AWS introduces Skill-level evaluation in Strands Evals and Bedrock AgentCore, adding three-layer checks — Skill Invoked, Skill Selection Accuracy, and Skill Instruction Following — to verify agents actually load, choose, and execute skills correctly, moving skill validation into CI/CD pipelines like unit tests.
AWS introduces skill-level evaluation in Strands Evals and Bedrock AgentCore, enabling three-layer checks — invocation, selection accuracy, and instruction following — that treat SKILL.md files as auditable engineering components integrated into CI/CD pipelines, shifting agent evaluation from final answers to execution traces.
Meituan's Turing team shares a comprehensive framework for evaluating AI agents, covering multi-layered assessment (result, process, efficiency, risk), human-machine alignment via binary rubrics, seed test sets, expert knowledge integration, and the shift toward infrastructure for long-horizon agents with task-based evaluation harnesses.
The article examines why many believe programmers lose their edge after working at foreign firms, contrasting domestic and overseas evaluation standards, tech stack differences, growth paths, and the trade‑offs between rapid skill breadth and deep, stable expertise, while sharing personal experiences.
This article presents a complete, data‑driven workflow for creating, instrumenting, offline evaluating, analyzing bad cases, and iteratively optimizing Skills on Alibaba Cloud AgentLoop, enabling developers to quantify quality, track improvements across versions, and reliably deliver high‑quality AI Agent capabilities.
Technical strength is not a fixed metric but a problem‑solving ability that varies by domain, complexity, and depth, and this article outlines a practical methodology—covering hard and soft skills, from points to bodies—to assess engineers during hiring, promotion, and daily work.
