Multi-Agent A2A with the Agent Development Kit(ADK), Azure ACI, and Gemini CLI

Leveraging the Google Agent Development Kit (ADK) and the underlying Gemini LLM to build Multi-Agent Applications with A2A protocol support using the Python programming language.

Aren’t There a Billion Python ADK 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 multi-agent test bed for building, debugging, and deploying multi-agent applications.

What you talkin ‘bout Willis?

So what is different about this lab compared to all the others out there?

This is one of the first deep dives into a Multi-Agent application leveraging the advanced tooling of Gemini CLI. The starting point for the demo was an existing Codelab- which was updated and re-engineered with Gemini CLI.

The original Codelab- is here:

Building a Multi-Agent System | Google Codelabs

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

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:

python --version
Python 3.13.13

Azure Container Instances

Azure Container Instances (ACI) is a serverless, managed service that allows you to run Docker containers in the cloud without managing virtual machines. It is ideal for rapid deployment, bursting, and simple, isolated applications, offering per-second billing and quick startup times. ACI supports Linux and Windows containers, with options for volume mounting and GPU resources.

More details are available here:

https://azure.microsoft.com/en-us/products/container-instances

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.

Azure Container Instance Configuration

To configure your Azure Service with the base system tools- this article provides a reference:

MCP Development with Python, and the Azure Container Instance

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 CLI v0.33.1
    ▝▜▄
   ▗▟▀ Logged in with Google /auth
  ▝▀ Gemini Code Assist Standard /upgrade 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

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:

Agent Development Kit (ADK)

Agent Skills

Gemini CLI can be customized to work with ADK agents. Both an Agent Development MCP server, and specific Agent skills are available.

More details are here:

Agent Development Kit (ADK)

To get the Agent Skills in Gemini CLI:

> /skills list
Available Agent Skills:

- adk-cheatsheet
      MUST READ before writing or modifying ADK agent code. ADK API quick reference for Python — agent types, tool definitions, orchestration
      patterns, callbacks, and state management. Includes an index of all ADK documentation pages. Do NOT use for creating new projects (use
      adk-scaffold).
  - adk-deploy-guide
      MUST READ before deploying any ADK agent. ADK deployment guide — Agent Engine, Cloud Run, GKE, CI/CD pipelines, secrets, observability, and
      production workflows. Use when deploying agents to Google Cloud or troubleshooting deployments. Do NOT use for API code patterns (use
      adk-cheatsheet), evaluation (use adk-eval-guide), or project scaffolding (use adk-scaffold).
  - adk-dev-guide
      ALWAYS ACTIVE — read at the start of any ADK agent development session. ADK development lifecycle and mandatory coding guidelines —
      spec-driven workflow, code preservation rules, model selection, and troubleshooting.
  - adk-eval-guide
      MUST READ before running any ADK evaluation. ADK evaluation methodology — eval metrics, evalset schema, LLM-as-judge, tool trajectory
      scoring, and common failure causes. Use when evaluating agent quality, running adk eval, or debugging eval results. Do NOT use for API code
      patterns (use adk-cheatsheet), deployment (use adk-deploy-guide), or project scaffolding (use adk-scaffold).
  - adk-observability-guide
      MUST READ before setting up observability for ADK agents or when analyzing production traffic, debugging agent behavior, or improving agent
      performance. ADK observability guide — Cloud Trace, prompt-response logging, BigQuery Agent Analytics, third-party integrations, and
      troubleshooting. Use when configuring monitoring, tracing, or logging for agents, or when understanding how a deployed agent handles real
      traffic.
  - adk-scaffold
      MUST READ before creating or enhancing any ADK agent project. Use when the user wants to build a new agent (e.g. "build me a search agent")
      or enhance an existing project (e.g. "add CI/CD to my project", "add RAG").

and the ADK documentation:

> /mcp list
Configured MCP servers:

🟢 adk-docs-mcp (from adk-docs-ext) - Ready (2 tools)
  Tools:
  - mcp_adk-docs-mcp_fetch_docs
  - mcp_adk-docs-mcp_list_doc_sources

Where do I start?

The strategy for starting multi agent development is a incremental step by step approach.

First, the basic development environment is setup with the required system variables, and a working Gemini CLI configuration.

Then, ADK Multi-Agent is built, debugged, and tested locally. Finally — the entire solution is deployed to Azure ACI.

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.

The next step is to clone the GitHub repository to your local environment:

cd ~
git clone https://github.com/xbill9/gemini-cli-azure
cd mulit-aci

Then run init2.sh from the cloned directory.

The script will attempt to determine your shell environment and set the correct variables:

source init2.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.

