Google Cloud Agent SDK Master - Jeremy's Agent Architecture Powerhouse

Comprehensive Google Cloud Agent Development Kit (ADK) and Agent Starter Pack mastery for building production-grade containerized multi-agent systems.

🎯 Purpose

This plugin makes Claude Code an expert in Google's Agent Development Kit (ADK) and Agent Starter Pack, with automatic activation for agent creation, multi-agent orchestration, containerized deployment, and production CI/CD.

✨ Key Features

🤖 Agent Development Kit (ADK)

• Build production agents in <100 lines of Python
• Same framework powering Google Agentspace
• Model-agnostic (optimized for Gemini)
• Flexible orchestration (workflow & LLM-driven)
• Multi-agent hierarchies

📦 Agent Starter Pack

• 5 production-ready templates
• One-command CI/CD setup
• GitHub Actions or Cloud Build
• Multi-environment deployment
• Automated testing & evaluation

🚀 Deployment Targets

• Cloud Run: Serverless containers
• GKE: Full Kubernetes orchestration
• Agent Engine: Fully managed runtime
• Local/Docker: Development & testing

🧠 Agent Types

• ReAct Agents: Tool use with reasoning
• RAG Agents: Document retrieval & Q&A
• Multi-Agent Systems: Hierarchical coordination
• Workflow Agents: Sequential, parallel, loop
• Custom Agents: User-defined implementations

🚀 Installation

# Install the plugin
/plugin install 004-jeremy-google-cloud-agent-sdk@claude-code-plugins-plus

📋 Components

Agent Skills (1)

• agent-sdk-master - Auto-activates for all ADK and agent operations

Slash Commands (1)

• /create-agent - Scaffold production-ready agent projects

💡 Usage Examples

Create RAG Agent

/create-agent

Agent name: docs-qa-agent
Template: agentic_rag
Deployment: cloud_run
Project: my-project
Region: us-central1

Generates:

• Complete agent project structure
• CI/CD pipelines (GitHub Actions + Cloud Build)
• Dockerfile and deployment configs
• Unit and integration tests
• README and documentation

Build Multi-Agent System

"Build a multi-agent system with 3 specialized agents: researcher, analyst, and writer"

Auto-activates skill and creates:

• Orchestrator agent architecture
• Specialized sub-agent implementations
• Inter-agent communication protocols
• Deployment configuration
• Evaluation framework

Deploy to Production

"Deploy this agent to Cloud Run with auto-scaling and monitoring"

Auto-generates:

• Cloud Run deployment manifest
• Terraform infrastructure code
• Monitoring dashboards
• Alert policies
• Deployment scripts

🔧 Technical Implementation

Prerequisites

# Install Agent Starter Pack
pip install agent-starter-pack

# Or use ADK directly
pip install google-cloud-aiplatform[adk,agent_engines]>=1.111

# Authenticate
gcloud auth application-default login

Create Agent (Quick Start)

# Using Agent Starter Pack (recommended)
uvx agent-starter-pack create my-agent \
    --template adk_base \
    --deployment cloud_run

# Navigate and deploy
cd my-agent
adk deploy --target cloud_run --region us-central1

Code Example (ADK)

from google.cloud.aiplatform import agent
from vertexai.preview.agents import ADKAgent

@agent.adk_agent
class MyAgent(ADKAgent):
    def __init__(self):
        super().__init__(
            model="gemini-2.5-pro",
            tools=[search_tool, code_exec_tool]
        )

    def run(self, query: str):
        return self.generate(query)

🎯 Available Templates

1. adk_base

Type: ReAct agent using ADK Best for: General-purpose agents with tool use Includes: Search, code execution, custom tools

2. agentic_rag

Type: Document retrieval + Q&A Best for: Knowledge bases, customer support Includes: Vertex AI Search, Vector Search

3. langgraph_base_react

Type: LangGraph orchestration Best for: Complex workflows with state Includes: State management, conditional logic

4. crewai_coding_crew

Type: Multi-agent collaboration Best for: Software development, research Includes: Role-based agents, task delegation

5. adk_live

Type: Multimodal RAG Best for: Video/audio processing Includes: Streaming support, multimodal understanding

🚀 Deployment Options

Cloud Run (Serverless)

• Scaling: 0→N automatic
• Pricing: Pay-per-use
• Timeout: 60 minutes
• Memory: Up to 8GB

Deploy:

adk deploy --target cloud_run --region us-central1

Agent Engine (Managed)

• Runtime: Fully managed
• Scaling: Automatic
• Observability: Built-in
• Integration: Native Vertex AI

Deploy:

asp deploy --env production --target agent_engine

GKE (Kubernetes)

• Control: Full orchestration
• Scaling: Advanced policies
• Networking: Custom configuration
• Resources: Flexible allocation

