AI Code Review in Agentic Coding Workflows

Sonar front-end developer Quentin Chevrin describes how AI coding agents have greatly accelerated software generation while shifting the main constraint to reviewing and verifying larger volumes of pull requests. After moving from GitHub Copilot suggestions to a terminal-based workflow using Claude Code, Sonar CLI, and development plugins, he now relies on a three-layer process in which SonarQube checks code quality and security, Gitar assesses broader correctness and product fit, and human reviewers provide final approval. Chevrin argues that automated deterministic analysis and reasoning-based review address different risks, while agents should never approve or commit their own output. AI remains less effective for design review, can incur substantial context and token costs, and may create comprehension debt when developers delegate specification and planning work they need to understand themselves. He manages these risks by giving agents constrained tasks, using short sessions, scaling autonomy to a change’s potential impact, and actively steering work, while noting that AI has expanded his ability to contribute across backend, infrastructure, CI, and cloud systems.

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