🔗 AI code review tools: what works, what doesn't, and how to set it up

AI code review tools excel at catching mechanical issues like security patterns, formatting violations, test coverage gaps, and code smells that humans miss when fatigued. However, they consistently fail at architectural decisions, business logic validation, and recognizing when correct code represents the wrong approach.

The practical setup involves choosing a tool based on infrastructure constraints, configuring it to reduce noise, and establishing a workflow where AI reviews first to handle surface-level checks. Human reviewers then focus on depth—architecture, business logic, and strategic fit—making reviews faster and more focused rather than replacing human judgment entirely.