Artificial intelligence-assisted code review analyzes code and changes to identify potential problems or explain unfamiliar logic. It can flag defects, unsafe patterns, missing tests, and architectural concerns, and can link findings to relevant repository or organizational context.
The findings are hypotheses rather than proof. Reviews can miss hidden behavior or produce false positives, especially without build context and runtime evidence. Security-sensitive changes need testing, provenance, and accountable human judgment in addition to model analysis.
