Plan and Execute › Bringing It Together: A Complete Example

## Using Claude Sonnet 4.5 for end-to-end plan execution
import os
from anthropic import Anthropic
import json

client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))

## Define our tools (simplified for illustration)
tools = [
    {
        "name": "search_destinations",
        "description": "Find destinations within specified travel time",
        "input_schema": {
            "type": "object",
            "properties": {
                "max_hours": {"type": "number"},
                "origin": {"type": "string"}
            }
        }
    },
    {
        "name": "search_hotels",
        "description": "Search for available hotels in a city",
        "input_schema": {
            "type": "object",
            "properties": {
                "city": {"type": "string"},
                "check_in": {"type": "string"},
                "check_out": {"type": "string"}
            }
        }
    }
]

def execute_complete_plan(user_request):
    """Full plan-and-execute cycle."""
    
    # Step 1: Create the plan (from previous chapter)
    plan_response = client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=1024,
        messages=[{
            "role": "user",
            "content": f"""Create a step-by-step plan for: {user_request}

List each step clearly."""
        }]
    )
    
    plan_text = plan_response.content[0].text
    print(f"Plan created:\n{plan_text}\n")
    
    # Step 2: Execute each step
    print("Beginning execution...\n")
    
    results = []
    messages = [{
        "role": "user",
        "content": f"Execute this plan step by step:\n{plan_text}"
    }]
    
    # Execute until complete
    while True:
        response = client.messages.create(
            model="claude-sonnet-4-5",
            max_tokens=2048,
            tools=tools,
            messages=messages
        )
        
        # Add assistant response to conversation
        messages.append({
            "role": "assistant",
            "content": response.content
        })
        
        # Check if agent wants to use a tool
        if response.stop_reason == "tool_use":
            # Find the tool use block
            tool_use = next(
                block for block in response.content 
                if block.type == "tool_use"
            )
            
            print(f"Using tool: {tool_use.name}")
            
            # Simulate tool execution (in real code, call actual APIs)
            tool_result = simulate_tool_call(tool_use.name, tool_use.input)
            
            # Add tool result to conversation
            messages.append({
                "role": "user",
                "content": [{
                    "type": "tool_result",
                    "tool_use_id": tool_use.id,
                    "content": json.dumps(tool_result)
                }]
            })
            
        elif response.stop_reason == "end_turn":
            # Agent is done
            final_response = next(
                block.text for block in response.content 
                if hasattr(block, "text")
            )
            print(f"\nFinal result:\n{final_response}")
            break
    
    return final_response

## Example usage
result = execute_complete_plan(
    "Plan a weekend getaway within 3 hours of San Francisco"
)