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For a code-first Gemini agent, use Google’s open-source Agent Development Kit (ADK): define the agent and its tools, wrap it in AdkApp, exercise it locally with async_stream_query, then deploy the application to Agent Runtime or another supported target. Agent Runtime is the managed destination for ADK agents; Cloud Run and Google Kubernetes Engine (GKE) are alternatives when you need more control over networking, containers, or platform operations. The practical sequence is project and API setup, pinned dependencies, agent definition, local tests, a staged deployment with an identity and Cloud Storage bucket, remote validation, and production governance through sessions, memory, gateways, evaluation, and observability.
What does Google Cloud’s agent infrastructure provide?
Google Cloud’s agent platform combines development frameworks with managed services for running and governing agents. Its documented building blocks include Agent Runtime, sessions, Memory Bank, agent identity, Agent Gateway, an agent registry, evaluation, and Cloud Observability and Topology. You can therefore treat an agent as more than a model call: it has executable tools, a runtime identity, conversation state, deployment controls, and operational telemetry.
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- Agent Studio is the low-code route for assembling agents without writing the full orchestration layer.
- The managed Agents API is configuration-driven and suits standardized agent definitions managed through an API.
- ADK is the code-first route for custom tools, branching logic, multi-agent orchestration, debugging, evaluation, and application-specific behavior.
This guide concentrates on ADK because it gives you the control implied by a custom Gemini agent while still allowing deployment to Google-managed infrastructure. ADK supports Python, TypeScript, Go, and Java, and an ADK agent can run locally, on Agent Runtime, on Cloud Run, or on GKE.
Which Google Cloud build path should I choose?
Choose the path that matches how much code and infrastructure you need to own. A prototype can start in Studio, while a production system with custom tools and orchestration generally belongs in ADK.
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| Path | Best fit | What you control | Important constraint |
|---|---|---|---|
| Agent Studio | Low-code experiments and straightforward workflows | Configuration and the visual agent design | Less control than a code-defined ADK application for complex orchestration |
| Managed Agents API | Configuration-driven agents managed through an API | Declarative agent settings and API integration | Custom application logic may require a different path |
| ADK with Agent Platform SDK | Interactive development, notebooks, and in-memory agent objects | Tools, prompts, orchestration, and application code | Complex non-serializable objects can limit SDK-based deployment |
| ADK from source files | Declarative CI/CD and infrastructure-as-code workflows | Source-controlled build and deployment configuration | The documented source-file method is Python-only |
| ADK through Developer Connect | A Git repository with collaborative, version-controlled CI/CD | Repository-driven release workflow | The documented Developer Connect method is Python-only |
| ADK with a Dockerfile | Custom API-server behavior or a controlled runtime contract | Server implementation, dependencies, and image build | Your server must satisfy the Agent Runtime contract; any language is possible when it does |
| ADK as a container image | Full control of image construction or lower deployment latency | Image layers, OS packages, and startup behavior | The image is hosted in Artifact Registry and must satisfy the runtime contract |
What do I need before building the agent?
Prepare the Google Cloud and local development prerequisites before writing orchestration code.
- A Google Cloud project: create or select one and ensure you have permission to enable the APIs required by the current quickstart.
- Billing and quota: confirm that the project can call the Gemini model and create the runtime resources your chosen deployment needs. Exact quotas and product availability vary by region and can change.
- Local tooling: use a supported Python, TypeScript, Go, or Java environment for your ADK language. The step-by-step workflow below uses Python concepts because the documented quickstart and SDK deployment path are Python-oriented.
- Version pins: pin the ADK, Agent Platform SDK, language runtime, and any tool libraries. The package names, preview labels, and model identifiers are revised over time, so use the versions in the current Google Cloud quickstart rather than copying an old lockfile blindly.
- Deployment resources: for the Agent Runtime quickstart, plan a staging Cloud Storage bucket and an agent identity with the permissions required to invoke models and tools.
How do I define a Gemini agent with ADK?
Start with one focused agent, one explicit instruction set, and small, testable tools. The agent should make a clear decision about when to call a tool and what information to return if the tool fails.
1. Install ADK and the Agent Platform SDK
Install the ADK and the Google Cloud Agent Platform SDK for your language, then record the exact versions in your project’s dependency lockfile. Keep the dependency set small: every extra package increases build time, image size, and the number of transitive versions that can differ between local and deployed environments. The current quickstart is the authority for package names, supported SDK versions, authentication setup, and required API enablement.
