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

--- title: Multi-Agent A2A with the Agent Development Kit(ADK), Azure App Service, and Gemini CLI published: true series: Azure date: 2026-05-03 00:05:41 UTC tags: geminicli,a2aprotocol,azureappservice,googleadk canonical_url: https://xbill999.medium.com/multi-agent-a2a-with-the-agent-development-kit-adk-azure-app-service-and-gemini-cli-3e3b93eab728 --- 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 deployed to Azure App Service.  #### 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](https://codelabs.developers.google.com/codelabs/production-ready-ai-roadshow/1-building-a-multi-agent-system/building-a-multi-agent-system#0) #### 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 python --version Python 3.13.13 ``` #### Azure App Service Azure App Service is a fully managed Platform-as-a-Service (PaaS) that enables developers to build, deploy, and scale web applications, APIs, and mobile backends quickly. It supports multiple languages ([.NET](https://www.google.com/search?q=.NET&rlz=1CAIWTJ_enUS1155&oq=what+is+azure+app+service&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQABiABDIHCAIQABiABDIHCAMQABiABDIHCAQQABiABDIHCAUQABiABDIICAYQABgWGB4yCAgHEAAYFhgeMggICBAAGBYYHjIICAkQABgWGB7SAQgzNzkzajBqN6gCALACAA&sourceid=chrome&ie=UTF-8&ved=2ahUKEwiLxOi4mKWTAxUfkYkEHW3NNloQgK4QegYIAQgAEAQ), [Java](https://www.google.com/search?q=Java&rlz=1CAIWTJ_enUS1155&oq=what+is+azure+app+service&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQABiABDIHCAIQABiABDIHCAMQABiABDIHCAQQABiABDIHCAUQABiABDIICAYQABgWGB4yCAgHEAAYFhgeMggICBAAGBYYHjIICAkQABgWGB7SAQgzNzkzajBqN6gCALACAA&sourceid=chrome&ie=UTF-8&ved=2ahUKEwiLxOi4mKWTAxUfkYkEHW3NNloQgK4QegYIAQgAEAU), [Node.js](https://www.google.com/search?q=Node.js&rlz=1CAIWTJ_enUS1155&oq=what+is+azure+app+service&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQABiABDIHCAIQABiABDIHCAMQABiABDIHCAQQABiABDIHCAUQABiABDIICAYQABgWGB4yCAgHEAAYFhgeMggICBAAGBYYHjIICAkQABgWGB7SAQgzNzkzajBqN6gCALACAA&sourceid=chrome&ie=UTF-8&ved=2ahUKEwiLxOi4mKWTAxUfkYkEHW3NNloQgK4QegYIAQgAEAY), [Python](https://www.google.com/search?q=Python&rlz=1CAIWTJ_enUS1155&oq=what+is+azure+app+service&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQABiABDIHCAIQABiABDIHCAMQABiABDIHCAQQABiABDIHCAUQABiABDIICAYQABgWGB4yCAgHEAAYFhgeMggICBAAGBYYHjIICAkQABgWGB7SAQgzNzkzajBqN6gCALACAA&sourceid=chrome&ie=UTF-8&ved=2ahUKEwiLxOi4mKWTAxUfkYkEHW3NNloQgK4QegYIAQgAEAc), PHP) on Windows or Linux, offering built-in CI/CD, auto-scaling, and high security. [https://azure.microsoft.com/en-us/products/app-service](https://azure.microsoft.com/en-us/products/app-service)  #### 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 App Services Configuration To configure your Azure Service with the base system tools- this article provides a reference: [MCP Development with Python, and the Azure App Service](https://dev.to/gde/mcp-development-with-python-and-the-azure-app-service-3ekl) #### 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: ```console 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: ```plaintext ▝▜▄ 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](https://github.com/nvm-sh/nvm) #### 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/) #### 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)](https://adk.dev/tutorials/coding-with-ai/) To get the Agent Skills in Gemini CLI: ```plaintext > /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: ```plaintext > /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 App Service. #### 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: ```console cd ~ git clone https://github.com/xbill9/gemini-cli-azure cd multi-appservice ``` Then run **init2.sh** from the cloned directory. The script will attempt to determine your shell environment and set the correct variables: ```console 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: ```console 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 make install ``` #### Verify The ADK Installation To verify the setup, run the ADK CLI locally with the researcher agent: ```console xbill@penguin:~/gemini-cli-azure/multi-appservice/agents$ adk run researcher /home/xbill/.pyenv/versions/3.13.13/lib/python3.13/site-packages/authlib/_joserfc_helpers.py:8: AuthlibDeprecationWarning: authlib.jose module is deprecated, please use joserfc instead. It will be compatible before version 2.0.0. from authlib.jose import ECKey /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() Log setup complete: /tmp/agents_log/agent.20260502_110602.log To access latest log: tail -F /tmp/agents_log/agent.latest.log {"asctime": "2026-05-02 11:06:02,580", "name": "root", "levelname": "INFO", "message": "Logging initialized for researcher", "filename": "logging_config.py", "lineno": 54, "service": "researcher", "log_level": "INFO"} {"asctime": "2026-05-02 11:06:02,582", "name": "researcher.agent", "levelname": "INFO", "message": "Initialized researcher agent with model: gemini-2.5-flash", "filename": "agent.py", "lineno": 85} {"asctime": "2026-05-02 11:06:02,584", "name": "google_adk.google.adk.cli.utils.envs", "levelname": "INFO", "message": "Loaded .env file for researcher at /home/xbill/gemini-cli-azure/multi-appservice/.env", "filename": "envs.py", "lineno": 83} {"asctime": "2026-05-02 11:06:02,584", "name": "google_adk.google.adk.cli.utils.local_storage", "levelname": "INFO", "message": "Using per-agent session storage rooted at /home/xbill/gemini-cli-azure/multi-appservice/agents", "filename": "local_storage.py", "lineno": 84} {"asctime": "2026-05-02 11:06:02,585", "name": "google_adk.google.adk.cli.utils.local_storage", "levelname": "INFO", "message": "Using file artifact service at /home/xbill/gemini-cli-azure/multi-appservice/agents/researcher/.adk/artifacts", "filename": "local_storage.py", "lineno": 110} {"asctime": "2026-05-02 11:06:02,585", "name": "google_adk.google.adk.cli.utils.service_factory", "levelname": "INFO", "message": "Using in-memory memory service", "filename": "service_factory.py", "lineno": 266} {"asctime": "2026-05-02 11:06:02,599", "name": "google_adk.google.adk.cli.utils.local_storage", "levelname": "INFO", "message": "Creating local session service at /home/xbill/gemini-cli-azure/multi-appservice/agents/researcher/.adk/session.db", "filename": "local_storage.py", "lineno": 60} Running agent researcher, type exit to exit. [user]: ``` #### Test The ADK Web Interface This tests the ADK agent interactions with a browser: ```console xbill@penguin:~/gemini-cli-azure/multi-appservice/agents$ adk web --host 0.0.0.0 /home/xbill/.pyenv/versions/3.13.13/lib/python3.13/site-packages/authlib/_joserfc_helpers.py:8: AuthlibDeprecationWarning: authlib.jose module is deprecated, please use joserfc instead. It will be compatible before version 2.0.0. from authlib.jose import ECKey /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() 2026-05-02 11:08:08,960 - INFO - service_factory.py:266 - Using in-memory memory service 2026-05-02 11:08:08,960 - INFO - local_storage.py:84 - Using per-agent session storage rooted at /home/xbill/gemini-cli-azure/multi-appservice/agents 2026-05-02 11:08:08,961 - INFO - local_storage.py:110 - Using file artifact service at /home/xbill/gemini-cli-azure/multi-appservice/agents/.adk/artifacts /home/xbill/.pyenv/versions/3.13.13/lib/python3.13/site-packages/google/adk/cli/fast_api.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/.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 [7020] INFO: Waiting for application startup. +-----------------------------------------------------------------------------+ | ADK Web Server started | | | | For local testing, access at http://0.0.0.0:8000. | +-----------------------------------------------------------------------------+ INFO: Application startup complete. INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit) ``` 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: ```console 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](https://dev.to/gde/multi-agent-a2a-with-the-agent-development-kitadk-cloud-run-agent-skills-and-gemini-cli-4n1h) #### 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: ```console xbill@penguin:~/gemini-cli-azure/multi-appservice$ 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: ```console xbill@penguin:~/gemini-cli-azure/multi-appservice$ make status 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. ``` Then all the agents can be started together: ```console xbill@penguin:~/gemini-cli-azure/multi-appservice$ make start 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 xbill@penguin:~/gemini-cli-azure/multi-appservice$ make status Checking status of locally running agents and servers... --- Network Status --- tcp 0 0 0.0.0.0:8004 0.0.0.0:* LISTEN 8177/python3 tcp 0 0 0.0.0.0:8002 0.0.0.0:* LISTEN 7851/python3 tcp 0 0 0.0.0.0:8003 0.0.0.0:* LISTEN 7850/python3 tcp 0 0 0.0.0.0:8000 0.0.0.0:* LISTEN 8185/python3 tcp 0 0 0.0.0.0:8001 