"AI agent disillusionment comes from mistaking POC's for products. Value comes from enterprise solutions backed by real QA, among other things. Evals is test automation for agents, getting them right is critical"

This article explains how to design, build, and maintain automated evaluations (evals) for AI agents. It defines eval components (tasks, trials, graders, transcripts, outcomes, harnesses), compares grader types (code-based, model-based, human), and recommends practices for capability vs. regression suites. The authors give concrete guidance for evaluating coding, conversational, research, and computer-use agents, discuss handling non-determinism (pass@k and pass^k), and provide a pragmatic roadmap for starting and scaling evals. The piece emphasizes reading transcripts, preventing brittle graders, monitoring for eval saturation, and integrating automated evals with production monitoring, A/B testing, and human review.