Even with automated evals, only a person can tell why an agent failed. LangChain argues expert judgment is a strategic advantage 🧑‍⚖️ Why human insight is a strategic advantage for building better agents Automated evaluation can flag problems, but it can't explain the cause or the fix. The article lays out three principles for building human review into the evaluation system. 🎯 Principle 1: humans define what "good" looks like Reviewers supply context a trace doesn't contain, such as the standard of quality for the job and what the user was after. Scores with reasoning also expose gaps in the evaluators themselves. 🧵 Principle 2: context makes the judgment hold up Looking at one step misses contradictions with something said several turns earlier. For multi-turn agents, the whole conversation (a thread) is the unit of review. 🔁 Principle 3: turn judgment into evaluation data Labels become ground truth, used to align LLM judges with human scores, update online evaluators, and build golden datasets. In the case study, Fetch Rewards manually checks even product-card images and offer availability. Automation handles scale; the advantage comes from turning expert judgment into a reusable asset. #AIAgents# #LLMEvaluation#