arXiv:2606.20512v1 Announce Type: cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test suite, which workflows have historically led to wrong fixes) that does not exist in the code itself. Engineers typically maintain \texttt{AGENTS.md} files to supply this context as instructions for coding agents, but whether they help is contested: recent studies disagree on whether LLM-generated guidance improves or harms agent performance. In this paper we show that how the guidance is produced is the dec
This paper addresses a critical, ongoing challenge in AI agent development concerning effective guidance for complex coding tasks, as LLM capabilities rapidly advance and deployment becomes more widespread.
Effective coding agents promise substantial productivity gains, and understanding how to optimally guide them is crucial for unlocking their full potential and addressing current limitations.
This research provides a methodology ('probe-and-refine tuning') for generating more effective repository guidance, potentially leading to more reliable and performance-enhanced coding agents.
- · AI software developers
- · Large language model providers
- · Enterprises adopting AI agents for software development
- · Software engineers using agent tools
- · Manual software debugging services
- · Inefficient software development pipelines
Improved performance and reliability of AI-powered coding agents, leading to faster software development cycles.
Increased adoption of AI agents across various software engineering functions, automating more complex tasks.
A shift in the role of software engineers towards higher-level architecture, oversight, and refinement of agent-generated code.
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