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name: adk-deploy-guide description: "MUST READ before deploying any ADK agent to Google Cloud — covers Cloud Run, Agent Engine, Vertex AI, event-driven agents, and ADK CI/CD pipelines. Use when deploying ADK agents to production, not for agent code patterns (use google-adk) or project scaffolding." allowed-tools: Bash, Read, Write, Edit metadata: triggers: deploy ADK agent, Cloud Run, Agent Engine, Vertex AI deployment, ADK production, ADK CI/CD related-skills: google-adk, agentic-ai-dev, python-dev domain: infrastructure role: specialist scope: deployment output-format: code last-reviewed: "2026-03-15"

ADK Deployment Guide

Scaffolded project? Use make commands — they wrap Terraform, Docker, and deployment.

No scaffold? See Quick Deploy below. For production infrastructure, see the /scaffold-adk command or load the architecture-design skill for infrastructure planning.

Iron Law

NEVER create GCP resources manually via gcloud for production. Define all infrastructure in Terraform. Exception: quick experimentation only — console or gcloud for throwaway resources is fine.

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Reference Files

File	Contents
reference/agent-engine.md	AdkApp pattern, deploy.py CLI, session/artifact services, CI/CD differences
reference/cloud-run.md	Dockerfile, session types, scaling defaults, networking, ingress, IAP
reference/event-driven.md	Pub/Sub, Eventarc, BigQuery Remote Function trigger patterns
reference/terraform-patterns.md	Custom resources, IAM bindings, state management, importing resources

For agent code patterns and testing strategies, see google-adk skill. For evaluations and observability, see adk-eval-guide and adk-observability-guide skills.

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Deployment Target Decision Matrix

Criteria	Agent Engine	Cloud Run
Deployment	Source-based, no Docker	Dockerfile + container image
Scaling	Managed auto-scaling	Fully configurable (min/max instances)
Session state	Native VertexAiSessionService	In-memory (dev), Cloud SQL, or Agent Engine backend
Event-driven	Not supported	Pub/Sub, Eventarc, BigQuery Remote Function via /invoke
Cost model	vCPU-hours + memory-hours (not billed when idle)	Per-instance-second + min instance costs
Setup complexity	Lower — managed, purpose-built for agents	Medium — Dockerfile, Terraform, networking
Best for	Minimal ops, managed infrastructure	Event-driven workloads, full infrastructure control

Ask the user which target fits their needs before proceeding.

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Quick Deploy (ADK CLI)

No Makefile, Terraform, or Dockerfile required.

# Cloud Run
adk deploy cloud_run --project=PROJECT --region=REGION path/to/agent/

# Agent Engine
adk deploy agent_engine --project=PROJECT --region=REGION path/to/agent/

Both support --with_ui to deploy the ADK dev UI. Cloud Run accepts extra gcloud flags after --.

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Process

1. Gather requirements — target (Agent Engine vs Cloud Run), project ID, region, secrets needed
2. Choose target — use the decision matrix above; ask if unclear
3. Scaffold or write Terraform — run /scaffold-adk or manually write deployment/terraform/
4. Configure secrets — use Secret Manager, not env vars, for API keys and credentials
5. Deploy — make deploy (scaffolded) or adk deploy <target> (CLI)
6. Verify — health check endpoint, test with curl or testing notebook, run load tests

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Secret Manager

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

# Update
echo -n "NEW_KEY" | gcloud secrets versions add MY_SECRET_NAME --data-file=-

# Agent Engine: pass at deploy time
make deploy SECRETS="API_KEY=my-api-key,DB_PASS=db-password:2"

Grant secretmanager.secretAccessor to app_sa (Cloud Run) or service-PROJECT_NUMBER@gcp-sa-aiplatform-re.iam.gserviceaccount.com (Agent Engine).