2. Configure the Gemini model and agent identity
Give the agent a stable name, select a Gemini model available to your project and region, and write an instruction that states the agent’s role, allowed actions, response format, and escalation behavior. Model identifiers and regional availability can change, so treat the identifier as deployment configuration rather than hard-coded business logic.
3. Add narrowly scoped tools
Tools are ordinary functions or services that perform work the model should not invent. A currency-exchange function is a useful example: it accepts a base currency and target currency, validates both values, calls an authoritative rate source, and returns a timestamped result. The model decides when to call it, but the function—not the model—should validate inputs, enforce authorization, handle timeouts, and report errors.
4. Wrap the agent in AdkApp
AdkApp is the application boundary used by the documented workflow. Keep model configuration, tool definitions, and orchestration inside the application object so the same object can be tested locally and passed to the deployment client. A representative structure looks like this; field names and imports must match the ADK release you install:
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agent = Agent(
name='currency_agent',
model='<current-Gemini-model-id>',
instruction='Give exchange-rate answers using the rate tool; explain failures clearly.',
tools=[get_exchange_rate],
)
app = AdkApp(agent=agent)The snippet shows the design rather than a promise that these exact constructor names remain unchanged. Check the current ADK reference for the release-specific import paths and required fields.
How do I test the agent locally before deployment?
Run the agent in the local ADK harness first. The documented pattern calls async_stream_query with a user ID and a message, and local ADK testing uses in-memory sessions.
- Start the local environment with the same dependency lockfile and environment variables you intend to use in deployment.
- Send a normal user request and at least one request that should trigger each tool.
- Inspect every streamed event, including intermediate tool calls, final text, and error events.
- Repeat the test with the same user ID to verify that the agent handles conversation turns as expected.
- Try malformed tool arguments, unavailable data, slow responses, and an unauthorized operation. Confirm that the agent fails safely instead of fabricating a result.
async for event in app.async_stream_query(
user_id='local-test-user',
message='What is the current exchange rate from EUR to USD?'
):
print(event)Because the local session store is in memory, restarting the process clears that state. Use local tests to validate orchestration and tool behavior, not to prove that production persistence, IAM, networking, or quotas are configured.
How do I deploy an ADK agent to Agent Runtime?
Agent Runtime is Google’s managed host for agents developed with ADK. The quickstart deploys through the Agent Platform client to a reasoningEngine resource, using a staging Cloud Storage bucket and an agent identity.
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- Select the project and region. Make sure the deployment project is the one where the required APIs are enabled and where the runtime identity has access to the model and tools.
- Create or select a staging Cloud Storage bucket. The deployment process uses this bucket to stage application materials. Keep the bucket in an appropriate location for your data-residency and latency requirements.
- Configure the agent identity. Grant only the permissions the agent needs: model invocation, access to its tool backends, logging, and any session or memory services you intentionally use. Do not reuse a broad human administrator identity for runtime calls.
- Pass the
AdkAppto the Agent Platform client. The client packages the application and creates the remotereasoningEngineresource. Follow the current SDK method names and required fields because these APIs and preview labels can change. - Wait for the long-running operation to complete. Record the resulting resource name, region, and revision metadata in your release record. A successful API response means the resource was created; it does not by itself prove that every tool path works.
- Run a remote smoke test. Send a streamed query to the deployed resource with a test user ID, compare the events with the local run, and inspect logs for permission, dependency, or timeout errors before exposing the endpoint to users.
Deploying to Agent Runtime makes the agent available remotely to handle requests, while Google manages the hosting layer. You still own the agent’s instructions, tool security, data handling, release process, and quality evaluation.
Should I use Agent Runtime, Cloud Run, or GKE?
Use Agent Runtime when you want a managed ADK host with the least infrastructure work. Choose Cloud Run for a containerized service with straightforward HTTP and autoscaling controls, or GKE when you need Kubernetes-level networking, scheduling, and platform customization.
