Designing eval harnesses, judge prompts, and offline/online metrics for LLM and agent systems. By 2026, robust eval is the most reliable way to ship AI products
LLM and agent evaluation is the proof discipline: golden datasets, calibrated LLM-judge pipelines, trajectory scoring for agents, regression gates, and production quality monitoring, the skill that decides whether AI systems are actually good.
Critically short: every serious AI team needs evaluation ownership and few practitioners exist, the most reliable current entry into senior applied-AI work.
Representative data, rubrics tied to real quality, judges calibrated against humans, and statistical honesty on deltas. Skip any one and the eval measures something other than truth.
Agents are judged on process: plan quality, tool selection, argument correctness, error recovery, and bounded behavior, scored over trajectories with trace analysis, not single outputs.