adk-deploy-guide

ADK Deployment Guide

Scaffolded project? Use the make commands throughout this guide — they wrap Terraform, Docker, and deployment into a tested pipeline.

No scaffold? See Quick Deploy below, or the ADK deployment docs. For production infrastructure, scaffold with /adk-scaffold.

Reference Files

For deeper details, consult these reference files in references/:

• cloud-run.md — Scaling defaults, Dockerfile, session types, networking
• agent-engine.md — deploy.py CLI, AdkApp pattern, Terraform resource, deployment metadata, CI/CD differences
• terraform-patterns.md — Custom infrastructure, IAM, state management, importing resources
• event-driven.md — Pub/Sub, Eventarc, BigQuery Remote Function triggers via custom fast_api_app.py endpoints

Observability: See the adk-observability-guide 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 Engine	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 Engine session backend	Custom (any Kubernetes-compatible store)
Batch/event processing	Not supported	/invoke endpoint for Pub/Sub, Eventarc, BigQuery	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, event-driven workloads	Full control, open models, GPU workloads

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

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Quick Deploy (ADK CLI)

For projects without Agent Starter Pack scaffolding. 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/

# GKE (requires existing cluster)
adk deploy gke --project=PROJECT --cluster_name=CLUSTER --region=REGION path/to/agent/

All commands support --with_ui to deploy the ADK dev UI. Cloud Run also accepts extra gcloud flags after -- (e.g., -- --no-allow-unauthenticated).

See adk deploy --help or the ADK deployment docs for full flag reference.

For CI/CD, observability, or production infrastructure, scaffold with /adk-scaffold and use the sections below.

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Dev Environment Setup & Deploy (Scaffolded Projects)

Setting Up Dev Infrastructure (Optional)

make setup-dev-env runs terraform apply in deployment/terraform/dev/. This provisions supporting infrastructure:

• Service accounts (app_sa for the agent, used for runtime permissions)
• Artifact Registry repository (for container images)
• IAM bindings (granting the app SA necessary roles)
• Telemetry resources (Cloud Logging bucket, BigQuery dataset)
• Any custom resources defined in deployment/terraform/dev/

This step is optional — make deploy works without it (Cloud Run creates the service on the fly via gcloud run deploy --source .). However, running it gives you proper service accounts, observability, and IAM setup.

make setup-dev-env

Note: make deploy doesn't automatically use the Terraform-created app_sa. Pass --service-account explicitly or update the Makefile.

Deploying

1. Notify the human: "Eval scores meet thresholds and tests pass. Ready to deploy to dev?"
2. Wait for explicit approval
3. Once approved: make deploy

IMPORTANT: Never run make deploy without explicit human approval.

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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 (see /adk-scaffold for full options):

   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 (see references/terraform-patterns.md)

Run uvx agent-starter-pack setup-cicd --help for the full flag reference (Cloud Build options, dev project, region, etc.).

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. The first push after setup-cicd won't trigger staging CD unless you modify something in app/. If nothing happens after pushing, this is why.

3. Production CD — Triggered after successful staging deploy via workflow_run. Might 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, not a custom mechanism.

IMPORTANT: setup-cicd creates infrastructure but doesn't deploy automatically. Terraform configures all required GitHub secrets and variables (WIF credentials, project IDs, service accounts). 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

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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://google.github.io/adk-docs/deploy/cloud-run/index.md.

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Agent Engine Specifics

Agent Engine 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 Engine. Deploy via deploy.py or adk deploy agent_engine. Query via the Python vertexai.Client SDK.

Deployments can take 5-10 minutes. If make deploy times out, check if the engine was created and manually populate deployment_metadata.json with the engine resource ID (see reference for details).

For detailed infrastructure configuration (deploy.py flags, AdkApp pattern, Terraform resource, deployment metadata, session/artifact services, CI/CD differences), see references/agent-engine.md. For ADK docs on Agent Engine deployment, fetch https://google.github.io/adk-docs/deploy/agent-engine/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
• "Artifact Registry read denied" → Cloud Run service agent missing read access in CI/CD project

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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 Engine, grant it to the platform-managed SA (service-PROJECT_NUMBER@gcp-sa-aiplatform-re.iam.gserviceaccount.com).

Pass secrets at deploy time (Agent Engine):

make 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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Observability

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

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Testing Your Deployed Agent

Agent Engine Deployment

Option 1: Testing Notebook

jupyter notebook notebooks/adk_app_testing.ipynb

Option 2: Python Script

import json
import vertexai

with open("deployment_metadata.json") as f:
    engine_id = json.load(f)["remote_agent_engine_id"]

client = vertexai.Client(location="us-central1")
agent = client.agent_engines.get(name=engine_id)

async for event in agent.async_stream_query(message="Hello!", user_id="test"):
    print(event)

Option 3: Playground

make playground

Cloud Run Deployment

Auth required by default. Cloud Run deploys with --no-allow-unauthenticated, so all requests need an Authorization: Bearer header with an identity token. Getting a 403? You're likely missing this header. To allow public access, redeploy with --allow-unauthenticated.

SERVICE_URL="https://SERVICE_NAME-PROJECT_NUMBER.REGION.run.app"
AUTH="Authorization: Bearer $(gcloud auth print-identity-token)"

# Test health endpoint
curl -H "$AUTH" "$SERVICE_URL/"

# Step 1: Create a session (required before sending messages)
curl -X POST "$SERVICE_URL/apps/app/users/test-user/sessions" \
  -H "Content-Type: application/json" \
  -H "$AUTH" \
  -d '{}'
# → returns JSON with "id" — use this as SESSION_ID below

# Step 2: Send a message via SSE streaming
curl -X POST "$SERVICE_URL/run_sse" \
  -H "Content-Type: application/json" \
  -H "$AUTH" \
  -d '{
    "app_name": "app",
    "user_id": "test-user",
    "session_id": "SESSION_ID",
    "new_message": {"role": "user", "parts": [{"text": "Hello!"}]}
  }'

Common mistake: Using {"message": "Hello!", "user_id": "...", "session_id": "..."} returns 422 Field required. The ADK HTTP server expects the new_message / parts schema shown above, and the session must already exist.

Load Tests

make load-test

See tests/load_test/README.md for configuration, default settings, and CI/CD integration details.

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Deploying with a UI (IAP)

To expose your agent with a web UI protected by Google identity authentication:

# Deploy with IAP (built-in framework UI)
make deploy IAP=true

# Deploy with custom frontend on a different port
make deploy IAP=true PORT=5173

IAP (Identity-Aware Proxy) secures the Cloud Run service — only authorized Google accounts can access it. After deploying, grant user access via the Cloud Console IAP settings.

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

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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 Engine doesn't support revision-based rollback — fix and redeplo

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