Building Specialized Agents with NVIDIA NemoClaw Blueprints
Deploy secure, optimized autonomous agents on your own controlled infrastructure stack.
1NemoClaw Platform Architecture
NemoClaw provides open blueprints that integrate tuned agent harnesses, Nemotron models, and NeMo optimization tools. It utilizes the OpenShell secure runtime to create a full-stack agent platform. Developers can maintain their existing orchestration layers, such as OpenClaw or LangChain Deep Agents, while adding critical governance and telemetry capabilities to their production workflows.
2Optimizing Harness Performance
LangChain tuned its Deep Agents harness specifically for the Nemotron 3 Ultra model. By adjusting prompts and middleware without full retraining, they achieved near-frontier performance. This optimization reduces costs by approximately 10x per run. The resulting model profile is currently available on GitHub for developers to integrate into their own agent stacks.
3Governance via OpenShell
OpenShell acts as the security layer, governing agent access to files, networks, and credentials. It ensures agents remain bound by strict policies even when retrieved context suggests unintended actions. This runtime environment keeps sensitive credentials outside the sandbox, preventing unauthorized data exfiltration while maintaining control over the agent's interaction with enterprise systems.
4Memory-Driven Agent Recipes
The NemoClaw repository features a Chief of Staff recipe that utilizes persistent, human-readable memory. This allows agents to track projects, priorities, and working patterns across sessions. By avoiding the need to reconstruct context from scratch, the agent operates more efficiently. It is implemented as a deployable Hermes profile within the NemoClaw framework.
5Telemetry and Auditability
NeMo Relay is integrated natively into Hermes sessions to manage trace spans at real session boundaries. It captures data during session resets or expirations, exporting trajectories for audit and further optimization. This observability ensures that developers can monitor agent behavior effectively, providing the necessary visibility to refine performance and security policies over time.
6Flexible Deployment Environments
NemoClaw agents are designed for versatility across diverse hardware environments. They run seamlessly in the cloud, data centers, NVIDIA DGX Spark, and local NVIDIA RTX workstations. This flexibility allows a clinical agent in a hospital and a simulation agent in electronic design automation to operate on the same stack while using different tools.
7Interoperability with Frontier Models
NemoClaw agents are built to function alongside frontier models like Claude, Codex, or Cursor. These larger systems can delegate domain-specific subtasks to NemoClaw-powered specialists running on open models. This hybrid approach enables organizations to leverage specialized, cost-effective agents on owned infrastructure while maintaining connectivity with broader, general-purpose AI systems for complex tasks.
8Strategic Implementation Path
Developers should start by selecting a NemoClaw blueprint that matches their current harness, such as Hermes Agent or LangChain. By customizing tools, models, and OpenShell policies, teams can build specialized agents on a stack they fully control. This modular approach balances the need for high-performance AI with enterprise-grade security and operational autonomy.