AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development

TL;DR

This study empirically evaluates the impact of autonomous coding agents on open-source software development, revealing initial velocity boosts but persistent quality risks, emphasizing the need for safeguards and strategic deployment.

Contribution

It provides the first longitudinal causal analysis comparing autonomous coding agents with IDE-based AI assistants, highlighting their effects on development velocity and software quality.

Findings

Large velocity gains when agents are the first AI tool used

Minimal or short-lived velocity improvements with prior IDE AI use

Persistent increase in static-analysis warnings and cognitive complexity

Abstract

Large language model (LLM) based coding agents increasingly act as autonomous contributors that generate and merge pull requests, yet their real-world effects on software projects are unclear-especially compared with widely adopted IDE-based AI assistants. We present a longitudinal causal study of agent adoption in open-source repositories using staggered difference-in-differences with matched controls. Using the AIDev dataset, we define adoption as the first agent-generated pull request and analyze monthly repository-level outcomes spanning development velocity (commits, lines added) and software quality (static-analysis warnings, cognitive complexity, duplication, and comment density). Results show large, front-loaded velocity gains only when agents are the first observable AI tool in a project; repositories with prior AI IDE usage experience minimal or short-lived throughput increases. In contrast, quality risks are persistent across settings, with static-analysis warnings and cognitive complexity rising by roughly 18% and 39%, indicating sustained agent-induced technical debt even when velocity advantages fade. These heterogeneous effects suggest diminishing returns to AI assistance and highlight the need for quality safeguards, provenance tracking, and selective deployment of autonomous agents. Our findings establish an empirical basis for understanding how agentic and IDE-based tools interact, and motivate research on balancing acceleration with maintainability in AI-integrated development workflows. The replication package for this study is publicly available at https://github.com/shyamagarwal13/agentic-coding-impact.