Beyond Task Completion: Revealing Corrupt Success in LLM Agents through Procedure-Aware Evaluation

Large Language Model (LLM) agents are being used in critical applications, but existing evaluation methods only assess task completion, not the process used to achieve it. A new framework, Procedure-Aware Evaluation (PAE), has been developed to address this limitation by formalizing agent procedures as structured observations and analyzing consistency relationships between observations, communications, and actions. This approach enables a more comprehensive evaluation of LLM agents, revealing potential inconsistencies and flaws in their decision-making processes¹. The PAE framework has significant implications for the development and deployment of LLM agents in high-stakes settings, where flawed decision-making can have serious consequences. By providing a more nuanced understanding of LLM agent behavior, PAE can help identify potential security vulnerabilities and improve overall system reliability. This matters to practitioners because it highlights the need for more rigorous evaluation methods to ensure LLM agents are trustworthy and effective in real-world applications.