AI RESEARCH
ContextCov: Deriving and Enforcing Executable Constraints from Agent Instruction Files
arXiv CS.AI
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ArXi:2603.00822v2 Announce Type: replace-cross As Large Language Model (LLM) agents increasingly execute complex, autonomous software engineering tasks, developers rely on natural language instruction files such as AGENTS.md to express project-specific coding conventions, tooling restrictions, and architectural boundaries. However, because these instructions remain passive text, agents frequently violate documented constraints due to context window saturation or conflicting local context.