From adk-deployment
Use this skill to deploy an ADK 2.0 agent to Vertex AI Agent Engine — Google's managed agent runtime. Triggers on: "deploy ADK to Vertex", "ADK Agent Engine", "Vertex agent runtime", "managed ADK deployment", "ADK Agent Engine deploy", "Reasoning Engine ADK", "Vertex agent hosting". Generates the deployment script using vertexai.agent_engines with proper artifacts, IAM, and the URL clients use to reach the hosted agent.
How this skill is triggered — by the user, by Claude, or both
Slash command
/adk-deployment:vertex-agent-engine-deployerThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Deploy ADK 2.0 agents to **Vertex AI Agent Engine** — Google's managed runtime. No containers, no Kubernetes — Vertex hosts and scales it.
Deploy ADK 2.0 agents to Vertex AI Agent Engine — Google's managed runtime. No containers, no Kubernetes — Vertex hosts and scales it.
# deploy.py
import vertexai
from vertexai.preview import agent_engines
from agent import root_agent
PROJECT = "my-project"
LOCATION = "us-central1"
STAGING_BUCKET = "gs://my-adk-staging"
vertexai.init(project=PROJECT, location=LOCATION, staging_bucket=STAGING_BUCKET)
remote_agent = agent_engines.create(
root_agent,
requirements=[
"google-adk[extensions] >= 2.0.0",
],
display_name="my_adk_agent",
description="ADK 2.0 agent deployed via Agent Engine.",
extra_packages=["./agent.py"],
)
print(f"Resource name: {remote_agent.resource_name}")
# e.g. projects/.../locations/us-central1/reasoningEngines/1234567890
Run:
pip install google-cloud-aiplatform[agent_engines] vertexai
python deploy.py
from vertexai.preview import agent_engines
remote_agent = agent_engines.get(
"projects/PROJECT/locations/us-central1/reasoningEngines/1234567890"
)
session = remote_agent.create_session(user_id="u1")
response = remote_agent.run(session=session, input="Hello")
print(response)
remote_agent = agent_engines.get(RESOURCE_NAME)
remote_agent.update(
agent=updated_root_agent,
requirements=[...],
)
The Agent Engine runtime needs:
roles/aiplatform.user for itself (auto-assigned)For caller authentication:
remote_agent.run(
session=session,
input="...",
auth_token=user_jwt, # propagated to tools
)
Agent Engine bills for:
Cheaper than always-on GKE for low-volume; comparable to Cloud Run.
for a in agent_engines.list():
print(a.resource_name, a.display_name)
agent_engines.delete(RESOURCE_NAME)
agent_engines.list() shows the new deploymentrun() call returns a non-empty responsecloud-run-deployer for full container controlgke-deployer for max control / specialized hardwarenpx claudepluginhub healthcare-ai-consulting-llc/adk-2-toolkit --plugin adk-deploymentProvides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
Fetches up-to-date documentation from Context7 for libraries and frameworks like React, Next.js, Prisma. Use for setup questions, API references, and code examples.