一篇给 Agent 开发者的系统化梳理,把工作智能体拆成模型加 harness 两层,还给了五种工作形态和五大能力族的分析框架,读个大纲就能对照自己项目查漏补缺。
#Agent评估
The takeaway is that agent self-improvement may be more fragile than reported scores suggest, because evaluation conditions differ sharply from production task streams. Teams building memory-based agents should run multi-seed evaluations, randomise task order, and enrich memory construction with explicit rubrics and feedback. Code and data are released openly, so practitioners can replicate the findings directly.
The framework shifts agent evaluation from single accuracy scores to a three-way trade-off, which changes how teams should size models and routing for production workloads. Builders deploying agents in cost-sensitive markets can use LangSmith evals to run the same quality-versus-cost benchmarking on their own pipelines before committing to a provider.
想测你的 agent 好不好用?LangChain 这个新技能库让你直接用 Codex 或 Claude Code 跑 eval 任务,还能自动检查验证器准不准。