mlsanityData to 7 Oct 2026

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

arXiv 2607.16740 ↗First posted 18 Jul 2026cs.SEcited by 1

On the map: LLM agents and tool use → Software engineering coding agents

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.

Key facts. Cited by 1 paper 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 10 of its 14 references that are in the map. First posted on arXiv 18 Jul 2026.

Builds on

10 of 14 references.

Closest ancestors · 2

Same direction, newest first

  1. 2026CLI-Gym: Scalable CLI Task Generation via Agentic Environment Inversion11 Feb
  2. 2025Process-Centric Analysis of Agentic Software Systems2 Dec

Other references · 8

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  3. 2025Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generationcited by 12
  4. 2025Rethinking Code Review Workflows with LLM Assistance: An Empirical Studycited by 10
  5. 2026HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automationcited by 5
  6. 2026AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Contextcited by 5
  7. 2025Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practicecited by 4
  8. 2026Sphinx: Benchmarking and Modeling for LLM-Driven Pull Request Reviewcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Is Agentic Code Review Helpful? Mining Developers' Feedback to CodeRabbit Reviews in the Wild
  2. 2026SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review
  3. 2026When AI Teammates Meet Code Review: Collaboration Signals Shaping the Integration of Agent-Authored Pull Requests
  4. 2026AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
  5. 2026Human-AI Synergy in Agentic Code Review
  6. 2026From Human-Centric to Agentic Code Review: The Impact of Different Generations of Generative AI Technology on Review Quality
  7. 2026CR-Bench: Evaluating the Real-World Utility of AI Code Review Agents
  8. 2026"Go Home Copilot, You're Drunk": Understanding Developer Responses to Agent-Generated Code Review Comments