# Example Bedrock Model Configuration
from strands.models import BedrockModel

bedrock_model = BedrockModel(
    model_id="us.amazon.nova-pro-v1:0",
    temperature=0.3,  # Balanced creativity and consistency
)


# Deployment Configuration
agentcore_runtime.deploy(
    agent_name="strands_claude_remote",
    entry_point="strands_claude_remote.py",
    platform="linux/arm64",
    network_mode="PUBLIC",
    observability_enabled=True
)


# AgentCore Memory Integration
from strands_tools.agent_core_memory import AgentCoreMemoryToolProvider

memory_provider = AgentCoreMemoryToolProvider(
    memory_id="memory-123abc4567",
    actor_id="user_123",
    session_id="session-789",
    namespace="default",
    region="us-west-2"
)


# S3 Session Management
from strands.session.s3_session_manager import S3SessionManager

session_manager = S3SessionManager(
    bucket_name="strands-sessions",
    customer_id="customer_001"
)


# Cognito Authentication Flow
def get_cognito_token(user_pool_id, client_id, client_secret, scope):
    cognito_client = boto3.client('cognito-idp')
    response = cognito_client.initiate_auth(
        ClientId=client_id,
        AuthFlow='CLIENT_CREDENTIALS',
        AuthParameters={'SECRET_HASH': secret_hash}
    )
    return response['AuthenticationResult']['AccessToken']


# OpenTelemetry Tracing Integration
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

tracer_provider = TracerProvider()
otlp_exporter = OTLPSpanExporter(
    endpoint="https://cloudwatch-endpoint/v1/traces"
)
tracer_provider.add_span_processor(BatchSpanProcessor(otlp_exporter))


# MCP Server Example - Calculator Tool
from mcp.server import Server
from mcp.server.stdio import stdio_server
import mcp.types as types

server = Server("calculator")

@server.list_tools()
async def list_tools() -> list[types.Tool]:
    return [
        types.Tool(
            name="calculate",
            description="Perform mathematical calculations",
            inputSchema={
                "type": "object",
                "properties": {
                    "expression": {"type": "string"}
                },
                "required": ["expression"]
            }
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict) -> str:
    if name == "calculate":
        return str(eval(arguments["expression"]))


# Teacher Assistant Orchestrator
from strands import Agent, tool
from strands_tools import use_agent

# Specialized agent tools
@tool
def math_assistant(query: str) -> str:
    """Process math-related queries using calculator tool"""
    formatted_query = f"Solve this math problem: {query}"
    math_agent = Agent(
        system_prompt="You are a math expert with calculator capabilities.",
        tools=[calculator],
    )
    return math_agent(formatted_query)

@tool
def computer_science_assistant(query: str) -> str:
    """Handle programming and CS queries"""
    cs_agent = Agent(
        system_prompt="You are a computer science expert.",
        tools=[code_interpreter, editor, file_read, file_write],
    )
    return cs_agent(query)

# Main orchestrator
teacher_agent = Agent(
    system_prompt=ORCHESTRATOR_PROMPT,
    tools=[
        math_assistant,
        english_assistant,
        language_assistant,
        computer_science_assistant,
        general_assistant
    ]
)

# Query routing
response = teacher_agent("What is the derivative of x^2?")
# Routes to math_assistant automatically


# Research Workflow: Researcher → Analyst → Writer
def run_research_workflow(user_input):
    # Step 1: Research Agent
    researcher = Agent(
        system_prompt="Gather web information using tools",
        tools=[http_request, retrieve]
    )
    research_data = researcher(user_input)
    
    # Step 2: Analyst Agent
    analyst = Agent(
        system_prompt="Verify facts and synthesize findings"
    )
    analysis = analyst(f"Analyze this research: {research_data}")
    
    # Step 3: Writer Agent
    writer = Agent(
        system_prompt="Create a comprehensive report"
    )
    final_report = writer(f"Write a report based on: {analysis}")
    
    return final_report


from strands_tools import mem0_memory

memory_agent = Agent(
    system_prompt="You remember user preferences and history",
    tools=[mem0_memory]
)

# Store information
memory_agent("Remember that I prefer Python over JavaScript")

# Later session - retrieves memory automatically
response = memory_agent("What programming language should I use?")
# Response: "Based on your preference, I recommend Python..."


from strands_tools import meta_tooling

meta_agent = Agent(
    system_prompt="You can create tools dynamically",
    tools=[meta_tooling]
)

# Create a custom tool through natural language
response = meta_agent(
    "Create a tool that converts temperatures from Celsius to Fahrenheit"
)
# Agent generates and loads the tool automatically

# Use the newly created tool
temp_response = meta_agent("Convert 25°C to Fahrenheit")
# Response: "25°C is 77°F"


# AgentCore Configuration
runtime_configuration:
  min_instances: 2
  max_instances: 100
  auto_scaling:
    target_utilization: 70%
    scale_up_cooldown: 60s
    scale_down_cooldown: 300s


# Custom Metrics
cloudwatch.put_metric_data(
    Namespace='StrandsAgents',
    MetricData=[
        {
            'MetricName': 'AgentInvocationCount',
            'Value': 1,
            'Unit': 'Count',
            'Dimensions': [
                {'Name': 'AgentType', 'Value': 'teacher_assistant'},
                {'Name': 'Model', 'Value': 'nova-pro-v1'}
            ]
        }
    ]
)