# Agentic Code Review in the Terminal: A Trajectory-Level Analysis of Behavior, Cost, and Human-Alignment

arXiv: https://arxiv.org/abs/2607.16740 · HTML: https://mlsanity.com/paper/2607.16740/

## 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 10 of its 14 references that are in the map.
- First posted on arXiv 18 Jul 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, 10 of 10)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Process-Centric Analysis of Agentic Software Systems](https://mlsanity.com/paper/2512.02393/) (2025)
- [CLI-Gym: Scalable CLI Task Generation via Agentic Environment Inversion](https://mlsanity.com/paper/2602.10999/) (2026)
- [Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation](https://mlsanity.com/paper/2502.02757/) (2025)
- [Rethinking Code Review Workflows with LLM Assistance: An Empirical Study](https://mlsanity.com/paper/2505.16339/) (2025)
- [HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation](https://mlsanity.com/paper/2601.19072/) (2026)
- [AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Context](https://mlsanity.com/paper/2601.19494/) (2026)
- [Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practice](https://mlsanity.com/paper/2511.07017/) (2025)
- [Sphinx: Benchmarking and Modeling for LLM-Driven Pull Request Review](https://mlsanity.com/paper/2601.04252/) (2026)

## Built on by (most cited first, 1 of 1)

- [Quantifying Overclaiming Propensity in Frontier LLM Agents](https://mlsanity.com/paper/2609.20812/) (2026)

## Abstract (arXiv)

Agentic code review in terminal-based environments enables early feedback during local development before pull request creation. However, existing evaluations remain performance-centric and fail to capture the dynamic behaviors of repository-grounded agentic reviewers. Understanding these behaviors is critical for identifying how agentic reviewers succeed, fail, and incur hidden operational costs in practice. Then, we analyze the reviewers' behavior based on their trajectories. Our results show that agentic reviewers achieve higher review precision but incur substantial exploration and validation overhead, while successful reviews are associated with stronger planning and less downstream validation. These findings highlight the potential benefits of trajectory-aware and cost-sensitive evaluation of future agentic code review systems.