# Agentic Much? Adoption of Coding Agents on GitHub

arXiv: https://arxiv.org/abs/2601.18341 · HTML: https://mlsanity.com/paper/2601.18341/

## Key facts

- Cited by 32 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 18 of its 55 references that are in the map.
- First posted on arXiv 26 Jan 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, 18 of 18)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025)
- [Productivity Assessment of Neural Code Completion](https://mlsanity.com/paper/2205.06537/) (2022)
- [Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants](https://mlsanity.com/paper/2208.09727/) (2022)
- [Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming](https://mlsanity.com/paper/2210.14306/) (2022)
- [Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories](https://mlsanity.com/paper/2506.18824/) (2025)
- [A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges](https://mlsanity.com/paper/2303.17125/) (2023)
- [Are "Solved Issues" in SWE-bench Really Solved Correctly? An Empirical Study](https://mlsanity.com/paper/2503.15223/) (2025)
- [Language Models for Code Completion: A Practical Evaluation](https://mlsanity.com/paper/2402.16197/) (2024)
- [Investigating and Designing for Trust in AI-powered Code Generation Tools](https://mlsanity.com/paper/2305.11248/) (2023)
- [Sampling Projects in GitHub for MSR Studies](https://mlsanity.com/paper/2103.04682/) (2021)
- [Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study](https://mlsanity.com/paper/2402.16480/) (2024)
- [A User-centered Security Evaluation of Copilot](https://mlsanity.com/paper/2308.06587/) (2023)
- [Context Engineering for AI Agents in Open-Source Software](https://mlsanity.com/paper/2510.21413/) (2025)

## Built on by (most cited first, 30 of 32)

- [Harness Engineering for Agentic AI Coding Tools: An Exploratory Study](https://mlsanity.com/paper/2602.14690/) (2026)
- [Are Coding Agents Generating Over-Mocked Tests? An Empirical Study](https://mlsanity.com/paper/2602.00409/) (2026)
- [Harness Engineering: Anatomy, Architecture, and Evolution of Coding Agents -- A Source-Code Study of Eleven Systems](https://mlsanity.com/paper/2609.00006/) (2026)
- [Codified Context: Infrastructure for AI Agents in a Complex Codebase](https://mlsanity.com/paper/2602.20478/) (2026)
- [Knowledge Activation: AI Skills as the Institutional Knowledge Primitive for Agentic Software Development](https://mlsanity.com/paper/2603.14805/) (2026)
- [Code Review Agent Benchmark](https://mlsanity.com/paper/2603.23448/) (2026)
- [A Dataset of Agentic AI Coding Tool Configurations](https://mlsanity.com/paper/2605.08435/) (2026)
- [Architecture Without Architects: How AI Coding Agents Shape Software Architecture](https://mlsanity.com/paper/2604.04990/) (2026)
- [The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase](https://mlsanity.com/paper/2603.25697/) (2026)
- [ORBIT: Guided Agentic Orchestration for Autonomous C-to-Rust Transpilation](https://mlsanity.com/paper/2604.12048/) (2026)
- [Does Code Cleanliness Affect Coding Agents? A Controlled Minimal-Pair Study](https://mlsanity.com/paper/2605.20049/) (2026)
- [Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study](https://mlsanity.com/paper/2606.12231/) (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](https://mlsanity.com/paper/2607.01418/) (2026)
- [Formal Disco: Scalable Open-Ended Generation of Formally Verified Programs](https://mlsanity.com/paper/2607.04631/) (2026)
- [IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests](https://mlsanity.com/paper/2607.20759/) (2026)
- [An Exploratory Study of Agent Plans for Agentic AI Coding Tools in Open-Source Software](https://mlsanity.com/paper/2608.04661/) (2026)
- [The Impact of Configuring Agentic AI Coding Tools on Build-vs-Buy Decisions: A Study Protocol](https://mlsanity.com/paper/2606.03907/) (2026)
- [Agentic Very Much! Adoption of Coding Agent in New GitHub Projects](https://mlsanity.com/paper/2606.07448/) (2026)
- [Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories](https://mlsanity.com/paper/2606.24429/) (2026)
- [Augmentation with Dilution: A Large-Scale Empirical Study of Human Contributor Ecosystems After AI Coding Agent Adoption](https://mlsanity.com/paper/2606.26289/) (2026)
- [From Conversation to Contribution: Characterizing Coding Agent in Open-Source Software](https://mlsanity.com/paper/2607.05677/) (2026)
- [How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests](https://mlsanity.com/paper/2607.21832/) (2026)
- [ALIBI: Adaptive Agentic Attacks on LLM-Based Vulnerability Detectors via Adversarial Code Comments](https://mlsanity.com/paper/2607.24964/) (2026)
- [AgenTag: Attribution of AI Coding Agents from Behavioral Fingerprints](https://mlsanity.com/paper/2608.00966/) (2026)
- [Understanding the Architecture of Coding Agents: An Exploratory Study Using a Research Prototype](https://mlsanity.com/paper/2608.10934/) (2026)
- [From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation](https://mlsanity.com/paper/2608.20195/) (2026)
- [A Case-Bundle Operating Model for Coding Agents in OpenFOAM-Based CFD](https://mlsanity.com/paper/2609.11941/) (2026)
- [Reflections on Trusting Trust, Revisited: Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarks](https://mlsanity.com/paper/2609.17817/) (2026)
- [Who Finishes the Job? A Study of Follow-Up Fixes and Commit Authorship on AI Coding Agent Pull Requests](https://mlsanity.com/paper/2609.26847/) (2026)
- [Specifying and Maintaining Agentic Workflows: An Empirical Study of GitHub Agentic Workflows](https://mlsanity.com/paper/2609.27263/) (2026)

## Abstract (arXiv)

In the first half of 2025, coding agents have emerged as a category of development tools that have very quickly transitioned to the practice. Unlike ''traditional'' code completion LLMs such as Copilot, agents like Cursor, Claude Code, or Codex operate with high degrees of autonomy, up to generating complete pull requests starting from a developer-provided task description. This new mode of operation is poised to change the landscape in an even larger way than code completion LLMs did, making the need to study their impact critical. Also, unlike traditional LLMs, coding agents tend to leave more explicit traces in software engineering artifacts, such as co-authoring commits or pull requests. We leverage these traces to present the first large-scale study (128,018 projects) of the adoption of coding agents on GitHub, finding an estimated adoption rate of 22.20%--28.66%, which is very high for a technology only a few months old--and increasing. We carry out an in-depth study of the adopters we identified, finding that adoption is broad: it spans the entire spectrum of project maturity; it includes established organizations; and it concerns diverse programming languages or project topics. At the commit level, we find that commits assisted by coding agents are larger than commits only authored by human developers, and have a large proportion of features and bug fixes. These findings highlight the need for further investigation into the practical use of coding agents.