Hardware
Building a Memory-Driven Agent with NVIDIA NemoClaw
NVIDIA NemoClaw delivers a memory‑driven Chief of Staff agent that constructs a structured self‑model (people, projects, priorities) stored in Markdown pages, with an SQLite ledger recording obligatio
NVIDIA NemoClaw delivers a memory‑driven Chief of Staff agent that constructs a structured self‑model (people, projects, priorities) stored in Markdown pages, with an SQLite ledger recording obligations separately from judgment to improve reasoning. Deterministic code and intent gates prioritize user‑specified goals over short‑term urgencies, while corrections are logged through an append‑only audit trail that refines a readable preference policy. Evaluation on the Agent Memory Benchmark shows accuracy rising from 82.8 % to 90.9 % and fact tracking from 60 % to 100 %, outperforming a baseline RAG model; runtime safety is achieved via NVIDIA OpenShell sandboxing.
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Source: NVIDIA Developer | 2026-09-04