Most enterprise AI deployments hit the same wall: the agent you deploy on day one is the same agent you have six months later. It doesn't learn your team's preferred report formats, your triage conventions, or the quirks of your internal tooling. Every new session starts from scratch. NVIDIA and Nous Research are taking a direct swing at that problem with the NemoClaw blueprint for Hermes Agent, a fully open-source stack that lets agents accumulate reusable skills over time, all inside a policy-enforced security sandbox.
The core idea: skills, not retraining
What mainly grows is not model weights, but skills for reusing business procedures and output formats. That distinction matters enormously. This isn't a system that fine-tunes itself in production, which would be both expensive and unpredictable. Instead, good procedures taught by humans are preserved as skills, and combined with memory and session search for use in future sessions.
The agent learns preferences and patterns, writing new memories and skills. The more users work with the agent, the better it gets. Concretely, when you teach Hermes a specific output format through a chat conversation, it writes a SKILL.md file to disk. That file has a YAML frontmatter with a name and description, plus the full format scaffolding as the body. It persists across deployments and can be read, edited, or shared by humans.
Three layers that make it work
The architecture is cleanly split into three concerns:
- Model layer: Uses NVIDIA Nemotron and compatible open models for reasoning, summarization, and task automation. The default is
nvidia/nemotron-3-super-120b-a12b, but Hermes Agent runs unchanged against a self-hosted NVIDIA Nemotron model on NVIDIA NIM, or vLLM when traffic must stay on-prem. - Harness layer (Hermes Agent): Handles team requests, coordinates planning and reasoning, and returns task-ready responses in shared workspaces. This is where skills, memory, and session search live.
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