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| Consideration | Agent Runtime | Cloud Run | GKE |
|---|---|---|---|
| Primary advantage | Managed hosting designed for ADK agents | Managed containers with a simple operational model | Maximum control over Kubernetes networking and workloads |
| Best deployment inputs | Agent Platform SDK and supported ADK packaging | Dockerfile or a compliant container image | Container images and Kubernetes manifests or operators |
| Server control | Lower; follow the managed runtime contract | Control the service process and HTTP surface | Control pods, services, sidecars, and cluster policies |
| Language choice | Depends on the supported ADK and deployment method | Any language that meets the container and server contract | Any language that runs in your containers |
| Operational burden | Lowest infrastructure burden | Moderate; you manage the image and service configuration | Highest; you operate or integrate with a Kubernetes cluster |
| When to avoid it | When you need unsupported native extensions or unusual server behavior | When cluster-level scheduling or networking is essential | When your team does not need Kubernetes control |
Use the Agent Platform SDK for interactive development
The SDK path is suited to notebooks, Colab-style experimentation, and in-memory agent objects. It is convenient for iterating on prompts and tools, but complex objects that cannot be serialized are a limitation. Move to source, Developer Connect, a Dockerfile, or a prebuilt image when reproducibility and release automation matter more than interactive convenience.
Use source files or Developer Connect for CI/CD
Source-file deployment fits declarative pipelines and infrastructure-as-code. Developer Connect fits a Git repository where review, branching, and collaborative CI/CD are central. The documented methods for both are Python-only, so a TypeScript, Go, or Java team should verify the currently supported packaging route before standardizing on one.
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Use a Dockerfile or image for runtime control
A Dockerfile is appropriate when you need to define the API server or runtime contract yourself. A prebuilt container image is appropriate when you already have a controlled build pipeline or want to minimize deployment-time image construction. Images are stored in Artifact Registry and must satisfy the runtime contract expected by the target.
How should I expose the agent in a production architecture?
A Google Cloud reference design places an ADK agent on Cloud Run, with Agent Runtime and GKE as alternatives. Put a deliberate boundary around the agent rather than allowing arbitrary model-generated calls to reach internal systems.
- Ingress and identity: accept requests through an authenticated service boundary and map the caller to a user or tenant identity before creating a session.
- Agent service: run the ADK application on the selected target and restrict its outbound access to the tool services it actually needs.
- Model and response controls: use Model Armor where required to inspect and filter prompts and responses for unsafe or disallowed content.
- Data tools: use MCP Toolbox for Databases for database-specific tools when it fits your architecture. Keep database credentials and query authorization outside the model prompt.
- External capabilities: connect to MCP clients and servers only after reviewing their authentication, data egress, and failure behavior.
- State services: use platform sessions for conversation state and Memory Bank for longer-term, personalized memory. Do not treat either service as a replacement for a transactional system of record.
- Governance: apply agent identity, Agent Gateway, registry, and policy controls so that only approved versions and callers can invoke the agent.
- Operations: use Gen AI evaluation for quality checks and Cloud Observability and Topology for logs, metrics, traces, and dependency visibility.
How do sessions and Memory Bank differ?
Sessions represent the conversational context needed to answer a sequence of turns; Memory Bank is for information that should persist as personalized memory beyond one conversation. Decide what belongs in each before deployment, because retaining every transcript as long-term memory can expose sensitive data and make responses harder to predict.
Session design checklist
- Assign a stable user or tenant identifier after authentication.
- Define session expiration and deletion behavior.
- Limit the context passed to the model to what the current task requires.
- Test concurrent requests so one user’s turns cannot enter another user’s session.
- Record a correlation ID that lets you trace a response without logging secrets or full sensitive prompts.
Memory Bank checklist
- Store only facts with a clear user benefit and an explicit retention policy.
- Give users a way to inspect, correct, or delete durable memory where your product requires it.
- Separate durable preferences from authoritative business records.
- Evaluate whether retrieved memories change answers in ways that are useful, safe, and explainable.
How do I secure tools and agent identity?
Prompt instructions are not an authorization system. Enforce permissions in the tool implementation and at the service boundary, using the runtime identity as the principal that Google Cloud can audit.
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- Define the smallest set of IAM roles required for model invocation, session or memory access, logging, and each backend tool.
- Validate every tool argument server-side, including identifiers, ranges, tenant ownership, and allowed operations.
- Use timeouts, retries with limits, and circuit breakers for remote tools so a stalled dependency cannot consume all agent capacity.
- Redact secrets and sensitive payloads from logs. Log tool names, status, latency, and correlation IDs instead.
- Place risky or irreversible actions behind explicit confirmation or a separate approval workflow.
- Use Model Armor and gateway policies where your threat model requires prompt and response inspection.
- Register only approved agent revisions and require a controlled promotion step from test to production.
How should I evaluate and observe a Gemini agent?
Evaluation should cover both answer quality and the reliability of the tool workflow. A fluent response can still be wrong if the agent selected an unauthorized tool, used stale data, or silently dropped an error.
