How to Spot and Fix Fail-Open Defaults in AI-Generated Code PRs
AI agent-generated pull requests can introduce unverified default values — such as hardcoded URLs and timeout figures — that have no traceable source in the existing codebase. A key risk is 'fail-open' behavior, where error-handling code silently returns a success response even when a critical service like billing is unreachable. Reviewers are advised to audit every new environment variable, endpoint, and return shape introduced by an agent patch, building a provenance table that cites a verified source for each default or strips it entirely. Automated scanning scripts and targeted regex searches can help surface swallowed errors and invented fallback values that visual review might miss. The recommended standard is fail-closed behavior by default, meaning errors should throw exceptions rather than return false success signals, unless a written SLA explicitly permits deferral.
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