mlsanityData to 7 Oct 2026

Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLI

arXiv 2607.01418 ↗First posted 1 Jul 2026cs.SE cs.AI cs.HCcited by 1

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

Abstract (arXiv)

Organizations rolling out agentic command line tools like Anthropic's Claude Code and GitHub's Copilot CLI need to know who will try them, who will keep using them, and whether the tools produce enough output to justify their cost. At organizational scale, token spend can run into millions of dollars annually, so misreading adoption, retention, or impact can make a rollout expensive without changing engineering velocity. Studying tens of thousands of engineers at Microsoft over its early-2026 rollout, we find that first use spread primarily through social networks, retention was associated more with engineers' coding activity than with demographics, and adopters merged roughly 24% more pull requests than they would have otherwise. We use merged pull requests as our proxy for output -- acknowledging that a merged PR is not the same as the value it delivers -- and the lift persists across our four-month window. These results suggest that CLI coding agents are neither uniformly adopted nor mere novelty effects and that organizations should treat visible peer use as central to rollout strategy.

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 23 of its 45 references that are in the map. First posted on arXiv 1 Jul 2026.

Builds on

23 of 45 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026Agentic Much? Adoption of Coding Agents on GitHub26 Jan
  2. 2026AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development20 Jan
  3. 2025Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects6 Nov
  4. 2025Why AI Agents Still Need You: Findings from Developer-Agent Collaborations in the Wild14 Jun

Other references · 19

  1. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 264
  2. 2022Productivity Assessment of Neural Code Completioncited by 97
  3. 2025Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivitycited by 93
  4. 2022Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programmingcited by 91
  5. 2024How much does AI impact development speed? An enterprise-based randomized controlled trialcited by 19
  6. 2025Who is using AI to code? Global diffusion and impact of generative AIcited by 17
  7. 2024The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilotcited by 13
  8. 2024The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilotcited by 11
  9. 2025The Effects of GitHub Copilot on Computing Students' Programming Effectiveness, Efficiency, and Processes in Brownfield Programming Taskscited by 9
  10. 2024Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplacecited by 7
  11. 2025Intuition to Evidence: Measuring AI's True Impact on Developer Productivitycited by 5
  12. 2025Developer Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Studycited by 5
7 more
  1. 2025The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Studycited by 4
  2. 2025Developers' Experience with Generative AI -- First Insights from an Empirical Mixed-Methods Field Studycited by 4
  3. 2025The Fast and Spurious: Developer Productivity with GenAIcited by 3
  4. 2024The Heterogeneous Productivity Effects of Generative AIcited by 2
  5. 2026The State of Generative AI in Software Development: Insights from Literature and a Developer Surveycited by 2
  6. 2026Impacts of Generative AI on Agile Teams' Productivity: A Multi-Case Longitudinal Studycited by 1
  7. 2026GitHub Copilot and Developer Productivity: An Observational Dose-Response Analysiscited by 1

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. 2026Augmentation with Dilution: A Large-Scale Empirical Study of Human Contributor Ecosystems After AI Coding Agent Adoption
  2. 2026Decoupling Code Complexity from Newcomer Participation: A Causal Study of AI Coding Agent Adoption in OSS
  3. 2026Early Adoption of Agentic Coding Tools by GitHub Projects
  4. 2026A Few Pages of Markdown: Committed AI Configuration and Lower Quality Cost after Coding-Agent Adoption
  5. 2026Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time
  6. 2026Agentic Very Much! Adoption of Coding Agent in New GitHub Projects
  7. 2025A Study of Library Usage in Agent-Authored Pull Requests
  8. 2026Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance