NVIDIA completes the AI software stack puzzle: teams up with LangChain to release Agent blueprint, inference costs drop tenfold.

```

After establishing a leading advantage in AI computing infrastructure, Nvidia is further shifting its competitive focus towards the software ecosystem.

On July 8th, Nvidia jointly released the NVIDIA NeMoClaw Deep Agents blueprint with AI development framework LangChain. This blueprint provides enterprises with an open, customizable, and governance-enabled reference architecture for AI agents, aiming to solve core challenges such as controllability, governance, and continual evolution in enterprise agent deployment.

Compared to simply improving model capabilities, this blueprint emphasizes the software engineering capabilities of enterprise-level agents. Official data shows that the solution not only leads in multiple benchmark tests, but also reduces agent inference costs by more than 10 times, enabling software-hardware synergy with the Blackwell inference platform to further compress AI application deployment costs.

For the market, this means Nvidia is completing the last piece of the NeMo software ecosystem puzzle, extending from an AI computing supplier to an ecosystem platform and defining enterprise-level agent development standards, opening up new growth opportunities for software business.

From Agent "Execution" to Agent "Governance"

As generative AI advances from content creation to autonomous task execution, agents are becoming the core form of enterprise AI applications, but deployment continues to face challenges in security, controllability, and integration with business processes. The newly released blueprint focuses not on building another agent framework, but on providing a complete enterprise-level reference architecture.

Based on cooperation with LangChain, the blueprint uses an open architecture design, allowing enterprises full control over the underlying systems, custom agent capabilities, and iterative development alongside business growth, instead of relying on closed platforms.

Compared to emphasizing "what tasks agents can accomplish," this solution pays more attention to how agents are governed, monitored, audited, and continuously optimized. This capability is particularly crucial for highly regulated industries such as finance, healthcare, and government, and also lowers the threshold for large-scale agent deployment in enterprises.

Software Optimization Combined with Blackwell, Inference Costs Drop Another Order of Magnitude

The acceleration of agent commercialization depends critically on inference costs.

Nvidia's latest NeMo Claw Deep Agents blueprint, while maintaining leading performance, can reduce agent inference costs by more than 10 times. This cost reduction is evident in synergy with the Blackwell platform.

According to LangChain's published evaluation results, Nemotron 3 Ultra equipped with LangChain Deep Agents achieved a composite score of 0.86, with inference costs of only $4.48 per task; while comparable competitor models run up to $43.48 per task, a drop of about 90%. Officials stated this advantage is due not only to the model itself, but also to joint optimization of tool invocation, context management, and intermediate inference processes.

At the hardware level, Blackwell-based next-generation inference systems have significantly reduced single-token inference costs through architectural upgrades; in some scenarios, costs are down to about 1/35 of the previous generation platform, with much improved inference throughput efficiency.

This blueprint further optimizes agent execution processes at the software level, covering key links such as task planning, tool invocation, context management, and inference path minimization, enabling the same computing power to handle more agent tasks and fully unlock the potential value of underlying GPUs.

Completing the NeMo Software Ecosystem, Competing for the Entry Point of AI Applications

From a product perspective, the NeMoClaw Deep Agents blueprint fills the gap in the NeMo ecosystem at the agent development layer, making Nvidia's software ecosystem increasingly complete. In recent years, Nvidia has continued to build the AI software stack around CUDA, TensorRT, NIM, and NeMo, aiming not just to sell GPUs but to become a full-chain platform for model training, inference deployment, and enterprise application development.

As agents become important carriers for AI applications, development frameworks are emerging as new ecosystem entry points. The partnership with LangChain shows Nvidia is leveraging mainstream frameworks to embed its capabilities into enterprise AI workflows, and competing for discourse power in the agent era beyond infrastructure.

For the capital market, this also has strategic significance. As AI infrastructure competition matures, relying solely on GPU sales can hardly sustain valuation expansion, while software and platform services boast higher gross margins and stickiness.

By improving the NeMo ecosystem and extending towards agent standards, Nvidia is evolving from an infrastructure supplier to a full-stack AI ecosystem platform, laying the foundation to capture more software revenue and ecosystem premium during the AI application boom cycle.

Risk warning and disclaimerThe market has risks, investment needs caution. This article does not constitute personalized investment advice, nor does it consider the specific investment goals, financial situation or needs of individual users. Users should consider whether any opinions, viewpoints or conclusions in this article are suitable for their specific circumstances. Investment based on this is at your own responsibility. ```