Code review has evolved for decades, from informal peer checking to today's pull request (PR) workflows, yet it remains a largely manual 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 in code review 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. We address this gap by treating review effectiveness as an outcome of the full code review lifecycle rather than a single stage, proposing a framework that carries context across stage boundaries. We propose a future vision for code review in which reviewers transition from manual inspectors into supervisory operators of agents. In this vision, staged, AI-powered workflows aim to align the pace of code generation with shared understanding and accountable engineering. In this paper, we review the historical evolution of code review practices, identify challenges in traditional code review systems, 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. Finally, we identify key adoption challenges and outline research directions for evaluation, governance, and responsible human-AI collaboration.

翻译：代码审查历经数十年发展，从非正式的同伴互审演进至如今的拉取请求（PR）工作流，但其本质上仍是一个高度依赖人工且认知负荷沉重的过程。人工智能（AI）编码助手的崛起进一步加剧了这一挑战：这些工具在提升代码生成速度的同时，也扩大了需要审查的代码量，使代码审查日益成为开发流程的瓶颈。当前AI对代码审查的支持仍较为零散，各类工具仅聚焦于孤立任务，如审查者推荐、PR描述生成或评论建议，而非覆盖端到端的PR审查工作流。我们通过将审查效能视为完整代码审查生命周期的产出（而非单一阶段的成果）来填补这一空白，并提出一个跨阶段传递上下文信息的框架。本文提出代码审查的未来愿景：审查者从手动检查者转变为智能体的监管运营者。在此愿景中，分阶段推进的AI驱动工作流旨在使代码生成速度与共享理解及负责任工程实践保持同步。本文回顾了代码审查实践的历史演进，识别了传统代码审查系统中的挑战，并审视了大语言模型（LLM）和智能体AI系统引发的范式转变。随后，我们提出一种结合专用智能体与人工控制质量门的AI驱动代码审查工作流愿景。该框架涵盖五个阶段：PR创建、PR增强、审查者选择、AI辅助代码审查及PR回溯，关键决策点保留人类介入以维护判断力、问责机制及团队级理解。最后，我们识别了关键采纳挑战，并概述了评估、治理及负责任人机协作方向的研究路径。