The End of Code Review: Coding Agents Supersede Human Inspection
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Code review has been the primary quality gate in software development since Fagan formalised code inspection in 1976. For five decades, having a human examine and comment on a colleague's changes before merge has been a cornerstone practice at organisations of every size. Coding agents are large language model (LLM)-based autonomous systems capable of reading, writing, testing, and repairing software. We argue that coding agents have crossed a threshold of capability at which traditional human code review is no longer a necessary component of a software quality pipeline. Our argument rests on two claims: every stated goal of code review can be served by agents at lower cost and higher throughput; the naive integration in which agents write code and humans remain the mandatory reviewers is a dead end because it neither provides meaningful assurance nor scales with AI-assisted throughput.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 16 of its 35 references that are in the map. First posted on arXiv 11 Jun 2026.
Builds on
16 of 35 references.
Closest ancestors · 2
Same direction, newest first
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
- 2024CodeAgent: Autonomous Communicative Agents for Code Review3 Feb
Other references · 14
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2022Language Models (Mostly) Know What They Knowcited by 1,783
- 2022Competition-Level Code Generation with AlphaCodecited by 1,427
- 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
- 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 417
- 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 264
- 2022Productivity Assessment of Neural Code Completioncited by 97
- 2023How Secure is Code Generated by ChatGPT?cited by 77
- 2022Using Pre-Trained Models to Boost Code Review Automationcited by 63
- 2023LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuningcited by 47
- 2025Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositoriescited by 29
- 2017Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practicescited by 22
2 more
Built on by
Citing papers per year: 2026: 3.
3 papers.
Similar, not cited
Close in wording, no citation either way
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review
- 2026Human-AI Synergy in Agentic Code Review
- 2026From Human-Centric to Agentic Code Review: The Impact of Different Generations of Generative AI Technology on Review Quality
- 2026Code Review Agent Benchmark
- 2026CR-Bench: Evaluating the Real-World Utility of AI Code Review Agents
- 2026Does Code Cleanliness Affect Coding Agents? A Controlled Minimal-Pair Study
- 2026Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time
- 2026A Case Study in Assuring AI-Written Software