google-agents-cli-deploy

Original：🇺🇸 English

Translated

This skill should be used when the user wants to "deploy an agent", "deploy my ADK agent", "set up CI/CD", "configure secrets", "troubleshoot a deployment", or needs guidance on Agent Runtime, Cloud Run, or GKE deployment targets. Covers deployment workflows, service accounts, rollback, and production infrastructure. Part of the Google ADK (Agent Development Kit) skills suite. Do NOT use for API code patterns (use google-agents-cli-adk-code), evaluation (use google-agents-cli-eval), or project scaffolding (use google-agents-cli-scaffold).

516.7kinstalls

Sourcegoogle/agents-cli

Added on2026-04-22

NPX Install

npx skill4agent add google/agents-cli google-agents-cli-deploy

SKILL.md Content

View Translation Comparison →

ADK Deployment Guide

Requires:
agents-cli
(
uv tool install google-agents-cli
) — install uv first if needed.

Prefer using the
agents-cli
commands throughout this guide — they wrap Terraform, Docker, and deployment into a tested pipeline. If your project isn't scaffolded yet, see
/google-agents-cli-scaffold
to add deployment support first.

Reference Files

For deeper details, consult these reference files in

references/

cloud-run.md
— Scaling defaults, Dockerfile, session types, networking
agent-runtime.md
— deploy.py CLI, AdkApp pattern, Terraform resource, deployment metadata, CI/CD differences
gke.md
— GKE Autopilot cluster, Kubernetes manifests, Workload Identity, session types, networking
terraform-patterns.md
— Custom infrastructure, IAM, state management, importing resources
batch-inference.md
— BigQuery Remote Function trigger; for Pub/Sub / Eventarc see
```
/google-agents-cli-adk-code
```
cicd-pipeline.md
— Full CI/CD pipeline setup,
```
infra cicd
```
flags, runner comparison, WIF auth, pipeline stages
testing-deployed-agents.md
— Testing instructions per deployment target, curl examples, load tests

Observability: See the
/google-agents-cli-observability
skill for Cloud Trace, prompt-response logging, BigQuery Analytics, and third-party integrations.

Deployment Target Decision Matrix

Choose the right deployment target based on your requirements:

Criteria	Agent Runtime	Cloud Run	GKE
Languages	Python	Python	Python (+ others via custom containers)
Scaling	Managed auto-scaling (configurable min/max, concurrency)	Fully configurable (min/max instances, concurrency, CPU allocation)	Full Kubernetes scaling (HPA, VPA, node auto-provisioning)
Networking	VPC-SC and PSC supported	Full VPC support, direct VPC egress, IAP, ingress rules	Full Kubernetes networking
Session state	Native `VertexAiSessionService` (persistent, managed)	In-memory (dev), Cloud SQL, or Agent Platform Sessions backend	In-memory (dev), Cloud SQL, or Agent Platform Sessions backend
Batch/event processing	Not supported	Native trigger endpoints (Pub/Sub, Eventarc); see `/google-agents-cli-adk-code`	Custom (Kubernetes Jobs, Pub/Sub)
Cost model	vCPU-iours + memory-iours (not billed when idle)	Per-instance-second + min instance costs	Node pool costs (always-on or auto-provisioned)
Setup complexity	Lower (managed, purpose-built for agents)	Medium (Dockerfile, Terraform, networking)	Higher (Kubernetes expertise required)
Best for	Managed infrastructure, minimal ops	Custom infra, event-driven workloads	Full Kubernetes control

Ask the user which deployment target fits their needs. Each is a valid production choice with different trade-offs.

Product name mapping: "Agent Engine" / "Vertex AI Agent Engine" is now Agent Runtime. Use
--deployment-target agent_runtime
.

Ambient / scheduled / event-driven agents: Agent Runtime does not support Pub/Sub, Eventarc, or Cloud Scheduler triggers. Use Cloud Run (recommended) or GKE for these workloads. See
/google-agents-cli-adk-code
Section 12 for the
trigger_sources
pattern.

OAuth / user consent agents: Use Agent Runtime with Gemini Enterprise for agents that need OAuth 2.0 user consent (e.g., accessing Google Drive, Calendar, or other user-scoped APIs). Cloud Run does not currently support managed OAuth flows. See the
adk-ae-oauth
sample in
/google-agents-cli-workflow
Phase 2.

Deploying to Dev

Deploy Workflow

Task tracking: Deployment involves multiple sequential steps (infra setup, CI/CD configuration, deploy, verification). Use a task list to track progress through these steps — skipping one often causes failures in later steps that are hard to trace back.

