AI-Powered Code Review: A Framework for Agentic Workflows in Software Development
By
[Submitted on 17 May 2026 (v1), last revised 5 Jun 2026 (this version, v2)]
Crispy enough to crunch, soft enough to enjoy. A good bake.
Summary
This paper examines the evolution of code review practices and proposes a vision for AI-powered, agentic code review workflows. It argues that current AI coding assistants increase code production velocity but also create a bottleneck by expanding the volume of code requiring review, while existing AI support remains fragmented across isolated tasks. The authors propose a five-stage framework (PR Creation, PR Augmentation, Reviewer Selection, AI-Assisted Code Review, and PR Retrospective) that treats review effectiveness as an outcome of the full lifecycle, with humans retained at key decision points. The vision transitions reviewers from manual inspectors into supervisory operators of AI agents, aiming to align code generation pace with shared understanding and accountable engineering.
Key quotes
· 4 pulledCurrent 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 propose a future vision for code review in which reviewers transition from manual inspectors into supervisory operators of agents.
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.
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.
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