Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review

TL;DR

This paper proposes a comprehensive AI-powered code review framework that integrates specialized agents with human oversight to improve efficiency, accountability, and collaboration in the evolving landscape of AI-assisted software development.

Contribution

It introduces a novel end-to-end AI-driven code review workflow framework that combines AI agents with human judgment at key decision points.

Findings

Identifies five stages of the proposed AI-assisted code review process.

Highlights open challenges like reliability, bias, privacy, and transparency.

Provides a research agenda for human-AI collaboration in code review.

Abstract

Code review has evolved for decades, from informal peer checking to today's pull request (PR) workflows, yet it remains a largely manual, uneven, and cognitively demanding process. The rise of Artificial Intelligence (AI) coding assistants has intensified this challenge: while these tools increase code production velocity, they also expand the volume of code requiring review, turning code review into a growing bottleneck. Current AI support remains fragmented, with tools focusing on isolated tasks such as reviewer recommendation, PR description generation, or comment suggestion rather than the end-to-end PR review workflow. In this paper, we review the historical evolution of code review practices and examine the shift driven by large language models (LLMs) and agentic AI systems. We then present a vision for an AI-powered code review workflow combining specialized agents with human-controlled quality gates. Our framework spans five stages: PR Creation, PR Augmentation, Reviewer Selection, AI-Assisted Code Review, and PR Retrospective, with humans retained at key decision points to preserve judgment, accountability, and team-level understanding. We identify major open challenges for responsible adoption, including reliability, bias, privacy, automation bias, transparency, and evaluation, and offer a research agenda for more effective human-AI collaboration in software engineering.