/google-agents-cli-deploy

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, or awareness of Agent Gateway.
  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: 1.3.1
  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](https://docs.astral.sh/uv/getting-started/installation/index.md) 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 | |----------|-------------|-----------|-----| | **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.

All three targets are container-based, so any language works.

> **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. For a worked example, look up OAuth user consent in the topic index in `/google-agents-cli-adk-code` → `references/samples.md`.

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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 fai