A three-layer, eleven-dimension framework for evaluating AI agents
11/07/2026 — 11/07, 19:18·1 sources (all)·1 reports (all)
Key takeaways
- An article proposes a three-layer, eleven-dimension framework for evaluating AI agents: end-to-end outcomes, process trajectories and compon…
Background & analysis
An article titled "Agent 评测怎么做：三层十一维框架与可跑脚本", published on 2026-11-07 at 18:13, outlines how to evaluate agents. It divides the evaluation system into three layers: judging end-to-end results, tracing the process trajectory, and assessing capabilities at the component level. For the question of how to measure agent quality, the article proposes an eleven-dimension evaluation framework.
On the data side, the article cites ClawBench benchmark results showing that the strongest Claude models achieve a completion rate of only 33.3%. At the end, the article includes a runnable script that helps detect failures masked as successful runs.
This is the only available report on the topic so far, with no other updates published yet.
AI-generated from 1 reports · updated 6 hours ago
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The post splits agent evaluation into three layers — end-to-end outcomes, trajectory-level process, and component-level capabilities — and lays out an eleven-dimension scoring framework. It cites ClawBench results in which even the strongest Claude model completed just 33.3% of tasks, and ships runnable scripts for spotting false successes.
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