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 obligations, and incorporate user decisions over time. Our experience shows that useful agent memory requires structure, selective retrieval, and governance—not just storage.

This post demonstrates how a memory-driven agent built with NVIDIA NemoClaw can improve productivity in real-world enterprise workflows.…