Session 7: ADK Enterprise Agent Development - Google's Production-Grade AI Platform¶

from google_adk import Agent, Tool, Memory
from google_adk.models import VertexAI
from google_adk.integrations import CloudStorage, BigQuery

# Enterprise-grade agent setup

class EnterpriseAgent:
    def __init__(self, project_id: str):
        self.project_id = project_id

        # Initialize with Google Cloud integration
        self.agent = Agent(
            name="enterprise_assistant",
            model=VertexAI(
                model_name="gemini-pro",
                project_id=project_id
            ),
            memory=Memory.cloud_memory(project_id=project_id),
            tools=[],  # Add tools as needed
            security_config={
                "authentication_required": True,
                "audit_logging": True
            }
        )

    def run(self, query: str) -> str:
        """Execute agent with enterprise security"""
        return self.agent.process(query)

# Install ADK (example - actual installation may vary)

# pip install google-agent-development-kit

import os
from google_adk.auth import authenticate

def setup_adk_environment():
    """Set up ADK with proper authentication"""

    # Authenticate with Google Cloud
    os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/service-account.json"

    # Verify authentication
    auth_status = authenticate()
    if auth_status.success:
        print("✅ ADK authentication successful")
        return True
    else:
        print("❌ ADK authentication failed")
        return False

# Setup

if setup_adk_environment():
    print("Ready to create ADK agents!")

# ADK configuration

adk_config = {
    "project_id": "your-gcp-project",
    "region": "us-central1",
    "model_config": {
        "model_name": "gemini-pro",
        "temperature": 0.7,
        "max_tokens": 1000
    },
    "security_config": {
        "enable_audit_logs": True,
        "require_authentication": True,
        "data_encryption": True
    }
}

from google_adk import Agent, ReasoningEngine
from google_adk.tools import WebSearch, Calculator

class ReasoningAgent:
    """ADK agent with reasoning capabilities"""

    def __init__(self, project_id: str):
        # Create reasoning-enabled agent
        self.agent = Agent(
            name="reasoning_specialist",
            project_id=project_id,

            # Enable reasoning engine
            reasoning_engine=ReasoningEngine(
                strategy="chain_of_thought",
                max_steps=5
            ),

            # Add enterprise tools
            tools=[
                WebSearch(project_id=project_id),
                Calculator()
            ],

            # System configuration
            system_prompt="""You are an enterprise reasoning agent.
            Break down complex problems step-by-step.
            Use available tools to gather information.
            Provide clear, actionable insights."""
        )

    def solve_problem(self, problem: str) -> dict:
        """Solve complex problems using reasoning"""

        response = self.agent.process(
            query=problem,
            enable_reasoning=True,
            return_reasoning_steps=True
        )

        return {
            "solution": response.content,
            "reasoning_steps": response.reasoning_steps,
            "tools_used": response.tools_used,
            "confidence": response.confidence_score
        }

# Usage

agent = ReasoningAgent("your-project-id")
result = agent.solve_problem("Calculate the ROI of implementing AI agents")

from google_adk import MultiModalAgent
from google_adk.inputs import TextInput, ImageInput, AudioInput

class MultiModalAssistant:
    """ADK agent handling multiple input types"""

    def __init__(self, project_id: str):
        self.agent = MultiModalAgent(
            name="multimodal_assistant",
            project_id=project_id,

            # Support multiple input types
            supported_inputs=[
                TextInput(),
                ImageInput(max_size="10MB"),
                AudioInput(format="wav")
            ],

            # Model configuration for multimodal
            model_config={
                "model_name": "gemini-pro-vision",
                "capabilities": ["text", "image", "audio"]
            }
        )

    def process_mixed_input(self, inputs: list) -> dict:
        """Process combination of text, image, and audio"""

        # ADK handles multimodal processing automatically
        response = self.agent.process_multimodal(inputs)

        return {
            "analysis": response.content,
            "input_types_processed": response.input_types,
            "processing_time": response.processing_time
        }

# Example usage

assistant = MultiModalAssistant("your-project-id")
result = assistant.process_mixed_input([
    {"type": "text", "content": "Analyze this image"},
    {"type": "image", "path": "path/to/image.jpg"}
])

import unittest
from google_adk.testing import ADKTestCase

class TestADKAgent(ADKTestCase):
    """Test ADK agent functionality"""

    def setUp(self):
        """Set up test environment"""
        self.project_id = "test-project"
        self.agent = ReasoningAgent(self.project_id)

    def test_basic_reasoning(self):
        """Test basic reasoning capabilities"""
        problem = "What is 15% of 240?"
        result = self.agent.solve_problem(problem)

        # Verify response structure
        self.assertIn("solution", result)
        self.assertIn("reasoning_steps", result)
        self.assertTrue(len(result["reasoning_steps"]) > 0)

        # Verify correct calculation
        self.assertIn("36", result["solution"])

    def test_tool_integration(self):
        """Test tool usage"""
        problem = "Find the current weather in New York"
        result = self.agent.solve_problem(problem)

        # Verify tools were used
        self.assertTrue(len(result["tools_used"]) > 0)
        self.assertIn("WebSearch", str(result["tools_used"]))

    def test_error_handling(self):
        """Test error handling"""
        with self.assertRaises(ValueError):
            self.agent.solve_problem("")  # Empty query should raise error

# Run tests

if __name__ == "__main__":
    unittest.main()