Deploy:

kubectl apply -f deployment/k8s/

📊 Multi-Agent Orchestration

Hierarchical Agents

class OrchestratorAgent(ADKAgent):
    def __init__(self):
        self.research_agent = ResearchAgent()
        self.analysis_agent = AnalysisAgent()
        self.writer_agent = WriterAgent()

    def run(self, task: str):
        research = self.research_agent.run(task)
        analysis = self.analysis_agent.run(research)
        output = self.writer_agent.run(analysis)
        return output

Parallel Execution

import asyncio

class ParallelAgent(ADKAgent):
    async def run_parallel(self, tasks: list[str]):
        results = await asyncio.gather(*[
            self.specialized_agent(task)
            for task in tasks
        ])
        return self.synthesize(results)

💰 Cost Optimization

Pricing Breakdown:

• Cloud Run: $0.00024/GB-second (scales to zero)
• Agent Engine: Pay-per-request
• Gemini 2.5 Pro: $3.50/1M input tokens
• Gemini 2.5 Flash: $0.35/1M input tokens

Optimization Tips:

• Use Flash for routine operations
• Cache embeddings for RAG
• Implement request batching
• Monitor token usage
• Set up budget alerts

Typical Monthly Costs:

• Small agent: $50-100
• Medium agent: $200-500
• Large multi-agent: $1000-2000

🔒 Security Best Practices

Service Accounts

# Create minimal-permission SA
gcloud iam service-accounts create agent-sa

# Grant required permissions
gcloud projects add-iam-policy-binding PROJECT_ID \
    --member="serviceAccount:[email protected]" \
    --role="roles/aiplatform.user"

Secret Management

from google.cloud import secretmanager

client = secretmanager.SecretManagerServiceClient()
name = "projects/PROJECT/secrets/api-key/versions/latest"
response = client.access_secret_version(name=name)
api_key = response.payload.data.decode('UTF-8')

VPC Service Controls

# Enable VPC SC
gcloud access-context-manager perimeters create agent-perimeter \
    --resources=projects/PROJECT_ID \
    --restricted-services=aiplatform.googleapis.com

📈 Monitoring & Evaluation

Built-in Evaluation

from google.cloud.aiplatform import agent_evaluation

eval_config = agent_evaluation.EvaluationConfig(
    metrics=["accuracy", "relevance", "safety"],
    test_dataset="gs://bucket/eval_data.jsonl"
)

results = agent.evaluate(eval_config)

Cloud Trace Integration

@traced_agent
class MonitoredAgent(ADKAgent):
    def run(self, query: str):
        with self.trace_span("retrieval"):
            docs = self.retrieve(query)
        with self.trace_span("generation"):
            response = self.generate(query, docs)
        return response

Monitoring Dashboard

# Create dashboard
gcloud monitoring dashboards create \
    --config-from-file monitoring/dashboard.json

🔄 CI/CD Automation

GitHub Actions (Auto-Generated)

name: Deploy Agent
on:
  push:
    branches: [main]
jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Test
        run: pytest tests/
      - name: Deploy
        run: adk deploy --target cloud_run

Cloud Build Pipeline

steps:
  - name: 'gcr.io/cloud-builders/docker'
    args: ['build', '-t', 'gcr.io/$PROJECT_ID/agent', '.']
  - name: 'gcr.io/cloud-builders/gcloud'
    args: ['run', 'deploy', 'agent', '--image=gcr.io/$PROJECT_ID/agent']

🎯 Use Cases

1. Customer Support Agent

• RAG over documentation
• Auto-respond to tickets
• Escalation routing

2. Research Assistant

• Multi-source information gathering
• Synthesis and summarization
• Citation tracking

3. Code Review Agent

• Analyze pull requests
• Suggest improvements
• Security scanning

4. Content Creation Crew

• Research → Write → Edit pipeline
• Multi-agent collaboration
• Quality assurance

📚 Documentation

Official Resources:

• ADK Documentation
• Agent Starter Pack GitHub
• Agent Engine Overview
• Agent Builder

Tutorials:

• Building AI Agents Codelab
• Multi-Agent Systems Blog

🎓 Training Resources

Learn:

• Agent architecture patterns
• Multi-agent orchestration
• RAG implementation
• Production deployment
• Monitoring & evaluation

🎯 When This Activates

Trigger phrases:

• "adk", "agent development kit"
• "agent starter pack", "build agent"
• "multi-agent", "orchestration"
• "cloud run deployment", "agent engine"
• "rag agent", "react agent"

📈 Roadmap

Planned features:

• Gemini 2.5 Pro integration
• Advanced multi-agent patterns
• Real-time streaming agents
• Agentic AI frameworks
• Enterprise templates

Part of Claude Code Plugins - 234 production-ready plugins

Author: Jeremy Longshore | License: MIT | Version: 1.0.0