adk-deploy-guide
Originally fromeliasecchig/adk-docs
Installation
Summary
Comprehensive deployment guide for ADK agents across Google Cloud platforms with CI/CD, infrastructure, and troubleshooting.
- Covers three deployment targets (Agent Engine, Cloud Run, GKE) with a decision matrix comparing scaling, networking, session state, and cost models
- Includes quick-deploy CLI commands, scaffolded project workflows with
makecommands, and full CI/CD pipeline setup via GitHub Actions or Cloud Build with Workload Identity Federation - Provides platform-specific details for Cloud Run (scaling, Dockerfile, FastAPI endpoints), Agent Engine (source-based deploy, AdkApp pattern), and GKE (Kubernetes resources, Workload Identity)
- Covers service account architecture, Secret Manager integration, observability setup, testing patterns, and IAP-protected UI deployment
- Extensive troubleshooting table addressing Terraform locks, auth failures, timeouts, IAM propagation, and common 403/503 errors
SKILL.md
ADK Deployment Guide
Scaffolded project? Use the
makecommands 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, networkingagent-engine.md— deploy.py CLI, AdkApp pattern, Terraform resource, deployment metadata, CI/CD differencesgke.md— GKE Autopilot cluster, Terraform-managed Kubernetes resources, Workload Identity, session types, networkingterraform-patterns.md— Custom infrastructure, IAM, state management, importing resourcesevent-driven.md— Pub/Sub, Eventarc, BigQuery Remote Function triggers via customfast_api_app.pyendpoints
Observability: See the adk-observability-guide skill for Cloud Trace, prompt-response logging, BigQuery Analytics, and third-party integrations.
Related skills