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2026-09-16 · Source：KDNuggets
KDNuggets outlines a four-part approach to agent evaluation: design clear tasks, choose the right graders, build a reliable eval harness and track performance changes over time.
A technical guide published by KDNuggets walks practitioners through the process of building effective evaluations, or 'evals,' for AI agents. The article organizes the work around four core elements: designing clear tasks, choosing the right graders, building a reliable eval harness, and tracking changes over time. Together, these steps form a framework intended to make agent behavior measurable rather than impressionistic.
The guide positions task design as the foundation of any evaluation effort. Effective evals begin with clearly designed tasks, on the premise that poorly specified tasks undermine the usefulness of whatever scores they produce. Once tasks are defined, the next decision concerns graders — the components that judge outputs. The article emphasizes choosing the right graders for the job, since the grader effectively determines what an evaluation measures and rewards.
Beyond task and grader design, the source stresses the importance of a reliable eval harness — the scaffolding that runs evaluations consistently. A dependable harness is presented as a prerequisite for trustworthy results, ensuring evaluations execute the same way each time they are run. The final element is temporal: tracking changes over time, so teams can observe how agent performance shifts as prompts, models, or underlying systems are updated.
For enterprise teams deploying agents in production, the guide's underlying message is that evaluation should be treated as engineering infrastructure rather than an afterthought. The combination of well-specified tasks, appropriate graders, a stable harness, and longitudinal tracking offers a structured path to understanding whether agents perform as intended — and whether any given change improves or degrades them before it reaches end users.
Mots-clés: AI Agents · Evals · Model Evaluation · Eval Harness · Graders · AI Testing
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