The ARMER Framework: A Holistic Evaluation Benchmark for Autonomous AI Coding Agents

The rapid evolution of Large Language Models (LLMs) has transitioned AI from simple chatbots to autonomous coding agents capable of managing entire repositories. However, as these agents gain more autonomy, the industry faces a critical challenge: objectively measuring reliability in real-world, complex engineering environments. To bridge this gap, I propose the ARMER Framework, a specialized benchmarking methodology designed to move beyond basic syntax checking and evaluate the 'cognitive' depth of AI agents