ContextCov: Deriving and Enforcing Executable Constraints from Agent Instruction Files

2026-02-28

AI Summary

This work addresses the challenge that large language model (LLM) agents in software engineering tasks often deviate from instructions due to context limitations or legacy code, leading to subtle policy violations and technical debt. To mitigate this, the paper introduces the first proactive compliance assurance framework that automatically extracts and synthesizes multi-dimensional, executable constraints from natural language agent instructions. The framework enforces compliance through a triad of mechanisms: abstract syntax tree (AST)-based static analysis, runtime interception of shell commands, and architecture-level semantic validation. Evaluated across 723 open-source projects, the approach successfully generated over 46,000 check rules with a syntactic validity rate of 99.997%, significantly enhancing the safety and adherence to specifications in LLM-driven software development.

Technology Category

• Natural Language Processing: Safety and Robustness
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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/.

Problem

• Context Drift
• Agent Instructions
• LLM agents
• technical debt
• compliance

Innovation

• ContextCov
• executable constraints
• Agent Instructions
• context drift
• LLM agents