Why AI-Generated Go Code Compiles Fine Yet Still Breaks Your App in Production

AI coding tools like Claude Code and Cursor can generate Go code that compiles and passes basic tests, yet still breaks real-world functionality due to outdated patterns and missing configuration data. Using the open-source go-admin framework as an example, models trained on older GitHub code often apply a legacy multi-file style instead of the current Actions-based three-file pattern, silently mixing incompatible conventions across a codebase. Beyond style mismatches, a module only becomes fully functional in the UI when seed data is correctly inserted into four database tables covering routes, menus, and permissions — a step AI tools routinely skip without any error or warning. To address this, the go-admin project introduced an AGENTS.md convention file, a CI-tested reference implementation, and a structured end-to-end 'Skill' that guides AI tools through scaffolding, migrations, and the critical permission-seeding step. The approach highlights a broader principle: reliable AI-assisted development requires explicit, machine-readable context layers rather than relying on a model's potentially outdated training data.