If prototype (no deployment target), first enhance:

agents-cli scaffold enhance . --deployment-target <target>

Notify the human: "Eval scores meet thresholds and tests pass. Ready to deploy to dev?"
Wait for explicit approval
Once approved:
```
agents-cli deploy
```

Agent Runtime timeout recovery: Agent Runtime deploys can take 5-10 minutes and may exceed command timeouts. If the deploy command is cancelled or times out, the deployment continues server-side. Run
agents-cli deploy --status
to check progress — poll every 60 seconds until it reports completion or failure.

IMPORTANT: Never run

agents-cli deploy

without explicit human approval.

Do NOT run
agents-cli infra single-project
before deploying. It is not a prerequisite —
agents-cli deploy
works on its own. Run it separately if the user needs observability features (prompt-response logging, BigQuery analytics) — see
/google-agents-cli-observability
.

Single-Project Infrastructure Setup (Optional — Advanced)

agents-cli infra single-project

runs

terraform apply

deployment/terraform/single-project/

. Use this to provision single-project GCP infrastructure without CI/CD (service accounts, IAM bindings, telemetry resources, Artifact Registry). Also useful to test things in a single project before going to production. It is NOT required for deploying.

bash

# Optional — provision infrastructure in a single GCP project
agents-cli infra single-project

Note:
agents-cli deploy
doesn't automatically use the Terraform-created
app_sa
. Pass the service account via
agents-cli deploy --service-account SA_EMAIL
or
uv run -m app.app_utils.deploy --service-account SA_EMAIL
for Agent Runtime targets.

Deploy Flag Reference

Flag	Description	Targets
`--project`	GCP project ID	All
`--region`	GCP region	All
`--service-account`	Service account email for the deployed agent	All
`--secrets`	Comma-separated `ENV=SECRET` or `ENV=SECRET:VERSION` pairs	Agent Runtime
`--update-env-vars`	Comma-separated `KEY=VALUE` environment variables	Agent Runtime, Cloud Run
`--agent-identity`	Enable agent identity (Preview)	Agent Runtime
`--memory`	Memory limit (default: `4Gi` )	Cloud Run
`--port`	Container port	Cloud Run
`--iap`	Enable Identity-Aware Proxy	Cloud Run
`--image`	Container image URI (skips source build)	Cloud Run, GKE
`--no-wait`	Start deployment and return immediately	Agent Runtime, Cloud Run
`--status`	Check the status of a pending `--no-wait` deployment	Agent Runtime, Cloud Run
`--list`	List existing deployments and exit	All
`--dry-run` / `-n`	Print what would be executed without running it	All
`--no-confirm-project`	Skip project confirmation prompt	All

Run

agents-cli deploy --help

for the full flag reference. Cloud Run also accepts extra

gcloud

flags after

--

(e.g.,

-- --timeout=600

Project Confirmation: If the project is resolved automatically (not passed via
--project
), the command will prompt for confirmation in interactive mode. Since agents typically run in non-interactive mode, you MUST pass
--no-confirm-project
to proceed if you are relying on automatic project resolution.

Production Deployment — CI/CD Pipeline

For the full CI/CD pipeline setup guide — prerequisites,

infra cicd

flags, runner comparison, WIF authentication, pipeline stages, and production approval — see

references/cicd-pipeline.md

Cloud Run Specifics

For detailed infrastructure configuration (scaling defaults, Dockerfile, FastAPI endpoints, session types, networking), see

references/cloud-run.md

. For ADK docs on Cloud Run deployment, fetch

https://adk.dev/deploy/cloud-run/index.md

For event-driven / ambient agent deployment on Cloud Run, see the

ambient-expense-agent

sample and

/google-agents-cli-adk-code

for the

trigger_sources

pattern.

Agent Runtime Specifics

Agent Runtime is a managed Vertex AI service for deploying Python ADK agents. Uses source-based deployment (no Dockerfile) via

deploy.py

and the

AdkApp

class.

No
gcloud
CLI exists for Agent Runtime. Deploy via
agents-cli deploy
or
deploy.py
. Query via the Python
vertexai.Client
SDK.

Deployments can take 5-10 minutes. Use

--no-wait

to start a deployment and return immediately, then check on it later with

--status

bash

# Start deployment without blocking
agents-cli deploy --no-wait

# Check on progress later
agents-cli deploy --status

When

--status

detects the operation has completed, it writes

deployment_metadata.json

and prints the same success output as a normal deploy.

