Toward Instructions-as-Code: Understanding the Impact of Instruction Files on Agentic Pull Requests
arXiv:2606.13449v1 Announce Type: cross Abstract: AI-agents (e.g., GitHub Copilot) collaborate as teammates in different software engineering tasks, including code generation proposed through pull requests (Agentic-PRs). For better agent efficiency, developers create instruction files that guide the AI-agents, including how to navigate the project, locate the right components, run tests, respect best practices, and more. In this paper, we investigate the relationship between the creation of these instructions and the performance of AI-agents in creating better pull requests, which have a highe
The proliferation of AI agents in software development necessitates new methods for guiding their performance and integration into existing workflows.
This research provides insights into how developers can effectively interact with and improve the output of AI agents, directly impacting software development efficiency and quality.
The focus shifts from merely using AI agents to actively engineering their operational parameters through dedicated instruction files, formalizing the 'instructions-as-code' paradigm.
- · Software developers
- · AI agent developers
- · Companies adopting AI in SDLC
- · AI tooling companies
- · Inefficient AI agent implementations
- · Manual code review for trivial tasks
Improved efficiency and quality of code generated by AI agents through structured instructions.
Increased adoption of AI agents in more complex software engineering tasks as their reliability and control improve.
The emergence of specialized roles and tools dedicated to 'AI agent instruction engineering' within software development teams.
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Read at arXiv cs.AI