google-cloud-agent-sdk-master

name

Google Cloud Agent SDK Master

description

Automatic activation for ALL Google Cloud Agent Development Kit (ADK) and Agent Starter Pack operations - multi-agent systems, containerized deployment, RAG agents, and production orchestration. **TRIGGER PHRASES:** - "adk", "agent development kit", "agent starter pack", "multi-agent", "build agent" - "cloud run agent", "gke deployment", "agent engine", "containerized agent" - "rag agent", "react agent", "agent orchestration", "agent templates" **AUTO-INVOKES FOR:** - Agent creation and scaffolding - Multi-agent system design - Containerized agent deployment - RAG (Retrieval-Augmented Generation) implementation - CI/CD pipeline setup for agents - Agent evaluation and monitoring

Google Cloud Agent SDK Master - Production-Ready Agent Systems

This Agent Skill provides comprehensive mastery of Google's Agent Development Kit (ADK) and Agent Starter Pack for building and deploying production-grade containerized agents.

Core Capabilities

🤖 Agent Development Kit (ADK)

Framework Overview:

• Open-source Python framework from Google
• Same framework powering Google Agentspace and CES
• Build production agents in <100 lines of code
• Model-agnostic (optimized for Gemini)
• Deployment-agnostic (local, Cloud Run, GKE, Agent Engine)

Supported Agent Types:

1. LLM Agents: Dynamic routing with intelligence
2. Workflow Agents:
   • Sequential: Linear execution
   • Loop: Iterative processing
   • Parallel: Concurrent execution
3. Custom Agents: User-defined implementations
4. Multi-agent Systems: Hierarchical coordination

Key Features:

• Flexible orchestration (workflow & LLM-driven)
• Tool ecosystem (search, code execution, custom functions)
• Third-party integrations (LangChain, CrewAI)
• Agents-as-tools capability
• Built-in evaluation framework
• Cloud Trace integration

📦 Agent Starter Pack

Production Templates:

1. adk_base - ReAct agent using ADK
2. agentic_rag - Document retrieval + Q&A with search
3. langgraph_base_react - LangGraph ReAct implementation
4. crewai_coding_crew - Multi-agent coding system
5. adk_live - Multimodal RAG (audio/video/text)

Infrastructure Automation:

• CI/CD setup with single command
• GitHub Actions or Cloud Build pipelines
• Multi-environment support (dev, staging, prod)
• Automated testing and evaluation
• Deployment rollback mechanisms

🚀 Deployment Targets

1. Vertex AI Agent Engine

• Fully managed runtime
• Auto-scaling and load balancing
• Built-in observability
• Serverless architecture
• Best for: Production-scale agents

2. Cloud Run

• Containerized serverless
• Pay-per-use pricing
• Custom domain support
• Traffic splitting
• Best for: Web-facing agents

3. Google Kubernetes Engine (GKE)

• Full container orchestration
• Advanced networking
• Resource management
• Multi-cluster support
• Best for: Complex multi-agent systems

4. Local/Docker

• Development and testing
• Custom infrastructure
• On-premises deployment
• Best for: POC and debugging

🔧 Technical Implementation

Installation:

# Agent Starter Pack (recommended)
pip install agent-starter-pack

# or direct from GitHub
uvx agent-starter-pack create my-agent

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

Create Agent (ADK):

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

# Simple ReAct agent
@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)

# Multi-agent orchestration
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

Using Agent Starter Pack:

# Create project with template
uvx agent-starter-pack create my-rag-agent \
    --template agentic_rag \
    --deployment cloud_run

# Generates complete structure:
my-rag-agent/
├── src/
│   ├── agent.py          # Agent implementation
│   ├── tools/            # Custom tools
│   └── config.py         # Configuration
├── deployment/
│   ├── Dockerfile
│   ├── cloudbuild.yaml
│   └── terraform/
├── tests/
│   ├── unit_tests.py
│   └── integration_tests.py
└── .github/workflows/    # CI/CD pipelines

Deploy to Cloud Run:

# Using ADK CLI
adk deploy \
    --target cloud_run \
    --region us-central1 \
    --service-account [e-mail]

# Manual with Docker
docker build -t gcr.io/PROJECT/agent:latest .
docker push gcr.io/PROJECT/agent:latest
gcloud run deploy agent \
    --image gcr.io/PROJECT/agent:latest \
    --region us-central1 \
    --allow-unauthenticated

Deploy to Agent Engine:

# Using Agent Starter Pack
asp deploy \
    --env production \
    --target agent_engine

# Manual deployment
from google.cloud.aiplatform import agent_engines
agent_engines.deploy_agent(
    agent_id="my-agent",
    project="PROJECT_ID",
    location="us-central1"
)

📊 RAG Agent Implementation

Vector Search Integration:

from vertexai.preview.rag import VectorSearchTool
from google.cloud import aiplatform

# Set up vector search
vector_search = VectorSearchTool(
    index_endpoint="projects/PROJECT/locations/LOCATION/indexEndpoints/INDEX_ID",
    deployed_index_id="deployed_index"
)

# RAG agent with ADK
class RAGAgent(ADKAgent):
    def __init__(self):
        super().__init__(
            model="gemini-2.5-pro",
            tools=[vector_search, web_search_tool]
        )

    def run(self, query: str):
        # Retrieves relevant docs automatically
        response = self.generate(
            f"Answer this using retrieved context: {query}"
        )
        return response

Vertex AI Search Integration:

from vertexai.preview.search import VertexAISearchTool

# Enterprise search integration
vertex_search = VertexAISearchTool(
    data_store_id="DATA_STORE_ID",
    project="PROJECT_ID"
)

agent = ADKAgent(
    model="gemini-2.5-pro",
    tools=[vertex_search]
)

🔄 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 Agent
        run: pytest tests/
      - name: Deploy to Cloud Run
        run: |
          gcloud run deploy agent \
            --source . \
            --region us-central1

Cloud Build Pipeline:

steps:
  # Build container
  - name: 'gcr.io/cloud-builders/docker'
    args: ['build', '-t', 'gcr.io/$PROJECT_ID/agent', '.']

