ContextCov: Deriving and Enforcing Executable Constraints from Agent Instruction Files

TL;DR

ContextCov is introduced, a framework that transforms passive Agent Instructions into active, executable guardrails and extracts natural language constraints and synthesizes enforcement checks across three domains: static AST analysis for code patterns, runtime shell shims that intercept prohibited commands, and architectural validators for structural and semantic constraints.

Abstract

As Large Language Model (LLM) agents increasingly execute complex, autonomous software engineering tasks, developers rely on natural language Agent Instructions (e.g., AGENTS.md) to enforce project-specific coding conventions, tooling, and architectural boundaries. However, these instructions are passive text. Agents frequently deviate from them due to context limitations or conflicting legacy code, a phenomenon we term Context Drift. Because agents operate without real-time human supervision, these silent violations rapidly compound into technical debt. To bridge this gap, we introduce ContextCov, a framework that transforms passive Agent Instructions into active, executable guardrails. ContextCov extracts natural language constraints and synthesizes enforcement checks across three domains: static AST analysis for code patterns, runtime shell shims that intercept prohibited commands, and architectural validators for structural and semantic constraints. Evaluations on 723 open-source repositories demonstrate that ContextCov successfully extracts over 46,000 executable checks with 99.997% syntax validity, providing a necessary automated compliance layer for safe, agent-driven development. Source code and evaluation results are available at https://anonymous.4open.science/r/ContextCov-4510/.

Figures (5)

• Figure 1: Excerpt from VS Code's copilot-instructions.md, a production Agent Instruction file used to guide AI coding assistants. Lines 1--6 define process constraints for the TypeScript build workflow, lines 8--22 specify source-level coding conventions, and lines 24--38 establish architectural boundaries and design principles.
• Figure 2: Architecture of ContextCov. The check generation phase comprises Extraction (parsing Agent Instructions into a Markdown AST and refining constraints via LLM) and Synthesis (routing constraints to domain-specialized code generators). The Enforcement phase executes checks through a Process Interceptor, Universal Static Linter, and Architectural Validator.
• Figure 3: Extraction pipeline flow across 723 repositories. The pipeline processed 51,490 AST leaf nodes, extracted 48,921 non-empty segments, and synthesized 46,316 checks across four enforcement domains. Source accounts for 28%, Process for 26%, Architectural Deterministic for 20%, and Architectural Semantic for 26%.
• Figure 4: Distribution of violations across repositories by domain. The distribution exhibits high variance, with some repositories having thousands of violations while others have none, reflecting diversity in Agent Instruction specificity and codebase compliance.
• Figure 5: Violation concentration across checks. A small number of constraints account for the majority of violations, suggesting that certain constraint types are systematically violated across codebases.