Enterprises are moving away from generic AI agents and building their own custom systems that understand their specific workflows, tools, and data. LangChain and NVIDIA announced the NemoClaw for LangChain Deep Agents blueprint, a reference architecture that combines open-source models, specialized software frameworks, and secure runtime environments to help organizations build, evaluate, and deploy advanced agent systems tailored to their business needs.

What Makes Building Your Own AI Agent Better Than Buying One?

As enterprises move agents into production, the systems they build around the model become valuable intellectual property. Agent memory, workflows, traces, model weights, and tuning data are proprietary intelligence specific to the business. Teams need a way to own that work, improve it over time, and run agents with the performance, cost control, and governance their organizations require.

The NemoClaw blueprint demonstrates why customization matters. In LangChain's evaluation benchmarks, NVIDIA Nemotron 3 Ultra, a fully open-weight model, achieved a top performance score of 0.86 at a cost of $4.48 per evaluation. The next closest performing model cost $43.48, making Nemotron 3 Ultra roughly 10 times lower in inference cost on this benchmark. These results reflect customizations made specifically for Nemotron 3 Ultra, where LangChain tuned how the agent uses tools, manages context, and evaluates intermediate steps.

"The way to build better agents is to keep improving the system around the model. Memory, tool use, evaluation, and model behavior compound when teams can tune them together. Our work with NVIDIA shows that enterprises can get strong performance from an open stack while keeping control over the agent systems they're building," said Harrison Chase, Co-founder and CEO of LangChain.

Harrison Chase, Co-founder and CEO of LangChain

For enterprises, the key takeaway is that agent performance improves when teams tune the model and harness together around the tool-use patterns, context requirements, and workflows specific to their business. Lower inference costs also make it practical to run and evaluate more specialized agents in production. Teams can create agents for specific domains, use evaluations and traces to measure performance, and adapt the harness as their workflows change.

How to Build Enterprise AI Agents With NemoClaw

The NemoClaw for LangChain Deep Agents blueprint brings together three essential components for building agents for the enterprise:

• Open-Weight Model Layer: NVIDIA Nemotron 3 Ultra provides the foundation for teams that want to customize model behavior for their domains while improving agent performance and lowering cost.
• Agent Harness Layer: LangChain Deep Agents provides the framework for long-running agents, including planning, tool use, memory, and task execution, with a profile specifically tuned for Nemotron 3 Ultra.
• Secure Runtime Layer: NVIDIA OpenShell provides the runtime environment for secure, governed deployment, helping teams control how agents interact with tools, systems, and data.

Together, these components give teams a tuned agent system that can be deployed, measured, governed, and improved in production. This modular approach means enterprises aren't locked into a single vendor's proprietary system; instead, they can mix and match components based on their specific requirements.

Why This Shift Matters for Enterprise AI Strategy

The announcement reflects a broader industry shift away from one-size-fits-all AI solutions. NVIDIA Founder and CEO Jensen Huang emphasized this direction, stating that the future of enterprise AI won't be monolithic.

"Super agents have arrived. With an open model like NVIDIA Nemotron, a LangChain harness, the NVIDIA OpenShell runtime, and a company's own data, every enterprise can build custom agents that understand its business, use its tools, and turn knowledge into action. The future of AI won't be one-size-fits-all; companies will use AI cloud services and build their own AI, shaped by their proprietary data, know-how, and workflows, and run it safely and securely wherever they operate," said Jensen Huang.

Jensen Huang, Founder and CEO of NVIDIA

This approach addresses a critical pain point for enterprises: control and ownership. When companies build agents using proprietary cloud services, they lose visibility into how their data is used, how the agent makes decisions, and whether the system aligns with their governance requirements. By using open-source models and frameworks, enterprises retain control over their agent systems and can audit, modify, and improve them independently.

The cost advantage is equally significant. At roughly 10 times lower inference cost, enterprises can afford to deploy more specialized agents for different business functions. Instead of one general-purpose agent handling all tasks, a company could deploy separate agents for customer service, internal knowledge retrieval, data analysis, and workflow automation, each optimized for its specific domain.

The NemoClaw blueprint represents a maturation of the AI agent market. Rather than waiting for vendors to build the perfect agent, enterprises now have the tools and reference architecture to build agents that reflect their unique business logic, data, and workflows. This shift from buying to building signals that AI agents are moving from experimental projects to core business infrastructure.