For detailed infrastructure configuration (deploy.py flags, AdkApp pattern, Terraform resource, deployment metadata, session/artifact services, CI/CD differences), see

references/agent-runtime.md

. For ADK docs on Agent Runtime deployment, fetch

https://adk.dev/deploy/agent-engine/index.md

GKE Specifics

For detailed infrastructure configuration (Kubernetes manifests, Terraform resources, Workload Identity, session types, networking), see

references/gke.md

. For ADK docs on GKE deployment, fetch

https://adk.dev/deploy/gke/index.md

Service Account Architecture

Scaffolded projects use two service accounts:

app_sa
(per environment) — Runtime identity for the deployed agent. Roles defined in
```
deployment/terraform/iam.tf
```
.
cicd_runner_sa
(CI/CD project) — CI/CD pipeline identity (GitHub Actions / Cloud Build). Lives in the CI/CD project (defaults to prod project), needs permissions in both staging and prod projects.

Check

deployment/terraform/iam.tf

for exact role bindings. Cross-project permissions (Cloud Run service agents, artifact registry access) are also configured there.

Common 403 errors:

"Permission denied on Cloud Run" →
```
cicd_runner_sa
```
missing deployment role in the target project
"Cannot act as service account" → Missing
```
iam.serviceAccountUser
```
binding on
```
app_sa
```
"Secret access denied" →
```
app_sa
```
missing
```
secretmanager.secretAccessor
```
"Artifact Registry read denied" → Cloud Run service agent missing read access in CI/CD project

Required Permissions for CI/CD Setup

roles/secretmanager.admin
granted to the Cloud Build service account (
```
service-<PROJECT_NUMBER>@gcp-sa-cloudbuild.iam.gserviceaccount.com
```
) in the CI/CD project. This allows Cloud Build to access the GitHub token stored in Secret Manager.

Required APIs

The following Google Cloud APIs must be enabled in your project for the skills and deployment to work:

cloudbuild.googleapis.com
— Required for building container images and running CI/CD pipelines.
secretmanager.googleapis.com
— Required for managing secrets and API keys.
run.googleapis.com
— Required for deploying to Cloud Run.

Ensure these are enabled before running deployment or CI/CD setup commands:

bash

gcloud services enable cloudbuild.googleapis.com secretmanager.googleapis.com run.googleapis.com --project=YOUR_PROJECT_ID

Secret Manager (for API Credentials)

Instead of passing sensitive keys as environment variables, use GCP Secret Manager.

bash

# Create a secret
echo -n "YOUR_API_KEY" | gcloud secrets create MY_SECRET_NAME --data-file=-

# Update an existing secret
echo -n "NEW_API_KEY" | gcloud secrets versions add MY_SECRET_NAME --data-file=-

Grant access: For Cloud Run, grant

secretmanager.secretAccessor

app_sa

. For Agent Runtime, grant it to the platform-managed SA (

service-PROJECT_NUMBER@gcp-sa-aiplatform-re.iam.gserviceaccount.com

). For GKE, grant

secretmanager.secretAccessor

app_sa

. Access secrets via Kubernetes Secrets or directly via the Secret Manager API with Workload Identity.

Pass secrets at deploy time (Agent Runtime):

bash

agents-cli deploy --secrets "API_KEY=my-api-key,DB_PASS=db-password:2"

Format:

ENV_VAR=SECRET_ID

ENV_VAR=SECRET_ID:VERSION

(defaults to latest). Access in code via

os.environ.get("API_KEY")

Observability

See the agents-cli-observability skill for observability configuration (Cloud Trace, prompt-response logging, BigQuery Analytics, third-party integrations).

Testing Your Deployed Agent

The quickest way to test a deployed agent is

agents-cli run --url <service-url> --mode <a2a|adk> "your prompt"

— it handles auth, sessions, and streaming automatically (supports Agent Runtime and Cloud Run).

For advanced testing (custom headers, session reuse, scripting, load tests), see

references/testing-deployed-agents.md

Deploying with a UI (IAP)

IAP (Identity-Aware Proxy) secures a Cloud Run service so only authorized Google accounts can access it. Support for IAP deployment via

agents-cli deploy

is planned for a future release.

For Agent Runtime with a custom frontend, use a decoupled deployment — deploy the frontend separately to Cloud Run or Cloud Storage, connecting to the Agent Runtime backend API.

For more information on IAP with Cloud Run, see the Cloud Console IAP settings.

Rollback & Recovery

The primary rollback mechanism is git-based: fix the issue, commit, and push to

main

. The CI/CD pipeline will automatically build and deploy the new version through staging → production.

