Agent-evals: Metacognitive scoring and boundary testing for LLM coding agents (thinkwright.ai)

A new tool, Agent-evals, has been introduced to enhance the performance assessment of Large Language Model (LLM) coding agents. This tool implements metacognitive scoring and boundary testing, allowing developers to identify and address potential issues in agent behavior, such as improper confidence levels and out-of-scope responses. Agent-evals conducts both static analyses to identify overlaps and gaps in agent capabilities and dynamic boundary probes to evaluate agent responses in real-time. It generates out-of-scope questions to rigorously test agents and provide scores for refusal health, calibration, and consistency. The significance of Agent-evals lies in its ability to improve the reliability of LLM agents, making it easier for teams to manage multiple AI agents that may claim overlapping domains or inconsistently respond to queries. By employing metrics like Jaccard similarity for overlap analysis and conducting live tests without requiring API calls or credentials, this tool streamlines the evaluation process for AI developers. Its compatibility with major LLM providers ensures that it can be integrated into existing workflows, ultimately enhancing the precision and accountability of AI systems in coding tasks.