  # Run tests
  - name: 'gcr.io/$PROJECT_ID/agent'
    args: ['pytest', 'tests/']

  # Deploy to Cloud Run
  - name: 'gcr.io/cloud-builders/gcloud'
    args:
      - 'run'
      - 'deploy'
      - 'agent'
      - '--image=gcr.io/$PROJECT_ID/agent'
      - '--region=us-central1'

🎯 Multi-Agent Orchestration

Hierarchical Agents:

# Coordinator agent with specialized sub-agents
class ProjectManagerAgent(ADKAgent):
    def __init__(self):
        self.researcher = ResearchAgent()
        self.analyst = AnalysisAgent()
        self.writer = WriterAgent()
        self.reviewer = ReviewAgent()

    def run(self, project_brief: str):
        # Coordinate multiple agents
        research = self.researcher.run(project_brief)
        analysis = self.analyst.run(research)
        draft = self.writer.run(analysis)
        final = self.reviewer.run(draft)
        return final

Parallel Agent Execution:

import asyncio

class ParallelResearchAgent(ADKAgent):
    async def research_topic(self, topics: list[str]):
        # Run multiple agents concurrently
        tasks = [
            self.specialized_agent(topic)
            for topic in topics
        ]
        results = await asyncio.gather(*tasks)
        return self.synthesize(results)

📈 Evaluation & Monitoring

Built-in Evaluation:

from google.cloud.aiplatform import agent_evaluation

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

# Run evaluation
results = agent.evaluate(eval_config)
print(f"Accuracy: {results.accuracy}")
print(f"Relevance: {results.relevance}")

Cloud Trace Integration:

from google.cloud import trace_v1

# Automatic tracing
@traced_agent
class MonitoredAgent(ADKAgent):
    def run(self, query: str):
        # All calls automatically traced
        with self.trace_span("retrieval"):
            docs = self.retrieve(query)

        with self.trace_span("generation"):
            response = self.generate(query, docs)

        return response

🔒 Security & Best Practices

1. Service Account Management:

# Create minimal-permission service account
gcloud iam service-accounts create agent-sa \
    --display-name "Agent Service Account"

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

2. Secret Management:

from google.cloud import secretmanager

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

3. VPC Service Controls:

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

💰 Cost Optimization

Strategies:

• Use Gemini 2.5 Flash for most operations
• Cache embeddings for RAG systems
• Implement request batching
• Use preemptible GKE nodes
• Monitor token usage in Cloud Monitoring

Pricing Examples:

• Cloud Run: $0.00024/GB-second
• Agent Engine: Pay-per-request pricing
• GKE: Standard cluster costs
• Gemini API: $3.50/1M tokens (Pro)

📚 Reference Architecture

Production Agent System:

┌─────────────────┐
│   Load Balancer │
└────────┬────────┘
         │
    ┌────▼────┐
    │Cloud Run│ (Agent containers)
    └────┬────┘
         │
    ┌────▼──────────┐
    │ Agent Engine  │ (Orchestration)
    └────┬──────────┘
         │
    ┌────▼────────────────┐
    │  Vertex AI Search   │ (RAG)
    │  Vector Search      │
    │  Gemini 2.5 Pro     │
    └─────────────────────┘

🎯 Best Practices for Jeremy

1. Start with Templates:

# Use Agent Starter Pack templates
uvx agent-starter-pack create my-agent --template agentic_rag

2. Local Development:

# Test locally first
adk serve --port 8080
curl http://localhost:8080/query -d '{"question": "test"}'

3. Gradual Deployment:

# Deploy to dev → staging → prod
asp deploy --env dev
# Test thoroughly
asp deploy --env staging
# Final production push
asp deploy --env production

4. Monitor Everything:

• Enable Cloud Trace
• Set up error reporting
• Track token usage
• Monitor response times
• Set up alerting

📖 Official Documentation

Core Resources:

• ADK Docs: https://google.github.io/adk-docs/
• Agent Starter Pack: https://github.com/GoogleCloudPlatform/agent-starter-pack
• Agent Engine: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview
• Agent Builder: https://cloud.google.com/products/agent-builder

Tutorials:

• Building AI Agents: https://codelabs.developers.google.com/devsite/codelabs/building-ai-agents-vertexai
• Multi-agent Systems: https://cloud.google.com/blog/products/ai-machine-learning/build-and-manage-multi-system-agents-with-vertex-ai

When This Skill Activates

This skill automatically activates when you mention:

• Agent development, ADK, or Agent Starter Pack
• Multi-agent systems or orchestration
• Containerized agent deployment
• Cloud Run, GKE, or Agent Engine deployment
• RAG agents or ReAct agents
• Agent templates or scaffolding
• CI/CD for agents
• Production agent systems

Integration with Other Services

Google Cloud:

• Vertex AI (Gemini, Search, Vector Search)
• Cloud Storage (data storage)
• Cloud Functions (triggers)
• Cloud Scheduler (automation)
• Cloud Logging & Monitoring

Third-party:

• LangChain integration
• CrewAI orchestration
• Custom tool frameworks

Success Metrics

Track:

• Agent response time (target: <2s)
• Evaluation scores (target: >85% accuracy)
• Deployment frequency (target: daily)
• System uptime (target: 99.9%)
• Cost per query (target: <$0.01)

---

This skill makes Jeremy a Google Cloud agent architecture expert with instant access to ADK, Agent Starter Pack, and production deployment patterns.