Finally install the packages and dependencies:

make install

Verify The ADK Installation

To verify the setup, run the ADK CLI locally with the researcher agent:

xbill@penguin:~/gemini-cli-azure/multi-aci/agents$ adk run researcher
/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.20260418_155613.log
To access latest log: tail -F /tmp/agents_log/agent.latest.log
{"asctime": "2026-04-18 15:56:13,235", "name": "root", "levelname": "INFO", "message": "Logging initialized for researcher", "filename": "logging_config.py", "lineno": 54, "service": "researcher", "log_level": "INFO"}
{"asctime": "2026-04-18 15:56:13,236", "name": "researcher.agent", "levelname": "INFO", "message": "Initialized researcher agent with model: gemini-2.5-flash", "filename": "agent.py", "lineno": 85}

Test The ADK Web Interface

This tests the ADK agent interactions with a browser:

xbill@penguin:~/gemini-cli-azure/multi-aci/agents$ adk web --host 0.0.0.0
/home/xbill/.pyenv/versions/3.13.13/lib/python3.13/site-packages/google/adk/features/_feature_decorator.py:72: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.
  check_feature_enabled()
2026-04-10 17:49:11,850 - INFO - service_factory.py:266 - Using in-memory memory service
2026-04-10 17:49:11,850 - INFO - local_storage.py:84 - Using per-agent session storage rooted at /home/xbill/multi-agent/agents
2026-04-10 17:49:11,850 - INFO - local_storage.py:110 - Using file artifact service at /home/xbill/multi-agent/agents/.adk/artifacts
/home/xbill/.pyenv/versions/3.13.13/lib/python3.13/site-packages/google/adk/cli/fast_api.py:198: 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/.pyenv/versions/3.13.13/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__ ()
INFO: Started server process [16063]
INFO: Waiting for application startup.

+-----------------------------------------------------------------------------+
| ADK Web Server started |
| |
| For local testing, access at http://0.0.0.0:8000. |
+-----------------------------------------------------------------------------+

Then use the web interface — either on the local interface 127.0.0.1 or the catch-all web interface 0.0.0.0 -depending on your environment:

Special note for Google Cloud Shell Deployments- add a CORS allow_origins configuration exemption to allow the ADK agent to run:

adk web --host 0.0.0.0 --allow_origins 'regex:.*'

Multi Agent Design

The multi-agent deployment consists of 5 agents:

Researcher
Judge
Orchestrator
Content Builder
Course Builder

An overview of the multi-agent system can be found here:

Multi-Agent A2A with the Agent Development Kit(ADK), Cloud Run, Agent Skills, and Gemini CLI

Running/Testing/Debugging Locally

The main Makefile has been extended with extensive targets for managing the agents on the local development environment.

The key targets include:

xbill@penguin:~/gemini-cli-azure/multi-aci$ make help
Available commands:
  --- Local Development ---
  install - Install all dependencies for root, agents, and app
  start - Start all services locally (alias for start-local)
  stop - Stop all local services (alias for stop-local)
  run - Start all services locally
  status - Show status of local services
  local - Show local service URLs
  test - Run all tests (pytest)
  lint - Run linting checks (ruff)
  --- Azure ACI ---
  deploy-aci - Deploy all services to Azure Container Instances (ACI)
  destroy-aci - Delete ACI resources
  status-aci - Show ACI status (alias: az-status)
  endpoint-aci - Show ACI service endpoint (alias: endpoint)
  test-e2e-aci - Run ACI E2E test
  --- Shared Azure ---
  az-login - Login to Azure CLI
  acr-login - Login to ACR
  acr-create - Create/Verify ACR and Resource Group
  clean - Remove caches and logs

First check for local running agents:

> make status
✦ I will run the make status command to check the status of your local services.


╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Shell Running 'make status' to check local services. │
│ │
│ Checking status of locally running agents and servers... │
│ --- Network Status --- │
│ No services listening on expected ports (8000-8004, 5173). │
│ --- Process Status --- │
│ No matching processes found. │
│ │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

✦ No local services are currently running.

