ContextCov: Deriving and Enforcing Executable Constraints from Agent Instruction Files

TL;DR

ContextCov transforms natural language instructions into executable constraints to ensure autonomous software engineering agents adhere to project-specific rules, reducing violations and technical debt.

Contribution

It introduces a novel framework that compiles passive instructions into active guardrails, combining static, runtime, and structural checks for improved compliance.

Findings

Achieves 88.3% constraint compliance, outperforming baselines.

Reduces feedback cost by 3.4 times compared to prompt-only methods.

Maintains functional correctness while enforcing constraints.

Abstract

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. In autonomous settings without real-time human supervision, such violations rapidly compound into technical debt. To ground autonomous agents in repository constraints, we introduce ContextCov, a framework that transforms passive natural language instructions into executable guardrails. Unlike prompt-only or reflection-only compliance approaches, ContextCov compiles documented constraints into three complementary checks: static AST queries for code patterns, runtime shell shims that intercept prohibited commands, and architectural validators that enforce structural rules. Acting as an automated, continuous reviewer, ContextCov intercepts agent actions and returns immediate, reproducible violation traces, enabling self-correction before non-compliant changes are finalized. We evaluate ContextCov on SWE-bench Lite (12 repositories, 300 tasks). Compared to prompt-only and LLM reflection baselines, ContextCov achieves 88.3% constraint compliance (vs. 67.0% and 50.3%) with 3.4x lower feedback cost, while maintaining functional correctness. The source code and evaluation results are available at https://github.com/reSHARMA/ContextCov.