Code That Doesn't Break

The Slack message arrived at 2:47 AM on a Tuesday. The nightly order sync had crashed. Not a new crash — the same one from two weeks ago, the one Priya thought she had fixed. Last time it was a missing customer email. This time it was a shipping address with a null postal code. Both times, the entire batch of 400 orders failed because one record had bad data and nobody had told the program what to do when that happened. Priya's fix the first time was reasonable: she added an if-statement to check for missing emails. It worked for exactly that one type of bad data. The postal code failure came from the same pipeline, three functions deeper, in a place she had never thought to guard. The program hit a None where it expected a string, Python raised a TypeError, and the whole sync collapsed. She knows the try/except syntax. She has written the generic version dozens of times: try the thing, catch Exception, print the error, move on. But catching every possible error and printing it is not actually handling it. It is sweeping it under a rug. The program keeps running, which feels like success, until you realise it has been silently skipping 30 orders a night for a week. Real error handling is not about preventing crashes. It is about code that knows what can go wrong and responds to each failure specifically.