Then all the agents can be started together:

make start
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Shell Starting all local services in the background. │
│ │
│ Stopping any existing agent and server processes... │
│ Starting all agents in background... │
│ Waiting for sub-agents to start... │
│ All agents started. Logs: researcher.log, judge.log, content_builder.log, orchestrator.log │
│ Starting App Backend in background... │
│ Starting Frontend dev server in background... │
│ All services started. Logs: researcher.log, judge.log, content_builder.log, orchestrator.log, backend.log, frontend.log │
│ Frontend: http://localhost:5173 │
│ Backend: http://localhost:8000 │
│

make status
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Shell Checking status of local services. │
│ │
│ ... first 22 lines hidden (Ctrl+O to show) ... │
│ dge/.well-known/agent-card.json; \ export CONTENT_BUILDER_AGENT_CARD_URL=http://localhost:8003/a2a/content_builder/.well-known/agent-card.jso │
│ n; \ /home/xbill/.pyenv/shims/python3 -m shared.adk_app --host 0.0.0.0 --port 8004 agents/orchestrator" │
│ xbill 14269 9.3 3.1 248416 148664 ? S 20:27 0:02 /home/xbill/.pyenv/versions/3.13.13/bin/python3 -m shared.adk_app --host 0.0 │
│ .0.0 --port 8004 agents/orchestrator │
│ xbill 14273 0.0 0.0 2584 1668 ? S 20:27 0:00 /bin/sh -c /bin/bash -c "source .env 2>/dev/null || true; \ export AGENT_SER │
│ VER_URL=http://localhost:8004; \ export AGENT_NAME=orchestrator; \ export PORT=8000; \ cd app && /home/xbill/.pyenv/shims/python3 main.py" │
│ xbill 14275 0.0 0.0 6940 3312 ? S 20:27 0:00 /bin/bash -c source .env 2>/dev/null || true; export AGENT_SERVER_URL=http:/ │
│ /localhost:8004; export AGENT_NAME=orchestrator; export PORT=8000; cd app && /home/xbill/.pyenv/shims/python3 main.py │
│ xbill 14278 8.4 3.1 1345296 144520 ? Sl 20:27 0:02 /home/xbill/.pyenv/versions/3.13.13/bin/python3 main.py │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

✦ The local agents and the backend server are running and listening on ports 8000-8004. The Vite dev server (port 5173) is still not appearing in the
  network status.

The entire project can be linted and tested as unit:

> make lint
✦ I will run the make lint command to confirm that all linting checks pass.

╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Shell Run linting checks to confirm they pass. │
│ │
│ ruff check . │
│ All checks passed! │
│ │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

✦ make lint completed successfully with no errors.

 > make test
✦ I will run the project's test suite using make test.

╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Shell Run all tests using pytest. │
│ │
│ ... first 41 lines hidden (Ctrl+O to show) ... │
│ agents/orchestrator/agent.py:294 │
│ /home/xbill/gemini-cli-azure/multi-aci/agents/orchestrator/agent.py:294: UserWarning: [EXPERIMENTAL] RemoteA2aAgent: ADK Implementation for │
│ A2A support (A2aAgentExecutor, RemoteA2aAgent and corresponding supporting components etc.) is in experimental mode and is subject to breaki │
│ ng changes. A2A protocol and SDK are themselves not experimental. Once it's stable enough the experimental mode will be removed. Your feedbac │
│ k is welcome. │
│ content_builder = RemoteA2aAgent( │
│ │
│ -- Docs: [https://docs.pytest.org/en/stable/how-to/capture-warnings.html](https://docs.pytest.org/en/stable/how-to/capture-warnings.html) │
│ ====================================================== 30 passed, 4 warnings in 7.71s ======================================================= │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

✦ make test passed all 30 tests successfully.

And end to end tested:

✓ Shell Running the end-to-end tests for the project. │
│ │
│ Running end-to-end test against http://localhost:8000... │
│ {"type": "progress", "text": "\ud83d\ude80 Connected to backend, starting research..."} │
│ {"type": "progress", "text": "\ud83d\ude80 Starting the course creation pipeline..."} │
│ {"type": "progress", "text": "\ud83d\udd0d Research is starting..."} │
│ {"type": "progress", "text": "\ud83d\udd0d Researcher is gathering information..."} │
│ {"type": "progress", "text": "\u2696\ufe0f Judge is evaluating findings..."} │
│ {"type": "progress", "text": "\u2696\ufe0f Judge is evaluating findings..."} │
│ {"type": "progress", "text": "\u270d\ufe0f Building the final course content..."} │
│ {"type": "progress", "text": "\u270d\ufe0f Content Builder is writing the course..."} │

Then connect to the local front end:

And the entire agent system will run in the local environment:

Local Logging / Debugging

Gemini CLI has full access to the local agent logs for debugging and troubleshooting:

✦ I've analyzed the logs from your e2e run. All agents (researcher, judge, content_builder, orchestrator) and both frontend and backend services
  started successfully. The course creation pipeline ran as expected: the orchestrator initiated the "history of the internet" course, the researcher
  gathered information, the judge approved it, and the content builder generated the course content.

