A new partnership between LangChain and NVIDIA is fundamentally changing the economics of building AI agents by delivering benchmark-leading performance at one-tenth the cost of competing systems. The NemoClaw for LangChain Deep Agents blueprint, announced in July 2026, combines an open-source model, a tuned agent framework, and a secure runtime to give enterprises full control over their autonomous AI systems while dramatically reducing operational expenses.

What Makes This Blueprint Different From Existing AI Agent Approaches?

For years, enterprises building AI agents have faced a difficult choice: use closed, proprietary systems from major cloud providers and lose control over their technology, or build everything from scratch and shoulder massive engineering costs. The NemoClaw blueprint offers a third path by combining three open-source layers that work together seamlessly.

The blueprint stacks an open model layer using NVIDIA Nemotron 3 Ultra, a tuned agent harness built on LangChain Deep Agents Code, and a governed runtime called NVIDIA OpenShell. This architecture lets teams customize each component for their specific workloads rather than accepting a one-size-fits-all solution. The result is a system where the model, the agent framework, the evaluation process, and the runtime are all optimized together, rather than treated as separate pieces.

How Much Money Can Enterprises Actually Save?

The cost advantage is striking. In LangChain's evaluation suite, NVIDIA Nemotron 3 Ultra paired with the tuned LangChain Deep Agents harness achieved a top performance score of 0.86 at a cost of $4.48 per task. The next closest competing model cost $43.48 for the same performance level, making Nemotron roughly 10 times cheaper to run in production.

This cost reduction matters because it changes how teams approach agent development. When inference is expensive, companies run fewer tests, compare fewer variations, and avoid building specialized agents for specific domains because the operating costs become prohibitive. Lower costs make it practical to run larger evaluation suites, test more variants, and deploy agents tailored to particular business problems.

Steps to Implement an Open Agent Architecture in Your Organization

• Evaluate Your Current Stack: Assess whether your existing agent system is locked into a proprietary platform or if you have flexibility to adopt open components that you can customize and improve over time.
• Define Your Governance Requirements: Determine what policies, security controls, and audit trails your organization needs for agents to operate in regulated environments or handle sensitive data.
• Plan Your Harness Tuning: Work with your team to customize how agents use tools, manage context, and evaluate intermediate steps based on your specific workloads and performance requirements.
• Set Up Continuous Evaluation: Establish a process to run evaluations throughout the agent development lifecycle, both before deployment and in production, to catch regressions and improvements.
• Choose Your Inference Partner: Select a provider that can serve open models like Nemotron with the throughput and latency your agents require at production scale.

Why Does Owning Your Agent Stack Matter for Enterprises?

As enterprises move agents into production, the systems built around the model become valuable intellectual property. Agent memory, workflows, evaluation datasets, harness configuration, and tuning data all reflect a company's unique domain expertise and competitive advantage. In closed ecosystems, teams don't fully control this work or own the improvements they make.

The NemoClaw blueprint gives enterprises ownership of the full agent stack. This means companies can measure agent performance, govern how agents interact with tools and data, and improve their systems over time without depending on a vendor's roadmap or pricing changes. For regulated industries, this transparency and control are essential for meeting compliance requirements and proving to regulators or boards that agents operate safely and predictably.

"Production agents need inference that is fast, reliable, and cost-efficient at scale. We have optimized NVIDIA Nemotron models on Baseten to deliver high throughput and low latency on NVIDIA hardware, so teams get strong price-performance without operating the infrastructure themselves," said Philip Kiely, Head of Developer Relations at Baseten.

Philip Kiely, Head of Developer Relations, Baseten

What Does This Mean for How Agents Will Be Built Going Forward?

The blueprint reflects a broader shift in how enterprises approach AI. Rather than relying on a single vendor's closed model and infrastructure, companies are building custom agents shaped by their own proprietary data, workflows, and business logic. The best system for a given workload optimizes across quality, cost, speed, and governance together, which means a real-time customer support agent will look very different from a coding agent running background tasks.

The announcement has attracted support from a wide ecosystem of partners, including EY, Baseten, Fireworks, Nebius, Crusoe, DeepInfra, and Together AI. These companies help enterprises serve Nemotron models in production and adapt the blueprint for business-critical applications. EY, for example, is building an implementation practice around the software stack to help clients in regulated industries move agentic AI from isolated pilots into production.

"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," declared Jensen Huang, Founder and CEO of NVIDIA.

Jensen Huang, Founder and CEO, NVIDIA

The NemoClaw for LangChain Deep Agents blueprint is available immediately, marking a significant moment in the maturation of enterprise AI. By making it economically viable to run sophisticated agents at scale, the blueprint removes one of the biggest barriers to moving agentic AI from experimental projects into production systems that drive real business value.