A growing consensus in the AI engineering community holds that evaluating LLM agents purely on final outputs misses critical aspects of performance. This Chinese tech blog post highlights the emerging practice of trajectory-level evaluation, which examines reasoning steps, tool usage patterns, and error recovery behaviors. For teams building production agent systems, this shift from output-only metrics to process-level assessment is essential for debugging, safety, and continuous improvement. The post reflects a broader industry trend toward more nuanced agent quality frameworks, moving beyond simple accuracy to capture the full decision-making chain. Developers should consider integrating trajectory evaluation into their CI/CD pipelines for agent-based applications.
This post argues that evaluating LLM agents solely on final answers is insufficient; trajectory-level evaluation provides deeper insight.