NVIDIA 展示基于 NemoClaw 的记忆驱动智能体提升企业效率
原标题：Building a Memory-Driven Agent with NVIDIA NemoClaw
AI 摘要
NVIDIA 技术博客介绍了使用 NVIDIA NemoClaw 构建的基于记忆的智能体（Chief of Staff），该智能体通过自模型维护结构化知识层，以提升企业工作流效率。文章展示了该智能体在多个任务上的性能提升，如整体准确率从 82.8% 提升至 90.9%，并分享了五个设计经验，包括上下文维护、证据与知识分离、用户意图优先、用户纠正机制以及安全边界。该智能体还集成了 NVIDIA OpenShell 安全运行时，确保代理操作在沙箱内受治理。
正文节选
Enterprise work spans messages, decisions, projects, and obligations that change over time. An AI agent that starts without this context must reconstruct it before contributing. To provide agents with this necessary context, our team used NVIDIA NemoClaw to build a memory-driven Chief of Staff. It maintains a human-readable knowledge layer called the self model: an agent memory of relevant people, projects, priorities, and working patterns. Scheduled jobs periodically review new activity, track