To build a custom work agent and host it remotely on Google Cloud, this guide uses Google’s code-first path: Agent Development Kit (ADK) with Agent Runtime, documented under Gemini Enterprise Agent Platform. You’ll prepare a project and its access, develop a Python agent, choose a deployment method, then test the deployed agent. Google also offers Agent Studio and the Managed Agents API; they are separate paths, not steps in this guide. The setup below does not describe every Google Gemini product or a prebuilt workplace assistant.
What you need before you start
- A Google Cloud project you can access, with billing enabled.
- Permission to enable the APIs required for the deployment method you choose. Google lists
serviceusage.services.enable; if you lack it, ask an administrator about the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). - The Agent Platform User role (
roles/aiplatform.user) for Agent Runtime use. Project creation is a separate permission: Google lists Project Creator (roles/resourcemanager.projectCreator). - A choice of authoring and deployment approach. This walkthrough uses Python and ADK, but not all Agent Runtime deployment methods support the same languages or workflows.
Google’s environment setup guide lists Gemini Enterprise Agent Platform, Cloud Storage, Cloud Logging, Cloud Monitoring, Telemetry, Cloud Trace, and Resource Manager APIs for its documented runtime environment. The ADK quickstart describes enabling Agent Platform and Cloud Storage APIs for that quickstart. Requirements can differ by deployment route, so use the current instructions for the route you actually deploy rather than assuming every API is necessary in every case.
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Choose the development path and deployment route
Google documents several ways to build agents, including low-code Agent Studio, the Managed Agents API, ADK, and other frameworks. ADK is a practical code-first choice when you want to build an agent in Python and follow Google’s documented develop-deploy-test flow. The framework you choose and the method you use to deploy it are related but distinct decisions.
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For Agent Runtime, compare the documented deployment routes before writing release automation:
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| Route | Fits | Constraints and considerations |
|---|---|---|
| Developer Connect | Deploying from a linked Git repository, with version control, team collaboration, and CI/CD. | Python only; create the Developer Connect repository link first. |
| Source files | Declarative or automated workflows, including CI/CD and infrastructure-as-code. | Python only. Google says this route does not require a Cloud Storage bucket. |
| Dockerfile | Teams that want to define and control the API server in the deployed container. | Any language; the container must follow the Agent Runtime contract. |
| Container image | Teams that want control over the image build. | Any language; the image must follow the runtime contract. |
| Agent Platform SDK object | Interactive development, including Colab, using an in-memory local_agent. |
Best suited to agents without complex non-serializable structures. Deployment stages artifacts in Cloud Storage. |
These capabilities and limits come from Google’s deployment documentation. Google describes Developer Connect and source-file deployment as Python-only; Dockerfile and container-image routes support any language. ADK itself is available in several languages, but that does not mean every runtime deployment route accepts all of them.
Prepare the project, APIs, and identity
1. Select a project and confirm billing
- In the Google Cloud console, select a project you can access, or create one. Creating a project requires Project Creator permission; selecting an existing project shared with you does not require a special project-selection role.
- Confirm billing is enabled for the project before continuing.
- Enable the APIs required by the deployment method, following the current environment setup and method-specific instructions.
2. Request the right project access
Ask for Agent Platform User (roles/aiplatform.user) if you need access to Agent Runtime. API enablement is separate: it requires serviceusage.services.enable, for which Google directs users without permission to request Service Usage Admin (roles/serviceusage.serviceUsageAdmin). Project creation, runtime access, and API enablement are different permissions; having one does not automatically grant the others.
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- Alexa can show you more - Echo Show 5 includes a 5.5” display so you can see news and weather at a glance, make video calls, view compatible cameras, stream music and shows, and more.
- Small size, bigger sound – Stream your favorite music, shows, podcasts, and more from providers like Amazon Music, Spotify, and Prime Video—now with deeper bass and clearer vocals. Includes a 5.5" display so you can view shows, song titles, and more at a glance.
- Keep your home comfortable – Control compatible smart devices like lights and thermostats, even while you're away.
- See more with the built-in camera – Check in on your family, pets, and more using the built-in camera. Drop in on your home when you're out or view the front door from your Echo Show 5 with compatible video doorbells.
- See your photos on display – When not in use, set the background to a rotating slideshow of your favorite photos. Invite family and friends to share photos to your Echo Show. Prime members also get unlimited cloud photo storage.
3. Decide what identity the deployed agent needs
Google documents a default Reasoning Engine service agent and also supports agent identity and custom service-account options for more granular access. Decide which identity your deployment will use, then grant it only the permissions needed for its actual work. If the agent accesses resources in another project, follow Google’s cross-project setup guidance rather than assuming project-level access carries over.
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Cloud Storage is not required for deployment from source files according to Google’s environment setup guide. In-memory Agent Platform SDK object deployment stages artifacts in a bucket, and the authenticated principal needs Storage Admin access to that bucket. Google recommends putting the bucket in the deployment project; for a bucket in another project, grant the Agent Platform service agent read access there.
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Set up Python and build the agent
1. Create an isolated development environment
Follow the current ADK quickstart to install the Agent Platform SDK for Python and ADK, create its example agent, and work through deployment and testing. Google recommends using a virtual environment to isolate dependencies. Package versions and exact commands can change, so use the current quickstart instructions rather than relying on copied commands from an older guide.
2. Define the work task before adding tools
Replace a tutorial example with a narrowly defined workplace task. Write down what the agent is expected to do, which tools it may use, what data it may access, and how it should respond when it cannot complete a request. This boundary-setting makes it possible to assess whether a deployed response is actually useful and whether its permissions are appropriate.
3. Keep deployment requirements in mind while developing
Google’s agent-creation guidance says deployed agent objects and tools need to be serializable. It recommends separating initialization—such as loading models and setting up tools—from query execution; framework templates handle these patterns. If you use in-memory object deployment, check early that your agent’s components can be serialized. Google also advises pinning package versions for reproducible builds and minimizing dependencies.
See Google’s agent creation guidance for framework and lifecycle considerations.
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Deploy the agent
Use the deployment instructions for the route selected above. For the ADK walkthrough, follow the quickstart’s sequence to create the example, deploy it to Agent Runtime, and test the remote deployment. If your team releases from source control, Developer Connect or source-file deployment may fit its workflow; use Dockerfile or container-image deployment when control of the server or image build is important. No single route is universally best.
For object deployment, verify that the bucket and permissions are ready before deployment. For a container route, ensure that the image or container implements the runtime contract. For automated releases, pin dependencies and make the build reproducible so a later deployment does not silently pick up different package versions.
Test the remote agent and evaluate it before relying on it
Send the deployed agent representative requests for its intended workplace task. Check not just whether it returns a response, but whether the response is correct for the request, whether its tools work, and whether the permissions it uses are limited to what the task requires. Test expected edge cases and requests outside the intended scope as well.
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