# Performance optimization for ADK

def create_optimized_agent(project_id: str):
    """Create performance-optimized ADK agent"""

    return Agent(
        name="optimized_agent",
        project_id=project_id,

        # Performance settings
        model_config={
            "model_name": "gemini-pro",
            "temperature": 0.3,  # Lower for consistency
            "max_tokens": 500,   # Limit for speed
        },

        # Caching configuration
        cache_config={
            "enable_response_cache": True,
            "cache_duration": 3600,  # 1 hour
            "cache_backend": "cloud_memcache"
        },

        # Connection pooling
        connection_config={
            "pool_size": 10,
            "max_retries": 3,
            "timeout": 30
        }
    )

from google_adk.integrations import BigQuery, CloudStorage, Firestore
from google_adk import Agent

class CloudIntegratedAgent:
    """ADK agent with full Google Cloud integration"""

    def __init__(self, project_id: str):
        self.project_id = project_id

        # Initialize cloud integrations
        self.bigquery = BigQuery(project_id=project_id)
        self.storage = CloudStorage(project_id=project_id)
        self.firestore = Firestore(project_id=project_id)

        # Create agent with cloud tools
        self.agent = Agent(
            name="cloud_agent",
            project_id=project_id,
            tools=[
                self.bigquery.as_tool(),
                self.storage.as_tool(),
                self.firestore.as_tool()
            ]
        )

    def analyze_stored_data(self, dataset_name: str) -> dict:
        """Analyze data stored in BigQuery"""

        query = f"""
        Use BigQuery to analyze the {dataset_name} dataset.
        Provide insights on trends and patterns.
        """

        response = self.agent.process(query)

        # Store results in Firestore
        self.firestore.collection("analysis_results").add({
            "dataset": dataset_name,
            "analysis": response.content,
            "timestamp": "current_time"
        })

        return {
            "analysis": response.content,
            "stored_in_firestore": True
        }

# Usage

cloud_agent = CloudIntegratedAgent("your-project-id")
result = cloud_agent.analyze_stored_data("sales_data")

from google_adk.auth import ServiceAccountAuth, IAMAuth
from google_adk.security import AuditLogger, DataEncryption

class SecureEnterpriseAgent:
    """Enterprise agent with comprehensive security"""

    def __init__(self, project_id: str, service_account_path: str):
        # Set up authentication
        auth = ServiceAccountAuth(
            service_account_path=service_account_path,
            scopes=[
                "https://www.googleapis.com/auth/cloud-platform",
                "https://www.googleapis.com/auth/bigquery"
            ]
        )

        # Configure audit logging
        audit_logger = AuditLogger(
            project_id=project_id,
            log_level="INFO",
            include_request_data=True
        )

        # Set up encryption
        encryption = DataEncryption(
            key_management="cloud_kms",
            encryption_keys=["key-1", "key-2"]
        )

        # Create secure agent
        self.agent = Agent(
            name="secure_enterprise_agent",
            project_id=project_id,
            auth=auth,
            audit_logger=audit_logger,
            data_encryption=encryption,

            # Security policies
            security_policies={
                "require_authentication": True,
                "log_all_requests": True,
                "encrypt_sensitive_data": True,
                "access_control": "role_based"
            }
        )

    def secure_process(self, query: str, user_id: str) -> dict:
        """Process query with full security logging"""

        # Log the request
        self.agent.audit_logger.log_request(
            user_id=user_id,
            query=query,
            timestamp="current_time"
        )

        # Process with security
        response = self.agent.process(
            query=query,
            user_context={"user_id": user_id},
            security_level="high"
        )

        # Log the response
        self.agent.audit_logger.log_response(
            user_id=user_id,
            response_summary=response.content[:100],
            timestamp="current_time"
        )

        return {
            "response": response.content,
            "security_audit_logged": True,
            "user_id": user_id
        }

# Production deployment configuration

deployment_config = {
    "environment": "production",
    "scaling": {
        "min_instances": 2,
        "max_instances": 10,
        "auto_scaling": True
    },
    "monitoring": {
        "enable_metrics": True,
        "alert_on_errors": True,
        "performance_tracking": True
    },
    "security": {
        "vpc_connector": "projects/your-project/locations/region/connectors/connector",
        "service_account": "agent-service-account@your-project.iam.gserviceaccount.com"
    }
}

def deploy_to_production(agent: Agent, config: dict):
    """Deploy ADK agent to production environment"""

    # Deploy with Google Cloud Run or App Engine
    deployment = agent.deploy(
        platform="cloud_run",
        config=config
    )

    return {
        "deployment_url": deployment.url,
        "status": deployment.status,
        "health_check": deployment.health_endpoint
    }

# ADK Security Best Practices

security_checklist = {
    "authentication": [
        "Use service accounts for production",
        "Implement proper IAM roles",
        "Enable audit logging"
    ],
    "data_protection": [
        "Encrypt sensitive data",
        "Use VPC for network isolation",
        "Implement data retention policies"
    ],
    "access_control": [
        "Principle of least privilege",
        "Regular access reviews", 
        "Multi-factor authentication"
    ]
}

# ADK Performance Best Practices

performance_guidelines = {
    "model_selection": "Choose appropriate model for task complexity",
    "caching": "Enable response caching for repeated queries",
    "connection_pooling": "Use connection pools for high throughput",
    "monitoring": "Monitor latency and error rates",
    "scaling": "Configure auto-scaling based on demand"
}