google-agents-cli-deploy

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name: google-agents-cli-deploy description: > 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). metadata: author: Google license: Apache-2.0 version: 0.6.1 requires: bins: - agents-cli install: "uv tool install google-agents-cli"

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 — container-based deploy, unified FastAPI app, the /api passthrough, 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.

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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-I supported (private VPC connectivity via network attachments)	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	Trigger endpoints reachable via the Agent Engine /api passthrough	Native trigger endpoints (Pub/Sub, Eventarc); see /google-agents-cli-adk-code	Custom (Kubernetes Jobs, Pub/Sub)
Cost model	vCPU-hours + memory-hours (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, full networking control	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: ADK's trigger_sources registers /apps/{app}/trigger/* endpoints on the same FastAPI app for all targets. On Cloud Run / GKE these are public HTTP routes you point a Pub/Sub push subscription or Eventarc trigger at; on Agent Runtime the same routes are reachable through the Agent Engine /api passthrough (e.g. .../reasoningEngines/v1/{resource}/api/apps/{app}/trigger/pubsub). Cloud Run remains the simplest target for unauthenticated trigger sources. See /google-agents-cli-adk-code (references/adk-python.md, section "12. Event-Driven / Ambient Agents") 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 1.

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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.

1. If prototype (no deployment target), first enhance: agents-cli scaffold enhance . --deployment-target <target>
2. Notify the human: "Eval scores meet thresholds and tests pass. Ready to deploy to dev?"
3. Wait for explicit approval
4. 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 in 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.

# 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 explicitly: agents-cli deploy --service-account SA_EMAIL.

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
--service-name	Override the deployed service name (Cloud Run service or Agent Runtime display name); defaults to the project name. If you override it, consider updating your Terraform and CI (if present) — they name resources from the project name. Not supported for GKE, whose names are fully owned by Terraform.	Agent Runtime, Cloud Run
--secrets	Comma-separated ENV=SECRET or ENV=SECRET:VERSION pairs	Agent Runtime, Cloud Run
--update-env-vars	Comma-separated KEY=VALUE environment variables	Agent Runtime, Cloud Run
--agent-identity	Enable agent identity (Preview)	Agent Runtime
--network-attachment	Network attachment resource name for PSC interface (enables private VPC connectivity)	Agent Runtime
--dns-peering-domain	DNS peering domain suffix, e.g. my-internal.corp. (requires --network-attachment)	Agent Runtime
--dns-peering-project	Project ID hosting the Cloud DNS managed zone for DNS peering (requires --network-attachment)	Agent Runtime
--dns-peering-network	VPC network name in the target project for DNS peering (requires --network-attachment)	Agent Runtime
--memory	Memory limit (default: 4Gi)	Agent Runtime, Cloud Run
--cpu	CPU limit (default: 1)	Agent Runtime, Cloud Run
--min-instances	Minimum number of instances (default: 1)	Agent Runtime, Cloud Run
--max-instances	Maximum number of instances (default: 10)	Agent Runtime, Cloud Run
--concurrency	Concurrent requests per container (default: 8; see Sizing a deployment)	Agent Runtime, Cloud Run
--num-workers	Worker processes per container (default: 1)	Agent Runtime
--port	Container port	Cloud Run, Agent Runtime
--build-args	Comma-separated KEY=VALUE Docker build args	Agent Runtime
--iap	Enable Identity-Aware Proxy	Cloud Run
--image	Container image URI (skips source build; not supported for Agent Runtime)	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.

Advanced Cloud Run Deploys: If you need features not exposed via agents-cli flags, use --dry-run (or -n) to print the full gcloud command, copy it, and add additional arguments as needed.

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.

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Sizing a deployment

Defaults (same on Agent Runtime, Cloud Run, and the generated service.tf): --cpu 1, --memory 4Gi, --num-workers 1, --concurrency 8, --min-instances 1, --max-instances 10.

The params are coupled — scale them together:

• Workers = vCPUs. Each worker is one GIL-bound process that saturates one core, so raise --num-workers with --cpu (e.g. --cpu 4 → --num-workers 4) or you pay for idle cores.
• Memory bounds concurrency. Each concurrent request keeps its full working set (context window, history, RAG chunks, response buffer) in memory while it waits on the model, so peak ≈ base + concurrency × per-request memory. Memory — not CPU — is the first limit, so raising --concurrency without --memory is the main OOM cause.
• Concurrency default is conservative. An async worker can serve many concurrent requests while it waits on the model, but per-request memory is agent-specific, so 8 protects a memory-heavy (RAG/multimodal) agent. Light agents can raise it to 16–32+ after load-testing. See Underutilized asynchronous workers.

# 4x throughput: scale every param, not just one
agents-cli deploy --cpu 4 --num-workers 4 --concurrency 16 --memory 16Gi

Tune with the scaffolded load test (tests/load_test/, run locally or in the CI/CD staging pipeline): drive load, watch max latency and memory/OOM restarts, then adjust — high max latency → raise concurrency (+ workers/cpu); OOM → raise memory or lower concurrency.

--num-workers is Agent-Runtime-only (Cloud Run runs one uvicorn process). On GKE these flags are rejected — size via the Terraform manifests + HorizontalPodAutoscaler under deployment/terraform/.

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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.

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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 (references/adk-python.md, section "12. Event-Driven / Ambient Agents") for the trigger_sources pattern.

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Agent Runtime Specifics

Agent Runtime is a managed Vertex AI service for deploying Python ADK agents. Uses container-based deployment: agents-cli deploy packages your project and Agent Engine builds the image from your project's Dockerfile (required) — the same fast_api_app:app image that serves Cloud Run and GKE.

No gcloud CLI exists for Agent Runtime. Deploy via agents-cli deploy. 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:

# 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 (container deploy flow, the unified FastAPI app and /api passthrough, 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-runtime/index.md.

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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.

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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
• "Cloud SQL connection failed / Not authorized" → Runtime service account missing roles/cloudsql.client
• "Artifact Registry read denied" → Cloud Run service agent missing read access in CI/CD project

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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.

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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:

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

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Secret Manager (for API Credentials)

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

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

Pass secrets at deploy time (Agent Runtime, Cloud Run):

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

Format: ENV_VAR=SECRET_ID or ENV_VAR=SECRET_ID:VERSION (defaults to latest). Access in code via os.environ.get("API_KEY").

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Cloud SQL Permissions (Manual Deployment)

When using Cloud SQL with Cloud Run in a manual deployment (e.g., adding --add-cloudsql-instances in non-Terraform setups), you must manually grant the Cloud SQL Client role to the runtime service account.

Without this, the deployment may succeed but fail at runtime with cloudsql.instances.get authorization errors.

gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
  --member="serviceAccount:YOUR_RUNTIME_SA_EMAIL" \
  --role="roles/cloudsql.client"

Note: In full Terraform-managed setups (infra cicd / infra single-project), this role is configured and managed automatically.

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Observability

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

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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.

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Deploying with a UI (IAP)

IAP (Identity-Aware Proxy) secures a Cloud Run service so only authorized Google accounts can access it. Enable it by adding the --iap flag when deploying (Cloud Run only): agents-cli deploy --iap.

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.

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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:

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:

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

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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

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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)
Cloud SQL connection failed / 403	Grant roles/cloudsql.client to the runtime service account when using manual deployments
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

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Platform Registration

For registering deployed agents with Gemini Enterprise, see /google-agents-cli-publish.

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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, dataset 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