AI-generated code can look clean, pass tests, and survive review, yet still behave subtly differently under real production traffic. The problems often come from unusual structure that is hard to spot in a diff, missing negative cases, environment differences, messy data, concurrency, time zones, or network failures. Sometimes issues are discovered days later through monitoring or user reports, making production debugging especially painful. What practices are helping you catch these problems before deployment, and how do you reduce the number of AI-assisted bugs that still escape into production?
4 Answers
A lot of “works locally, breaks in production” issues are environmental rather than specifically AI-related. Check configuration and secrets, URL differences, filesystem case sensitivity, real data volume, null values, missing indexes, time zones, concurrency, and network timeouts. Running the production build locally, using messy data in a staging database, matching production time-zone settings, and testing realistic traffic can expose many of these problems earlier.
Make sure the safety nets are actually running. A large test suite can create false confidence if a configuration mistake causes every file to be skipped or no tests to be collected. The test command should fail when zero suites run, and CI should verify the expected number of tests or enforce coverage thresholds. Local success also means little if the local server does not use important production settings such as the same security policy.
You cannot eliminate every production-only issue, so add a strong backstop: structured logs, useful metrics, tracing, error alerts, and gradual rollouts or canaries. Watch behavior after deployment and make rollback easy. Observability will not replace review and testing, but it shortens the time between a subtle failure appearing and someone understanding what happened.
Treat AI-generated code like code from a junior developer: read it line by line and make sure you understand how it executes. Tests should cover failure paths and boundary conditions, not only the expected success case. Writing tests first can help because it forces you to define what should happen when inputs are missing, invalid, slow, duplicated, or partially unavailable.
Traffic replay has been useful for this. We run recent production request patterns against the candidate build before merging, which catches performance and data-shape problems that ordinary unit tests miss.