Building a Multimodal Agent with the ADK, Azure ACI, and Gemini Flash Live 3.1

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Lint and Test the Main Python Code

The final step is to build, lint, and test the main Python code.

To Lint:

xbill@penguin:~/gemini-cli-azure/gemini31-aci$make lint
ruff check .
All checks passed!
ruff format --check .
10 files already formatted
cd frontend && npm run lint
>frontend@0.0.0 lint
>eslint .
xbill@penguin:~/gemini-cli-azure/gemini31-aca$

To Test:

xbill@penguin:~/gemini-cli-azure/gemini31-aci$make test
python -m pytest
============================================================ test session starts ============================================================
platform linux -- Python 3.13.12, pytest-9.0.2, pluggy-1.6.0
rootdir: /home/xbill
configfile: pyproject.toml
plugins: anyio-4.11.0
collected 9 items / 1 skipped 
backend/app/biometric_agent/test_agent.py ..... [55%]
test_ws_backend.py .. [77%]
test_ws_backend_v2.py ..

Running Locally

The main Python Code can then be run locally:

xbill@penguin:~/gemini-cli-azure/gemini31-aci$make run
. ./biosync.sh
Local URL
http://127.0.0.1:8080/
2026年04月06日 16:09:42,868 - INFO - System Config: 2.0 FPS, 10.0s Heartbeat
Serving static files from: /home/xbill/way-back-home/level_3_gemini/frontend/dist
INFO: Started server process [25860]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)

Then connect to the local front end:

Deploying to Google Azure ACA

A utility script runs the deployment to Azure ACA. Use the deploy version from the local system:

xbill@penguin:~/gemini-cli-azure/gemini31-aci$make deploy
./deploy.sh
 0.0s 0.0s

You can validate the final result by checking the messages:

Azure Deployment complete.
URL: https://biometric-scout-app.wonderfuldune-ec8eec50.eastus.azurecontainerapps.io
xbill@penguin:~/gemini-cli-azure/gemini31-aci$

Once the container is deployed- you can then get the endpoint:

xbill@penguin:~/gemini-cli-azure/gemini31-aca$make status
Name State URL
------------------- --------- -----------------------------------------------------------------------
biometric-scout-app Succeeded biometric-scout-app.wonderfuldune-ec8eec50.eastus.azurecontainerapps.io
xbill@penguin:~/gemini-cli-azure/gemini31-aca$make endpoint
biometric-scout-app.wonderfuldune-ec8eec50.eastus.azurecontainerapps.io
xbill@penguin:~/gemini-cli-azure/gemini31-aca$

The service will be visible in the Azure console:

biometric-scout-ssl-pengu.eastus.azurecontainer.io

Running the Web Interface

Start a connection to the Azure deployed app:

biometric-scout-ssl-pengu.eastus.azurecontainer.io

Then connect to the app :

Then use the Live model to process audio and video:

Finally — complete the sequence:

Project Code Review

Gemini CLI was used for a final project review:

✦ The code is in great shape. All 8 tests passed, and the entire project is compliant with the linter rules.
 There is one warning related to an experimental feature (PLUGGABLE_AUTH) in the Google ADK, but this is informational and doesn't indicate an
 error.
 Since the automated checks are clean, what specific part of the codebase would you like me to review? For example, we could look at:
 * The agent's logic in backend/app/biometric_agent/agent.py
 * The frontend WebSocket and component logic in frontend/src/BiometricLock.jsx
 * The Azure deployment scripts
 * The overall architecture

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 ACI. Several key take-aways and lessons learned were summarized from working with the transition to a new Live Gemini LLM model. Finally, Gemini CLI was used for a complete project code review.