Code review plays a central role in collaborative software development. It helps ensure correctness, maintainability, and shared understanding. Yet despite decades of tooling improvements, the process remains slow, inconsistent, and heavily reliant on human effort. Reviewers often face fragmented context and misaligned pull requests, while authors receive feedback that varies in depth and quality. These breakdowns can delay integration, introduce defects into production, and increase coordination costs across teams.

In response to these challenges, this paper revisits the historical trajectory of code review and identifies persistent gaps in efficiency, traceability, quality, and consistency. We present our vision for a new generation of AI-augmented code review, where Large Language Models (LLMs) and multi-agent systems collaborate with human reviewers across the entire process. Instead of isolated tools, we introduce a structured and goal-oriented process that supports contextualized decisions and human oversight at every stage. This process is composed of six interlinked capabilities: (1) automated PR-issue linkage and validation, (2) alignment checks between issue intent and implemented changes, (3) LLM-generated review comments grounded in code semantics, (4) PR enrichment via impact and risk analysis, (5) evaluation of human review quality, and (6) post-review summarization and analytics.

By integrating these agentic capabilities into existing version control and CI/CD pipelines, the framework aims to shift code review from a reactive, manual activity to a proactive, context-aware, and adaptive process. We argue that this collaboration between humans and AI can significantly reduce review cycles, improve the clarity of feedback, and support continuous learning for reviewers. In doing so, the paper outlines how generative and agentic AI can redefine the foundations of code review, turning it from a human bottleneck into a structured and collaborative process between developers and intelligent systems.