How to improve agent skills with tracing and evals

The text discusses a process for optimizing AI agent skills by focusing on efficiency gains while ensuring response completeness, using a structured evaluation methodology. Initially, an agent skill was developed that significantly reduced latency, token usage, and costs, but it also compromised the completeness of responses. This regression was identified through a completeness evaluator, which highlighted missing critical information in the streamlined responses. By employing a long-running agent and an iterative evaluation process using a fixed dataset, the author was able to refine the skill to maintain efficiency gains while improving completeness beyond the original baseline. The text emphasizes the importance of thorough evaluation over subjective judgment and suggests a systematic approach: tagging variables for clear comparisons, selecting appropriate evaluators, and iterating improvements based on evaluation scores. The document concludes by underscoring that agent skills require rigorous evaluation to ensure that enhancements in efficiency do not inadvertently diminish the utility and effectiveness of the AI agent's responses.

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