What if building a powerful AI agent did not mean surrendering control of the stack? NVIDIA NemoClaw brings together open blueprints, tuned models, optimization tools, and a secure runtime so developers can build specialized agents around the harness they already use.
The idea reaches beyond choosing a bigger model. NemoClaw connects the model, the software that directs it, the tools that extend its abilities, and the controls that govern its actions. That combination targets the central challenge of production agents: making them capable enough for real work and safe enough to access enterprise systems and data.
A Full Agent Stack Without a Forced Rewrite
NVIDIA NemoClaw is not a competing harness, and it is not a runtime. It is a collection of open blueprints that assembles an agent stack on top of the harness selected by the developer.
Each blueprint brings together four components:
- A tuned open harness that defines how the agent operates.
- Nemotron open models that provide the reasoning engine.
- NVIDIA NeMo tools for model and agent optimization.
- NVIDIA OpenShell as the secure runtime that governs access and actions.
The blueprints currently support OpenClaw, Hermes Agent, and LangChain Deep Agents as orchestration layers. If a team already builds with one of those harnesses, a NemoClaw blueprint can assemble the surrounding stack without requiring the team to rewrite its agent.
That flexibility matters because the harness is not a minor wrapper around a model. A recent study took apart 11 coding agents and found that every one was built from the same seven parts; the only thing that changed was the size of each part. The study offers a simple idea: an agent is a model plus a harness.
The model does the thinking. The harness is everything else—the software around the model that turns a text generator into a worker you can trust.
Three Harnesses, Three Ways to Work
LangChain Deep Agents provides a customizable harness for complex, multistep tasks. It combines planning, file-based context management, memory, and delegation to specialized subagents, giving developers a structure for agents that need to move through several stages of work.
LangChain tuned its Deep Agents harness for Nemotron 3 Ultra and achieved near-frontier performance at approximately 10x lower cost per run. That result points to the value of tuning the model and harness together instead of treating the model as the only source of performance gains.
Hermes Agent, from Nous Research, is designed to get better with use. It captures patterns as durable skills that can be reused in future work, creating a path for repeated tasks to build on earlier experience.
OpenClaw takes another route, giving agents access to files and applications, the ability to write code, spawn sub-agents, and maintain context across sessions. It runs on your own machine or server and supports long-running sessions, memory, and scheduled tasks.
NemoClaw works with these agent harnesses while letting developers keep their existing setup. The blueprints add model optimization, telemetry, and governance around the harness rather than replacing it.
OpenShell Draws the Safety Boundary
Capability alone does not make an agent ready for production. OpenShell governs access to files, networks, credentials, and tools, keeping agents bound by policy even when memory or retrieved context suggests unintended actions.
That boundary addresses a problem that a smarter model cannot solve by itself. If an agent says a job is done but tests never ran, a smarter model will not help. If it forgets your rules after 20 minutes, a new model will not help.
NVIDIA NeMo Relay also provides a way to observe and improve agent execution. Together, optimization, telemetry, and governance give developers more control over what the agent does and how the system improves.
NemoClaw agents can run across the cloud, data centers, NVIDIA DGX Spark, and NVIDIA RTX PCs and workstations. They can also operate alongside frontier agents such as Claude, Codex, or Cursor, creating room for specialized open agents to work within broader development environments.
The larger shift is clear: developers can choose the harness, tune the model, define the tools, and set the rules for access. NemoClaw turns those choices into an assembled platform instead of leaving each team to connect every piece alone.
As agents move into longer tasks and real systems, the winning stack may not belong to the biggest model alone. It may belong to the teams that can shape every layer around that model—and keep those layers under their control.
Based on
- Build Specialized Agents on a Stack You Control with NVIDIA NemoClaw — developer.nvidia.com
- The Harness Makes The Agent — forbes.com
- Jensen Huang Says Open Models Can Beat The Frontier. Here’s How — forbes.com
- Cloudera Launches Cloudera Blueprints, Bringing NVIDIA Agent Toolkit to Enterprise Data Anywhere | Markets Insider — markets.businessinsider.com
- Physical AI: When AI Stops Generating And Starts Acting On The World — forbes.com