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Documentation Sources

Source	URL	Purpose
ADK Deployment	https://google.github.io/adk-docs/deploy/	Official deployment docs
Cloud Run	https://google.github.io/adk-docs/deploy/cloud-run/index.md	Cloud Run specifics
Agent Engine	https://google.github.io/adk-docs/deploy/agent-engine/index.md	Agent Engine specifics

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Production Deployment — CI/CD Pipeline

Best for: Production applications, teams requiring staging → production promotion.

Prerequisites:

1. Project must NOT be in a gitignored folder
2. User must provide staging and production GCP project IDs
3. GitHub repository name and owner

Steps:

1. If prototype, first add Terraform/CI-CD files using the Agent Starter Pack CLI:

   uvx agent-starter-pack enhance . --cicd-runner github_actions -y -s
   

2. Ensure you're logged in to GitHub CLI:

   gh auth login  # (skip if already authenticated)
   

3. Run setup-cicd:

   uvx agent-starter-pack setup-cicd \
     --staging-project YOUR_STAGING_PROJECT \
     --prod-project YOUR_PROD_PROJECT \
     --repository-name YOUR_REPO_NAME \
     --repository-owner YOUR_GITHUB_USERNAME \
     --auto-approve \
     --create-repository
   

4. Push code to trigger deployments

Key setup-cicd Flags

Flag	Description
--staging-project	GCP project ID for staging environment
--prod-project	GCP project ID for production environment
--repository-name / --repository-owner	GitHub repository name and owner
--auto-approve	Skip Terraform plan confirmation prompts
--create-repository	Create the GitHub repo if it doesn't exist
--cicd-project	Separate GCP project for CI/CD infrastructure. Defaults to prod project
--local-state	Store Terraform state locally instead of in GCS

Choosing a CI/CD Runner

Runner	Pros	Cons
github_actions (Default)	No PAT needed, uses gh auth, WIF-based, fully automated	Requires GitHub CLI authentication
google_cloud_build	Native GCP integration	Requires interactive browser authorization (or PAT + app installation ID for programmatic mode)

How Authentication Works (WIF)

Both runners use Workload Identity Federation (WIF) — GitHub/Cloud Build OIDC tokens are trusted by a GCP Workload Identity Pool, which grants cicd_runner_sa impersonation. No long-lived service account keys needed. Terraform in setup-cicd creates the pool, provider, and SA bindings automatically. If auth fails, re-run terraform apply in the CI/CD Terraform directory.

CI/CD Pipeline Stages

The pipeline has three stages:

1. CI (PR checks) — Triggered on pull request. Runs unit and integration tests.
2. Staging CD — Triggered on merge to main. Builds container, deploys to staging, runs load tests.

   Path filter: Staging CD uses paths: ['app/**'] — it only triggers when files under app/ change. If nothing happens after the first push after setup-cicd, this is why.

3. Production CD — Triggered after successful staging deploy. May require manual approval before deploying to production.

   Approving: Go to GitHub Actions → the production workflow run → click "Review deployments" → approve the pending production environment. This is GitHub's environment protection rules.

IMPORTANT: setup-cicd creates infrastructure but doesn't deploy automatically. Push code to trigger the pipeline:

git add . && git commit -m "Initial agent implementation"
git push origin main

To approve production deployment:

# GitHub Actions: Approve via repository Actions tab (environment protection rules)

# Cloud Build: Find pending build and approve
gcloud builds list --project=PROD_PROJECT --region=REGION --filter="status=PENDING"
gcloud builds approve BUILD_ID --project=PROD_PROJECT

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). Needs permissions in both staging and prod projects.

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Troubleshooting

Issue	Solution
Agent Engine deploy timeout	Check if engine was created; manually populate deployment_metadata.json
403 on deploy	cicd_runner_sa missing deployment role or SA impersonation in target project
403 testing Cloud Run	Add Authorization: Bearer $(gcloud auth print-identity-token) header
Secret access denied	Verify secretAccessor granted to app_sa, not default compute SA
Cold starts slow	Set min_instance_count > 0 in Cloud Run Terraform config
Resource already exists	Use terraform import — see reference/terraform-patterns.md