AI coding agents may be getting bad instructions from ‘smelly’ config files
AI coding agents are becoming critical to software development, but the configuration files that guide them, such as Agents.md or Claude.md, can be “smelly.” That means they can contain structural flaws, redundancies, or counterproductive workflows that bloat context, waste tokens, and make coding agents less reliable. Researchers from the Department of Computer Science at Brazil’s Federal University of Minas Gerais hope to shed light on this problem, presenting what they call the “first catalog of smells” for coding agent configuration files. The most odorous? Lint and skill leakage, context bloat, and conflicting instructions. “Our results show that these smells are widespread in practice,” the researchers wrote. Consequently, they “may directly influence how coding agents interpret project conventions, prioritize instructions, and perform development tasks.” Smelly configs in the harness make models misbehave Agents like Claude Code, Codex, Cursor, and Gemini are increasingly taking