0.0.0.0:* LISTEN 7847/python3 tcp 0 0 0.0.0.0:5173 0.0.0.0:* LISTEN 8410/node --- Process Status --- ``` The entire project can be linted and tested as unit: ```console > make lint ✦ I will run make lint to verify everything is clean. ╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ ✓ Shell make lint │ │ │ │ ruff check . │ │ All checks passed! │ │ │ ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ✦ The make lint check has passed successfully with no errors or warnings remaining in the codebase! Is there anything else you would like to do? ``` And end to end tested: ```console ✓ 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: ```plaintext ✦ 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 App Service The project level Makefile has targets for managing the Agent deployment to serverless endpoints: ```console xbill@penguin:~/gemini-cli-azure/multi-appservice$ 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 App Service: ```console > make status-as ✦ I will run the make status-as command to show you the current status of your App Services. ╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ ✓ Shell make status-as │ │ │ │ ./appservice/status-appservice.sh │ │ === Azure App Service Status === │ │ Resource Group: adk-rg-as │ │ Filtering for: adk-penguin-* │ │ --- adk-penguin-full --- │ │ State HostNames │ │ ------- ---------------------------------- │ │ Running adk-penguin-full.azurewebsites.net │ │ │ ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ✦ The make status-as command confirms that your single-container deployment (adk-penguin-full) is currently Running. ``` You can then deploy the services: ```console > make deploy ✦ I am starting the deployment of the multi-agent microservices architecture to Azure App Service. This process builds multiple containers and deploys several web apps, so it might take a few minutes. ``` And check the endpoint: ```console > make endpoint-as The public endpoint for your application is: https://adk-penguin-full.azurewebsites.net ``` The service will be visible in the Azure console:  #### Test End to End in App Service The entire agent system is tested on the remote Azure endpoint: ```console ✦ The make endpoint-appservice command has successfully retrieved and displayed the public URL for the Azure Container App. > make e2e-test-appservice │ E2E Test Completed successfully! │ │ make[1]: Leaving directory '/home/xbill/gemini-cli-azure/multi-appservice' │ │ Output too long and was saved to: │ │ /home/xbill/.gemini/tmp/multi-appservice/tool-outputs/session-7a0ea6eb-af02-462d-b737-692bcda1caa3/run_shell_comm │ │ and_w9nn9sur.txt │ ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ✦ The End-to-End test against your Azure App Service deployment completed successfully! The multi-agent pipeline successfully researched and generated a course on the history of the internet. ``` #### Running the Web Interface Start a connection to the Cloud Run deployed app: ```plaintext https://adk-penguin-full.azurewebsites.net ``` 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: ```plaintext Overall Code Review Summary After reviewing the app and agents directories, I can say this is an exceptionally well-engineered multi-agent system. High-Level Architecture: The architecture is sophisticated and effective. The use of a main orchestrator to manage a pipeline of specialized agents (researcher, judge, content_builder) is a strong and scalable pattern. The inclusion of a research-and-refine loop with the judge agent is a standout feature that significantly enhances the quality of the final output. Key Strengths: 1. Expert ADK Usage: The project demonstrates a deep understanding of the Google ADK, using advanced features like SequentialAgent, LoopAgent, RemoteA2aAgent, structured Pydantic outputs, and agent callbacks to their full potential. 2. Excellent Prompt Engineering: The instruction prompts for all agents are clear, specific, and well-crafted. This is the foundation of the system's success. 3. Robust State Management: The custom StateCapturer agent is a brilliant, reusable utility that cleanly handles the flow of information between agents. 4. Production-Ready Features: The system includes production-grade features like environment-aware authentication for service-to-service calls, robust error handling, and detailed logging. ``` #### 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 App Service. 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.