"""
TAB Agent Template
==================
Implement the 'respond' function below. TAB will call this function
with each benchmark prompt and use your return value as the agent's answer.

Your function receives a single string (the prompt) and must return a single string (the response).

You can import any standard library modules. If you need external packages,
list them in requirements.txt and upload alongside your agent file.

Example packages that are pre-installed: requests, numpy, pandas, openai, anthropic
"""

def respond(prompt: str) -> str:
    """
    Process a benchmark prompt and return your agent's response.
    
    Args:
        prompt: The benchmark test prompt (could be a question, coding task, etc.)
    
    Returns:
        Your agent's response as a string
    """
    # YOUR AGENT LOGIC HERE
    # Example: Call your own model
    # 
    # import openai
    # client = openai.OpenAI(api_key="your-key-here")  
    # response = client.chat.completions.create(
    #     model="gpt-4",
    #     messages=[{"role": "user", "content": prompt}]
    # )
    # return response.choices[0].message.content
    
    return "Replace this with your agent's response logic"