Build an evaluation set
- Include normal questions, ambiguous requests, unsupported requests, and adversarial prompts.
- Include cases that require each tool, cases where a tool returns an error, and cases where no tool should be called.
- Score factual correctness, citation or provenance behavior where applicable, tool-selection accuracy, latency, and safe refusal behavior.
- Run the set against the local app and the deployed revision, then compare streamed events as well as final text.
Instrument the runtime
- Track request, model, tool, and session correlation IDs.
- Measure end-to-end latency and the latency of every tool call.
- Alert on authentication failures, repeated tool errors, rising timeouts, and unusual request volume.
- Use Cloud Observability and Topology to connect agent symptoms with the model, runtime, database, or external service involved.
- Keep evaluation results with the revision that produced them so a rollback has a known quality baseline.
What commonly fails during build and deployment?
The local app works but deployment packaging fails
This usually indicates an unsupported dependency, a non-serializable object, a missing runtime entry point, or a mismatch between the selected deployment method and language. Reduce the application to a minimal AdkApp, verify the supported contract, and move complex initialization into the server or container path when appropriate.
The remote agent cannot invoke Gemini or a tool
Check the deployed region, enabled APIs, runtime identity, and IAM permissions first. Then inspect whether the tool endpoint is reachable from the selected runtime and whether its credentials are available to the service rather than only to your local shell.
Streaming output differs between local and remote tests
Compare model identifier, instruction text, dependency versions, environment variables, and session history. A local in-memory session may contain turns that are absent remotely, so start both tests with fresh user IDs and identical messages before comparing events.
Requests time out or consume excessive resources
Set explicit deadlines on model and tool calls, cap retries, and stream partial progress to the caller when the interface supports it. Profile the slowest tool, reduce unnecessary context, and choose Cloud Run or GKE only when their operational controls solve a measured bottleneck rather than adding infrastructure by default.
A model update changes behavior
Model identifiers and platform labels can change. Keep the model selection in configuration, rerun the evaluation set for every model or ADK upgrade, and promote a new revision only after checking tool calls, safety behavior, latency, and cost against the previous baseline.
What should I verify before opening the agent to users?
- The project, region, enabled APIs, quotas, and billing account are correct.
- ADK, Agent Platform SDK, language runtime, and tool dependencies are pinned and reproducibly built.
- The runtime identity has least-privilege access and no human-only credentials are embedded in the image or source.
- Sessions isolate users and tenants, and retention and deletion policies are documented.
- Tools validate authorization and input values independently of model instructions.
- Model Armor, Agent Gateway, registry policies, and network controls match your threat model.
- Evaluation covers ordinary, unsafe, ambiguous, and tool-failure cases.
- Cloud Observability and Topology dashboards and alerts are in place before launch.
- A rollback path exists for the agent revision, model configuration, container image, and tool dependencies.
- The deployment method matches your team: SDK for interactive work, source or Developer Connect for CI/CD, and Dockerfile or image when runtime control is essential.
How do I keep the deployment maintainable as Google Cloud changes?
Separate stable application behavior from volatile platform settings. Keep model IDs, regions, runtime resource names, bucket names, and feature-preview flags in environment-specific configuration; keep tool interfaces and safety policies under source control; and record the exact versions used for each release.
Review the current Google Cloud documentation before upgrading because supported languages, package versions, deployment methods, permissions, and preview labels can change. Treat a platform upgrade as a tested release: rebuild from the lockfile, deploy to a non-production resource, run the full evaluation set, perform remote smoke tests, and only then promote the revision.
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Can I put an existing API gateway in front of an ADK agent?
Yes. Place the gateway or an authenticated service in front of the agent endpoint, then pass a verified caller and tenant identity into the session creation step. Keep rate limits, request validation, and authorization at that boundary rather than relying on the model to enforce them.
What happens to a local conversation when I deploy the agent?
It does not automatically move with the deployment. Local ADK sessions are in memory, so a restart or a new process removes them; production conversations must be created and retained through the platform session design you configure.
Do I need Memory Bank for every Gemini agent?
No. Use sessions for short-lived conversational context and add Memory Bank only when durable, personalized recall is a deliberate product requirement. Transactional facts, audit records, and authoritative business data should remain in their systems of record.
How can I make an agent release reproducible?
Lock the language runtime and ADK and SDK versions, keep the model identifier and deployment configuration under version control, and build immutable container images where you use containers. Store evaluation results with each revision so you can distinguish a code change from a model or platform change.
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