How to build agents that actually work: A practical guide to evaluating AI

This post details a practical guide to evaluating AI agents, focusing on creating evalsets with real-world examples and defining success metrics like message quality (completeness, personalization, tone, groundedness). It outlines a systematic approach to improving agents by refining instructions, structuring context, tuning parameters, and selecting appropriate LLMs. The post also discusses scaling evaluations through automation and LLM grading, and integrating user feedback. This contributes to the 'Security AI Agents' thread by providing a methodology for ensuring agent reliability and trustworthiness, which is crucial for security applications.