AWS 开源 UAEF：框架无关的 AI 智能体闭环评估系统
UAEF: A framework-agnostic system for closed-loop evaluation of AI agents
做智能体的朋友可以看看，AWS 这个 UAEF 能把不同框架的运行记录统一打分，内置 36 个指标、7 个维度，还能存下来对比版本防回退。用 LangGraph 还是自研框架都能接，迭代时改提示词有没有效果一目了然。
AWS 发布 Universal Agent Evaluation Framework（UAEF），一个框架无关的智能体评估系统。它把不同框架产生的 trace 统一转换成一种规范格式，再进行打分。
系统内置 36 项指标，覆盖 7 个评估维度，评估结果会被持久化存储，团队据此对比不同版本的智能体表现并捕捉性能回退。所谓闭环，是指评估结果能反哺迭代：改了提示词、换了模型或调整了工具调用逻辑后，可以立即用同一套指标量化前后差异。
对开发者来说，其价值在于摆脱单一框架绑定——无论用 LangGraph、Strands 还是自研框架，trace 都能进入同一评估管线，跨框架对比成为可能。随着智能体从 demo 走向生产，评估基础设施正成为和训练、部署并列的必备环节。
UAEF: A framework-agnostic system for closed-loop evaluation of AI agents
The Universal Agent Evaluation Framework (UAEF) is a framework-agnostic system for evaluating AI agents. UAEF converts each framework's traces into one canonical format, scores them with 36 built-in metrics across 7 dimensions, and stores results so teams can compare agent versions and catch regressions.
This report is categorized under AITOP's "Products" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include UAEF, AWS, 智能体, Agent 评估.
Primary source verification: published directly by AWS Machine Learning Blog with first-party authority.