For immediate Cloud Run rollback without a new commit, use revision traffic shifting:

bash

gcloud run revisions list --service=SERVICE_NAME --region=REGION
gcloud run services update-traffic SERVICE_NAME \
  --to-revisions=REVISION_NAME=100 --region=REGION

Agent Runtime doesn't support revision-based rollback — fix and redeploy via

agents-cli deploy

For GKE rollback, use

kubectl rollout undo

bash

kubectl rollout undo deployment/DEPLOYMENT_NAME -n NAMESPACE
kubectl rollout status deployment/DEPLOYMENT_NAME -n NAMESPACE

Custom Infrastructure (Terraform)

CRITICAL: When your agent requires custom infrastructure (Cloud SQL, Pub/Sub, Eventarc, BigQuery, etc.), you MUST define it in Terraform — never create resources manually via

gcloud

commands. Exception: quick experimentation is fine with

gcloud

or console, but production infrastructure must be in Terraform.

For custom infrastructure patterns, consult

references/terraform-patterns.md

for:

Where to put custom Terraform files (single-project vs CI/CD)
Resource examples (Pub/Sub, BigQuery, Eventarc triggers)
IAM bindings for custom resources
Terraform state management (remote vs local, importing resources)
Common infrastructure patterns

Troubleshooting

Issue	Solution
Terraform state locked	`terraform force-unlock -force LOCK_ID` in deployment/terraform/
GitHub Actions auth failed	Re-run `terraform apply` in CI/CD terraform dir; verify WIF pool/provider
Cloud Build authorization pending	Use `github_actions` runner instead
Resource already exists	`terraform import` (see `references/terraform-patterns.md` )
Agent Runtime deploy timeout / hangs	Deployments take 5-10 min; check if engine was created (see Agent Runtime Specifics)
Secret not available	Verify `secretAccessor` granted to `app_sa` (not the default compute SA)
403 on deploy	Check `deployment/terraform/iam.tf` — `cicd_runner_sa` needs deployment + SA impersonation roles in the target project
403 when testing Cloud Run	Default is `--no-allow-unauthenticated` ; include `Authorization: Bearer $(gcloud auth print-identity-token)` header
Cold starts too slow	Set `min_instance_count > 0` in Cloud Run Terraform config
Cloud Run 503 errors	Check resource limits (memory/CPU), increase `max_instance_count` , or check container crash logs
403 right after granting IAM role	IAM propagation is not instant — wait a couple of minutes before retrying. Don't keep re-granting the same role
Resource seems missing but Terraform created it	Run `terraform state list` to check what Terraform actually manages. Resources created via `null_resource` + `local-exec` (e.g., BQ linked datasets) won't appear in `gcloud` CLI output
Deployment failed or agent not responding	Check Cloud Logging: `gcloud logging read "resource.type=cloud_run_revision AND resource.labels.service_name=SERVICE" --project=PROJECT --limit=50 --format="table(timestamp,severity,textPayload)"` for Cloud Run, or `gcloud logging read "resource.type=aiplatform.googleapis.com/ReasoningEngine" --project=PROJECT --limit=50` for Agent Runtime
Agent returns errors after deploy	Open Cloud Logging in Console → filter by service name (Cloud Run) or reasoning engine resource (Agent Runtime) → look for Python tracebacks or permission errors in recent log entries

Platform Registration

For registering deployed agents with Gemini Enterprise, see

/google-agents-cli-publish

Related Skills

```
/google-agents-cli-workflow
```
— Development workflow, coding guidelines, and operational rules
```
/google-agents-cli-adk-code
```
— ADK Python API quick reference for writing agent code
```
/google-agents-cli-eval
```
— Evaluation methodology, evalset schema, and the eval-fix loop

/google-agents-cli-scaffold

— Project creation and enhancement with

agents-cli scaffold create

scaffold enhance

```
/google-agents-cli-observability
```
— Cloud Trace, logging, BigQuery Analytics, and third-party integrations
```
/google-agents-cli-publish
```
— Gemini Enterprise registration

google-agents-cli-deploy

NPX Install

Tags

SKILL.md Content

ADK Deployment Guide

Reference Files

Deployment Target Decision Matrix

Deploying to Dev

Deploy Workflow

Single-Project Infrastructure Setup (Optional — Advanced)

Deploy Flag Reference

Production Deployment — CI/CD Pipeline

Cloud Run Specifics

Agent Runtime Specifics

GKE Specifics

Service Account Architecture

Required Permissions for CI/CD Setup

Required APIs

Secret Manager (for API Credentials)

Observability

Testing Your Deployed Agent

Deploying with a UI (IAP)

Rollback & Recovery

Custom Infrastructure (Terraform)

Troubleshooting

Platform Registration

Related Skills