# Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review arXiv: https://arxiv.org/abs/2605.17548 ยท HTML: https://mlsanity.com/paper/2605.17548/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 90 of its 250 references that are in the map. - First posted on arXiv 17 May 2026. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 30 of 90) - [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021) - [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019) - [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023) - [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023) - [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020) - [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022) - [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016) - [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020) - [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023) - [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020) - [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023) - [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021) - [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023) - [Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions](https://mlsanity.com/paper/2306.02224/) (2023) - [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023) - [AI Agents That Matter](https://mlsanity.com/paper/2407.01502/) (2024) - [The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies](https://mlsanity.com/paper/2407.19354/) (2024) - [In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents](https://mlsanity.com/paper/2503.08026/) (2025) - [Automating Code Review Activities by Large-Scale Pre-training](https://mlsanity.com/paper/2203.09095/) (2022) - [LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation](https://mlsanity.com/paper/2409.20550/) (2024) - [Using Pre-Trained Models to Boost Code Review Automation](https://mlsanity.com/paper/2201.06850/) (2022) - [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025) - [Combating the Compounding-Error Problem with a Multi-step Model](https://mlsanity.com/paper/1905.13320/) (2019) - [Towards Automating Code Review Activities](https://mlsanity.com/paper/2101.02518/) (2021) - [LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2308.11148/) (2023) - [Getafix: Learning to Fix Bugs Automatically](https://mlsanity.com/paper/1902.06111/) (2019) - [CC2Vec: Distributed Representations of Code Changes](https://mlsanity.com/paper/2003.05620/) (2020) - [Evaluating Agent-based Program Repair at Google](https://mlsanity.com/paper/2501.07531/) (2025) ## Built on by (most cited first, 1 of 1) - [Where Accountability Lives: Mapping Human Responsibility to Workflow Artifacts in Agentic Software Development](https://mlsanity.com/paper/2608.15678/) (2026) ## Abstract (arXiv) 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.