NVIDIA 博客：从工具调用到任务完成的 AI Agent 评估方法
原标题：How to Evaluate AI Agents From Tool Calls to Task Completion
AI 摘要
NVIDIA 技术博客发文梳理 AI Agent 评估方法的演进：从早期静态任务与单次函数调用评分（如 BFCL），转向需要完整执行环境、跨多步追踪状态的全任务评估。文章提出步骤级（过程评分）与端到端（结果评分）两层评分体系，并给出任务成功率、一致性、工具调用精度、参数准确率、每成功任务步数与成本等指标，强调不同基准在任务复杂度、状态性和验证方法上的差异会导致结果不可比。
正文节选
When you ship an AI agent, the key question is whether it can execute a chain of work across dozens of sequential tool calls against a live environment, and recover when a step fails. Scoring whether the model sounds right tells you almost nothing about whether the work finished. That gap is why agent evaluation has had to evolve from scoring a single function call to scoring an entire task, with tool calling as the connective tissue underneath. This post traces that arc and explains why nearly