create-agent

name	create-agent
description	Create a production-ready Google Cloud agent using ADK and Agent Starter Pack with CI/CD, deployment, and testing infrastructure
model	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

1. Choose Agent Template: Select from 5 production templates
2. Configure Deployment: Select deployment target (Cloud Run, GKE, Agent Engine)
3. Generate Project: Create complete project structure with CI/CD
4. 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:

1. Add custom tools for your use case
2. Configure RAG data sources (if using agentic_rag)
3. Set up monitoring alerts
4. Implement evaluation metrics
5. 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

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This command scaffolds production-ready agent projects in <5 minutes with full CI/CD, testing, and deployment automation.