Let's begin by implementing the program in Python using the Pandas library for data manipulation and CSV handling. Below is the code for the program:

import pandas as pd

def calculate_total_compensation(salary, bonus):
    return salary + bonus

def process_employee_data(input_file, output_file):
    try:
        # Read the input CSV file
        df = pd.read_csv(input_file)

        # Calculate total compensation for each employee
        df['Total Compensation'] = df.apply(lambda row: calculate_total_compensation(row['Salary'], row['Bonus']), axis=1)

        # Save the updated data to a new CSV file
        df.to_csv(output_file, index=False)

        print(f"Total compensation calculated and saved to {output_file}")
    except Exception as e:
        print(f"Error processing data: {e}")

# Input and output file paths
input_file = 'employee_data.csv'
output_file = 'employee_compensation.csv'

# Process the employee data
process_employee_data(input_file, output_file)

In this code:

• We define a function calculate_total_compensation to calculate the total compensation for each employee based on their salary and bonus.
• The process_employee_data function reads the input CSV file, calculates total compensation using Pandas' apply function, and then saves the updated data to a new CSV file.
• Error handling is implemented in the try-except block to catch any potential exceptions during file reading or data processing.

You can replace 'employee_data.csv' with the actual path to your input CSV file and 'employee_compensation.csv' with the desired output file path.

Please let me know if you would like any modifications or if there's anything else you'd like to discuss!