8.4 KiB
8.4 KiB
name, description, model
| name | description | model |
|---|---|---|
| create-agent | Create a production-ready Google Cloud agent using ADK and Agent Starter Pack with CI/CD, deployment, and testing infrastructure | sonnet |
Create Production-Ready Google Cloud Agent
Scaffold a complete agent project using Google's Agent Development Kit (ADK) and Agent Starter Pack with production-ready infrastructure.
What This Does
- Choose Agent Template: Select from 5 production templates
- Configure Deployment: Select deployment target (Cloud Run, GKE, Agent Engine)
- Generate Project: Create complete project structure with CI/CD
- Setup Instructions: Provide step-by-step deployment guide
Available Templates
1. adk_base (ReAct Agent)
Best for: General-purpose agents with tool use Includes: Search, code execution, custom tools Use case: Q&A agents, research assistants, task automation
2. agentic_rag (RAG Agent)
Best for: Document-based Q&A Includes: Vertex AI Search, Vector Search integration Use case: Knowledge bases, documentation agents, customer support
3. langgraph_base_react (LangGraph)
Best for: Complex workflows with state management Includes: LangGraph orchestration, custom nodes Use case: Multi-step processes, conditional logic, state tracking
4. crewai_coding_crew (Multi-Agent)
Best for: Collaborative multi-agent systems Includes: Specialized agents, role-based coordination Use case: Software development, research teams, content creation
5. adk_live (Multimodal RAG)
Best for: Audio/video/text processing Includes: Multimodal understanding, live streaming Use case: Video analysis, audio transcription, media processing
Deployment Targets
Cloud Run (Serverless)
Pros:
- Automatic scaling 0→N
- Pay-per-use pricing
- Fast deployment
- Custom domains
Cons:
- 60-minute timeout
- Limited memory (8GB max)
Best for: Web-facing agents, APIs, low-traffic services
Agent Engine (Managed)
Pros:
- Fully managed runtime
- Built-in observability
- Auto-scaling
- Integrated with Vertex AI
Cons:
- Vertex AI pricing
- Less customization
Best for: Production agents, high-scale deployment
GKE (Kubernetes)
Pros:
- Full control
- Advanced networking
- Resource management
- Multi-cluster
Cons:
- Higher complexity
- Cluster management overhead
Best for: Complex multi-agent systems, enterprise deployment
Usage
/create-agent
Then provide:
- Agent name
- Template choice
- Deployment target
- GCP project ID
- Region preference
Example Workflow
Input:
Agent name: customer-support-agent
Template: agentic_rag
Deployment: cloud_run
Project: my-gcp-project
Region: us-central1
Generated Structure:
customer-support-agent/
├── src/
│ ├── agent.py # Main agent implementation
│ ├── tools/
│ │ ├── search_tool.py
│ │ └── custom_tools.py
│ ├── config.py # Configuration
│ └── prompts/
│ └── system_prompt.txt
├── deployment/
│ ├── Dockerfile
│ ├── cloudbuild.yaml
│ ├── cloud-run.yaml
│ └── terraform/
│ ├── main.tf
│ ├── variables.tf
│ └── outputs.tf
├── tests/
│ ├── unit/
│ │ ├── test_agent.py
│ │ └── test_tools.py
│ └── integration/
│ └── test_e2e.py
├── .github/workflows/
│ ├── test.yaml # CI testing
│ └── deploy.yaml # CD deployment
├── requirements.txt
├── pyproject.toml
├── README.md
└── .env.example
Step-by-Step Deployment
1. Install Dependencies
cd customer-support-agent
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
2. Configure GCP
# Authenticate
gcloud auth login
gcloud config set project my-gcp-project
# Enable APIs
gcloud services enable \
aiplatform.googleapis.com \
run.googleapis.com \
cloudbuild.googleapis.com
3. Set Up Environment
# Copy example env
cp .env.example .env
# Edit with your values
vim .env
Required variables:
GOOGLE_CLOUD_PROJECT=my-gcp-project
GOOGLE_CLOUD_REGION=us-central1
GOOGLE_APPLICATION_CREDENTIALS=/path/to/key.json
VERTEX_AI_SEARCH_DATASTORE=datastore-id
4. Test Locally
# Run agent locally
python src/agent.py
# Or use ADK CLI
adk serve --port 8080
# Test endpoint
curl http://localhost:8080/query \
-H "Content-Type: application/json" \
-d '{"question": "What are your support hours?"}'
5. Run Tests
# Unit tests
pytest tests/unit/
# Integration tests
pytest tests/integration/
# Coverage report
pytest --cov=src tests/
6. Deploy to Cloud Run
# Using ADK CLI (recommended)
adk deploy \
--target cloud_run \
--region us-central1 \
--allow-unauthenticated
# Or using gcloud
gcloud run deploy customer-support-agent \
--source . \
--region us-central1 \
--allow-unauthenticated \
--memory 2Gi \
--cpu 2 \
--timeout 300s
7. Setup CI/CD
# Connect GitHub repo
gh repo create customer-support-agent --public
git init
git add .
