsAIA&sourceid=chrome&ie=UTF-8&mstk=AUtExfB5Oo7ZHHcDEHu7aqZiPBA2l1c-QGh5dB7xkkDPIiYcn8O1Imt2IHNR7bzA6JnyDCSDCUGpGWTeBW14namlN_QqzJLLI5-px1BE9jfSxwli6njPDPERjm5pRqNP3uC6HhUKiRcTJ1T8x5LHQrCkVxylw7QWg0N8B4dQDIcWpnVX9Gc&csui=3&ved=2ahUKEwjYu-G8p-uSAxXrv4kEHUbpLo0QgK4QegQIARAB) (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: [Agent Development Kit (ADK)](https://google.github.io/adk-docs/) #### 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](https://xbill999.medium.com/mcp-development-with-python-and-the-azure-app-service-683e68e1f7f0) #### 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](https://codelabs.developers.google.com/way-back-home-level-3/instructions#3) 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 Google Cloud Run. #### 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 ‘level\_3-lightsail’ setup. The next step is to clone the GitHub repository to your local environment: ```shell cd ~ git clone https://github.com/xbill9/gemini-cli-azure cd level_3-aks ``` Then run **init.sh** from the cloned directory. The script will attempt to determine your shell environment and set the correct variables: ```shell 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: ```shell 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: ```console xbill@penguin:~/gemini-cli-azure/level_3-aks/backend/app$ adk run biometric_agent Log setup complete: /tmp/agents_log/agent.20260403_091821.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. [user]: ``` #### Deploying to Azure Kubernates Service The first step is to refresh the Azure credentials in the current build environment: ```shell xbill@penguin:~/gemini-cli-azure/level_3-appservice$ az login ``` Run the deploy version on the local system: ```shell xbill@penguin:~/gemini-cli-azure/level_3-aks$ make deploy ./deploy-aks.sh Ensuring Resource Group exists... 0.0s 0.0s ``` You can validate the final result by checking the messages: ```console xbill@penguin:~/gemini-cli-azure/level_3-aks$ make status Checking AKS status for biometric-scout-aks... Name State K8sVersion ------------------- --------- ------------ biometric-scout-aks Succeeded 1.33 Kubernetes Services: NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE biometric-scout-service ClusterIP 10.0.252.23 <none> 80/TCP 68m Kubernetes Pods: NAME READY STATUS RESTARTS AGE biometric-scout-app-5bb549f585-dpwrx 1/1 Running 0 68m xbill@penguin:~/gemini-cli-azure/level_3-aks$ ``` The service will be visible in the Azure console:  #### Running the Web Interface Start a connection to the deployed app: ```plaintext https://biometric-scout-app.orangebeach-e5cba679.canadaeast.azurecontainerapps.io ``` Then connect tothe app :  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 services. This approach validates that cross cloud tools can be used — even with more complex agents.