# Using Agentic AI for contextualized and multifaceted code review at Ericsson

arXiv: https://arxiv.org/abs/2609.15877 · HTML: https://mlsanity.com/paper/2609.15877/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 14 Sep 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, 13 of 13)

- [BitsAI-CR: Automated Code Review via LLM in Practice](https://mlsanity.com/paper/2501.15134/) (2025)
- [Exploring the Capabilities of LLMs for Code Change Related Tasks](https://mlsanity.com/paper/2407.02824/) (2024)
- [Modern Code Reviews -- Survey of Literature and Practice](https://mlsanity.com/paper/2403.00088/) (2024)
- [RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian](https://mlsanity.com/paper/2601.01129/) (2026)
- [Automated Code Review Using Large Language Models at Ericsson: An Experience Report](https://mlsanity.com/paper/2507.19115/) (2025)
- [Mining Code Review Data to Understand Waiting Times Between Acceptance and Merging: An Empirical Analysis](https://mlsanity.com/paper/2203.05048/) (2022)
- [Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practice](https://mlsanity.com/paper/2511.07017/) (2025)
- [A Roadmap on Modern Code Review: Challenges and Opportunities](https://mlsanity.com/paper/2405.18216/) (2024)
- [Studying the Prevalence of Exception Handling Anti-Patterns](https://mlsanity.com/paper/1704.00778/) (2017)
- [Reassessing Java Code Readability Models with a Human-Centered Approach](https://mlsanity.com/paper/2401.14936/) (2024)
- [The AI-Native Large-Scale Agile Software Development Manifesto](https://mlsanity.com/paper/2605.07717/) (2026)
- [Help Me to Understand this Commit! -- A Vision for Contextualized Code Reviews](https://mlsanity.com/paper/2402.09528/) (2024)
- [Assessing Consensus of Developers' Views on Code Readability](https://mlsanity.com/paper/2407.03790/) (2024)

## Abstract (arXiv)

Context: Conducting effective code reviews is increasingly challenging due to the growing complexity of software systems and the accelerated code generation by AI coding agents. LLM-based approaches for code reviews have shown promising results in identifying defects and improving code quality. However, existing approaches rarely consider project-specific contextualized knowledge, and few have been evaluated in industrial settings. Objective: In this study, we propose a multi-agent-based solution that provides multifaceted assessments of code changes. Method: Following the Design Science Research Process, we developed and evaluated our solution in an industrial setting. Our solution combines specialized agent skills with context-specific knowledge to identify antipatterns in code changes across four dimensions: readability, maintainability, reliability, and performance. Using our solution, we generated reviews for several code commits and identified more than 200 issues. These issues were then manually validated by the developers of the case company for their correctness and importance. Results: The evaluation results show that our solution achieves 96% accuracy in correctly identifying issues in the investigated code commits. Furthermore, around 69% of the correctly identified issues were rated as important, with approximately 33% rated as severe issues that must be fixed and 36% as important issues that should be fixed. Qualitative feedback from developers corroborates these findings and highlights the usefulness of the generated reviews. Conclusion: Our findings provide empirical evidence from an industrial evaluation that combining specialized agent skills with context-specific knowledge yields accurate, practically useful code reviews.