How to Write Evaluations for AI Agents: Core Concepts and a Seven-Step Guide

EvalShift has released a guide to building AI agent evaluations, systematically outlining the core differences between end-to-end agent evaluations and traditional single-turn LLM evaluations, alongside a seven-step workflow for developing test suites. The guide emphasizes that agent evaluations must rigorously verify external environment states and critical execution rules to prevent fluent text responses from concealing failures to invoke tools. In practice, it recommends prioritizing code assertions for deterministic rules, complemented by validated LLM-as-a-judge for text quality assessment, and using the pass^k metric to gauge production stability.