Logging Architecture: Making Every Log Line AI Writes Valuable
Standardize log formats, log levels, and trace instrumentation so that AI stops logging chaotically everywhere. As AI-assisted programming becomes increasingly common, an easily overlooked problem is emerging: AI is very good at writing logs, but not necessarily good at writing “useful logs.” Ask AI to implement a feature, and it will likely generate something like: logger.info("start") logger.info("processing...") logger.info("data loaded") logger.error("failed") Individually, these seem fine. But as the project grows larger, you’ll find your logs turning into something like this: start processing... loading data... done request failed retry... success When something actually breaks, it becomes very difficult to answer a few of the most basic questions: Which request caused the error? Which module caused the error? Which step caused the error? … The problem is rarely “too few logs.” It’s usually: There are plenty of logs, but no system behind them. Therefore, just like exception handling, dependency injection, and interface contracts, logging should also be part of the project architecture — not left to the free rein of developers or AI. ...