Deploying to Azure ACI

The project level Makefile has targets for managing the Agent deployment to serverless endpoints:

xbill@penguin:~/gemini-cli-azure/multi-aci$ az login
A web browser has been opened at https://login.microsoftonline.com/organizations/oauth2/v2.0/authorize. Please continue the login in the web browser. If no web browser is available or if the web browser fails to open, use device code flow with `az login --use-device-code`.

A utility script check the deployment to Azure ACI:

xbill@penguin:~/gemini-cli-azure/multi-aci$ make status-aci
./aci/status-aci.sh
Checking Azure Container Instances status in adk-rg-aci...
(ResourceGroupNotFound) Resource group 'adk-rg-aci' could not be found.
Code: ResourceGroupNotFound
Message: Resource group 'adk-rg-aci' could not be found.
make: *** [Makefile:202: status-aci] Error 3

You can then deploy the services:


xbill@penguin:~/gemini-cli-azure/multi-aci$ make deploy
./aci/deploy-aci.sh
=== Azure ACI Deployment ===
Azure Location: westus2
Resource Group: adk-rg-aci
ACR Name: adkacrpenguin
=============================
Ensuring Resource Group exists...
Location Name
---------- ----------
westus2 adk-rg-aci
Ensuring ACR exists...

✦ I have successfully rebuilt the ACI deployment by consolidating the microservices into a single Azure Container
  Group and enabling Log Analytics integration.

Once the containers are deployed- you can then get the endpoint:

And check the endpoint:
│ ✓ Shell make aci-status │
│ │
│ ./aci/status-aci.sh │
│ Checking Azure Container Instances status in adk-rg-aci... │
│ Name FQDN IP │
│ -------------------- ------------------------------------------------------ -------------- │
│ course-creator-group course-creator-group-penguin.westus2.azurecontainer.io 172.179.100.61 │
│                                                                                              

xbill@penguin:~/gemini-cli-azure/multi-aci$ make endpoint
./aci/endpoint-aci.sh
--- Azure ACI Endpoint ---
http://course-creator-group-penguin.westus2.azurecontainer.io:8080/
xbill@penguin:~/gemini-cli-azure/multi-aci$

The service will be visible in the Azure console:

Test End to End in ACI

The entire agent system is tested on the remote Azure endpoint:

✦ The make endpoint-aci command has successfully retrieved and displayed the public URL for the Azure Container App.
 > make e2e-test-aci

✦ The end-to-end test against the newly deployed Azure Container Group was successful. The system correctly
  orchestrated the research, evaluation, and content generation phases, producing a structured course on the
  history of the internet.

Running the Web Interface

Start a connection to the Cloud Run deployed app:

http://course-creator-group-penguin.westus2.azurecontainer.io:8080/

Then connect to the app :

Then use online course generator:

Final Gemini CLI Code Review

As a final step — Gemini CLI was used for a full code review of the project:

✦ I have successfully rebuilt the ACI deployment by consolidating the microservices into a single Azure Container
  Group and enabling Log Analytics integration.

  Summary of Actions
   - Cleanup: Destroyed the five individual container instances to transition to a grouped deployment.
   - Image Rebuild: Built and pushed new versions of all five microservice images (researcher, judge,
     content-builder, orchestrator, and course-creator) to the Azure Container Registry with the latest tag.
   - Logging Configuration: Successfully retrieved the workspace ID and primary key for the existing
     adk-log-workspace Log Analytics workspace.
   - Group Deployment: Deployed the course-creator-group container group using the generated
     aci-container-group.yaml, ensuring all containers share the same network and lifecycle while streaming logs
     to Azure Monitor.

  Deployment Details
   - Container Group Name: course-creator-group
   - Resource Group: adk-rg-aci
   - Region: westus2
   - Public URL: http://course-creator-group-penguin.westus2.azurecontainer.io:8080
     (http://course-creator-group-penguin.westus2.azurecontainer.io:8080)
   - Logging: Integrated with Log Analytics workspace adk-log-workspace.

  Key Achievements
   - Container Group Deployment: The multi-agent system is now running efficiently as a single container group in
     ACI, reducing overhead and simplifying inter-agent communication via localhost.
   - Integrated Logging: Log Analytics is active, providing centralized monitoring for all microservices.
   - Verified Functionality: The e2e-test-aci confirmed that the orchestrator can successfully communicate with
     the sub-agents and the main application remains fully functional in the Azure environment.

  The deployment is now fully optimized and verified.

Summary

The Agent Development Kit (ADK) was used to build a multi-agent system with A2A support using the Gemini Flash LLM Model. This application was tested locally with Gemini CLI and then deployed to Azure ACI. Several key take-aways and lessons learned were summarized from debugging and testing the multi-agent system- including deep log reviews. Finally, Gemini CLI was used for a complete project code review.