Inference Costs Plummet 90%. Nvidia Partners With LangChain to Define Agent Standards, Vying for New AI Application Entry Points

TradingKey - On July 8, NVIDIA ( NVDA) and AI development framework LangChain jointly released the NVIDIA NeMoClaw Deep Agents blueprint. This move signifies NVIDIA's comprehensive expansion from a pure AI computing power provider to an ecosystem platform that defines enterprise-grade Agent development standards, opening up new growth space for its software business.

The blueprint provides enterprises with an open, customizable, and governable reference architecture for AI agents, aiming to solve core challenges such as controllability, governance, and continuous evolution that enterprises face in agent deployment.

Rather than simply improving model capabilities, this blueprint places greater emphasis on the software engineering capabilities of enterprise-grade Agents. Official data shows that this solution not only achieves leading performance in multiple benchmarks but also reduces Agent inference costs by more than 10 times. It forms a hardware-software synergy with the Blackwell inference platform, further driving down the deployment costs of AI applications.

As generative AI progresses from content generation to autonomous task execution, Agents are becoming the core form of enterprise AI applications. However, enterprises have consistently faced challenges during deployment, such as security, controllability, and difficulty integrating with business processes.

The core of the newly launched blueprint is not to build a new Agent framework, but rather to provide a complete enterprise-grade reference architecture. Based on the collaboration with LangChain, the blueprint adopts an open architecture design, allowing enterprises to have full control over the underlying systems, customize Agent capabilities, and continuously iterate as their business evolves, rather than relying on a closed platform.

Focusing on Agent Governance to Resolve Pain Points in Enterprise-Grade Deployment

Rather than emphasizing "what tasks the Agent can complete," the solution introduced by NVIDIA this time focuses more on how Agents are governed, monitored, audited, and continuously optimized. This capability is particularly critical for highly regulated industries such as finance, healthcare, and government, and it also lowers the barrier for enterprises to deploy Agents at scale.

Harrison Chase, co-founder and CEO of LangChain, stated: "The key to building better agents lies in continuously improving the systems surrounding the model. When teams can synchronously tune memory, tool usage, evaluation, and model behavior, these capabilities generate a synergistic effect. Our collaboration with NVIDIA demonstrates that enterprises can not only achieve powerful performance through an open stack but also maintain control over the agent systems they build."

As Agents take on increasingly high-risk tasks, evolving from question-answering assistants into execution entities capable of taking action within core systems, enterprises' demands for the controllability and security of AI systems are rising.

The open architecture provided by the NeMo Claw blueprint allows enterprises to own the complete technology stack end-to-end, enabling them to customize and continuously improve based on their unique professional advantages, and to run it in any environment, including their own infrastructure, private clouds, and proprietary governance frameworks.

Cost reduction through hardware-software synergy

Cost control has always been the core bottleneck for the large-scale commercialization of AI, and the NeMoClaw blueprint demonstrates significant advantages in this regard.

According to evaluation data released by LangChain, the Nemotron 3 Ultra model equipped with the Deep Agents suite achieved a comprehensive score of 0.86, with a single-task inference cost of only $4.48, whereas the closest competing model cost as much as $43.48—representing a reduction of approximately 90%. This achievement was not driven by retraining the model itself, but rather was realized by optimizing tool-calling strategies, context management mechanisms, and intermediate reasoning workflows.

At the hardware level, Nvidia's Blackwell architecture has already reduced the inference cost per token to about 1/35 of the previous generation's platform through architectural upgrades, significantly improving inference throughput efficiency.

The NeMoClaw blueprint further taps into hardware potential from the software level. Through systematic optimization of Agent task planning, tool calling, context management, and reasoning paths, it allows the same computing power to support more tasks, maximizing the synergy between hardware and software.

This release is a key step for Nvidia in refining its NeMo software ecosystem, addressing shortcomings in the Agent development layer. In recent years, Nvidia has built a complete AI software stack around CUDA, TensorRT, NIM, and NeMo, aiming to become an end-to-end platform covering model training, inference deployment, and enterprise application development.

As Agents become the core carrier of AI applications, development frameworks are emerging as the new gateway to the ecosystem. The partnership with LangChain allows Nvidia to embed its capabilities into enterprise AI workflows via mainstream frameworks, competing for dominance in the Agent era beyond mere infrastructure.

For capital markets, this strategic layout is of great significance. With competition in AI infrastructure maturing, relying solely on GPU sales makes it difficult to sustain valuation expansion, whereas software and platform services offer higher gross margins and customer stickiness.

By perfecting the NeMo ecosystem and extending into Agent standards, Nvidia is evolving from a hardware provider into a full-stack AI ecosystem platform, laying the foundation to capture more software revenue and ecosystem premiums in the future.

Currently, enterprises such as Abridge, Amdocs, and Box have embedded specialized agents into their platforms, and system integrators like EY are expanding Nvidia technology deployment capabilities around the NeMoClaw blueprint to assist clients in customizing, evaluating, and governing agents in high-value workflows.

In the long run, Nvidia's layout is not only about capturing market share in the Agent era, but also about building an unshakeable AI ecosystem barrier.

Through a full-stack solution featuring hardware-software synergy, Nvidia is firmly binding customers to its technology ecosystem, consolidating its core position in the AI industry chain, and preparing fully for the upcoming explosion of AI applications.