git commit -m "Initial agent setup"
git branch -M main
git remote add origin https://github.com/USER/customer-support-agent.git
git push -u origin main
# GitHub Actions automatically trigger on push
8. Monitor Deployment
# View logs
gcloud run services logs read customer-support-agent \
--region us-central1 \
--limit 100 \
--format json
# Check metrics
gcloud monitoring dashboards create \
--config-from-file monitoring/dashboard.json
Advanced Features
RAG Integration
# Automatically included in agentic_rag template
from vertexai.preview.rag import VectorSearchTool
vector_search = VectorSearchTool(
index_endpoint="projects/PROJECT/locations/REGION/indexEndpoints/INDEX",
deployed_index_id="deployed_index"
)
agent.add_tool(vector_search)
Multi-Agent Orchestration
# Automatically included in crewai_coding_crew template
from crewai import Agent, Task, Crew
researcher = Agent(
role="Researcher",
goal="Research technical topics",
tools=[search_tool]
)
writer = Agent(
role="Writer",
goal="Write documentation",
tools=[write_tool]
)
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_task]
)
Custom Tools
# Add custom tools to any agent
from vertexai.preview.agents import FunctionTool
@FunctionTool
def check_inventory(product_id: str) -> dict:
"""Check product inventory levels"""
# Your custom logic
return {"in_stock": True, "quantity": 42}
agent.add_tool(check_inventory)
Cost Estimation
Development:
- Local testing: Free
- CI/CD (GitHub Actions): Free (2000 min/month)
Production (Cloud Run):
- Idle: $0 (scales to zero)
- Active: ~$0.10/hour at moderate load
- Gemini API: $3.50/1M tokens
Monthly estimate for typical agent:
- Infrastructure: $50-100
- AI API costs: $100-300
- Total: $150-400/month
Troubleshooting
Common Issues
1. Authentication Errors
# Fix: Set credentials
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/key.json
gcloud auth application-default login
2. Timeout Errors
# Fix: Increase Cloud Run timeout
gcloud run services update customer-support-agent \
--timeout 300s
3. Memory Issues
# Fix: Increase memory
gcloud run services update customer-support-agent \
--memory 4Gi
4. Rate Limiting
# Fix: Implement exponential backoff
# Code automatically included in templates
Next Steps
After deployment:
- Add custom tools for your use case
- Configure RAG data sources (if using agentic_rag)
- Set up monitoring alerts
- Implement evaluation metrics
- Scale based on traffic
Resources
Documentation:
- ADK Quickstart: https://google.github.io/adk-docs/
- Agent Starter Pack: https://github.com/GoogleCloudPlatform/agent-starter-pack
- Cloud Run Docs: https://cloud.google.com/run/docs
Examples:
- Agent Gallery: https://cloud.google.com/vertex-ai/generative-ai/docs/samples
- GitHub Samples: https://github.com/GoogleCloudPlatform/generative-ai
This command scaffolds production-ready agent projects in <5 minutes with full CI/CD, testing, and deployment automation.