LangChain and NVIDIA Release NemoClaw Deep Agents Blueprint for Enterprise AI Agents

LangChain and NVIDIA presented a joint blueprint NemoClaw Deep Agents — a reference architecture for building managed enterprise-level AI agents. The solution combines three components: the open LangChain Deep Agents Code framework, the NVIDIA Nemotron 3 Ultra model and the OpenShell execution environment.

What the blueprint consists of

Deep Agents Code is a LangChain framework for building agents capable of multi-step planning and sequential tool use, rather than just conversing with users. Nemotron 3 Ultra is a model from the open Nemotron family, which NVIDIA promotes as the foundation for agentic and reasoning tasks in the corporate environment. OpenShell acts as a managed execution environment through which the agent gains access to system resources and external tools under operator control.

• NemoClaw Deep Agents blueprint — a joint project of LangChain and NVIDIA
• Combines the Deep Agents Code framework, Nemotron 3 Ultra model and OpenShell execution environment
• Positioned as a solution for "open and managed" (governed) corporate agents

Why business needs managed agents

As companies transition from chatbots to autonomous agents capable of executing chains of actions without constant human oversight, the question of manageability comes to the forefront: who is responsible for the agent's actions, how to audit its decisions and restrict access to sensitive systems. This is exactly what the term "governed" in the blueprint announcement describes — it is about built-in control mechanisms, not just a set of tools for assembling an agent.

LangChain has remained for several years one of the most used open frameworks for building applications on large language models, and with the spread of agentic scenarios, the company has made a bet on the deep agents pattern — agents with hierarchical task planning, in contrast to simple linear chains of tool calls. NVIDIA, in turn, promotes the line of open Nemotron models as an alternative to competitors' closed APIs for companies that need control over model weights and the ability to deploy it in their own infrastructure.

The role of observability and audit

LangChain already has a separate product LangSmith — a platform for tracing, logging and evaluating agent behavior, which the company promotes as a mandatory layer for running agents in production, not just at the development stage. It is logical to expect that such mechanisms of tracing agent decision reasoning and tool calls formed the basis of the "manageability" that LangChain and NVIDIA claim in NemoClaw Deep Agents: without recording every step of reasoning and tool calls, it is impossible to either investigate an incident or prove to a regulator that the agent acted within the scope of its authority.

How the blueprint differs from other agentic stacks

There are already several competing approaches to building corporate agents on the market — from frameworks like Microsoft AutoGen and CrewAI to closed agentic SDKs of major labs. Most such solutions are tied to cloud models with closed weights. LangChain and NVIDIA's bet on the open Nemotron 3 Ultra model and a dedicated manageability layer via OpenShell is aimed primarily at companies with audit requirements, source code accessibility of the model and deployment in their own circuit — for example, in the financial sector, public sector or regulated production.

What this means

The partnership of LangChain and NVIDIA consolidates the trend toward standardization of the corporate stack for AI agents: the framework developer unites with the model manufacturer and compute infrastructure provider to offer business a ready and managed bundle instead of self-assembly of an agent from disparate components.