Artificial intelligence agents are quickly becoming one of the most talked-about developments in enterprise technology. Companies want AI systems that can do more than generate text or analyze data — they want software that can take action, automate workflows, and operate inside business systems. However, with that power comes risk. Organizations are asking a critical question: how can AI agents be deployed without losing control over data, security, and compliance?
At GTC 2026 in San Jose on March 16, NVIDIA CEO Jensen Huang introduced a new solution aimed at answering that question. The company announced the NVIDIA Agent Toolkit, an open-source software stack designed to help developers and enterprises build, manage, and secure autonomous AI agents.
The toolkit is built to address one of the biggest barriers to enterprise adoption: trust. Businesses are interested in agent-based AI, but they need reliable guardrails to ensure that autonomous systems operate safely, follow policies, and do not expose sensitive information. NVIDIA’s new platform focuses on creating those guardrails while still allowing companies to scale AI deployments.
This article explains what the NVIDIA Agent Toolkit is, why enterprise AI agents are difficult to deploy, how OpenShell solves the safety problem, what AI-Q brings to the stack, which companies are adopting the technology, and why NVIDIA is positioning itself as the infrastructure layer for the next generation of enterprise AI.
Why Enterprises Want AI Agents but Hesitate to Deploy Them
In recent years, artificial intelligence has moved from experimental projects to real business applications. Companies now use AI for customer service, analytics, fraud detection, software development, and content generation. The next step is the rise of AI agents, systems that can make decisions and perform tasks automatically.
Unlike traditional AI tools, agents are designed to act rather than just respond. They can:
- Execute workflows
- Access enterprise databases
- Trigger business processes
- Communicate with other systems
- Make decisions based on rules or goals
This makes them extremely useful, but also risky. When AI can act inside enterprise systems, mistakes can have serious consequences.
Organizations worry about:
- Data leaks
- Unauthorized actions
- Compliance violations
- Security breaches
- Lack of audit trails
Because of these concerns, many companies are still cautious about deploying autonomous agents at scale.
NVIDIA’s Agent Toolkit is designed to make these deployments safer and easier.
NVIDIA Agent Toolkit: A Software Stack for Autonomous AI
The NVIDIA Agent Toolkit is an open-source platform that provides tools for building, running, and managing AI agents in enterprise environments.
The goal is to give developers a standard framework for creating agents while ensuring that security, privacy, and policy controls are built in from the start.
According to NVIDIA, the toolkit helps organizations:
- Build custom AI agents
- Connect agents to enterprise data
- Apply security policies
- Monitor agent activity
- Control how agents interact with systems
By providing a unified stack, NVIDIA hopes to reduce the complexity that currently slows down enterprise AI adoption.
The company also wants the toolkit to work across cloud platforms, on-premises systems, and hybrid environments, which is essential for large organizations.
OpenShell: The Core Runtime That Keeps Agents Under Control
At the center of the toolkit is NVIDIA OpenShell, an open-source runtime designed to enforce policy-based security and privacy rules for autonomous agents.
In NVIDIA’s terminology, individual agents are called “claws,” and OpenShell acts as the system that controls how those agents behave.
OpenShell provides:
- Policy enforcement
- Permission management
- Data access controls
- Activity logging
- Security integration
This means agents can operate inside enterprise systems without bypassing existing rules.
At GTC 2026, Jensen Huang described the importance of guardrails for agent-based AI, saying that the industry has reached a turning point where AI is moving beyond generation and reasoning into real-world action.
He explained that employees will increasingly work alongside teams of AI agents that help complete tasks, but those agents must be deployed in a controlled and secure way.
Without strong runtime controls, companies may hesitate to use autonomous systems in sensitive environments.
Security Partnerships Strengthen the Platform
To make OpenShell compatible with existing enterprise security tools, NVIDIA is working with several major technology companies.
Partners include:
- Cisco
- CrowdStrike
- Microsoft Security
- TrendAI
These integrations allow OpenShell to connect with security monitoring, threat detection, and compliance systems that companies already use.
This approach is important because enterprises rarely replace their security tools. Instead, new software must work with existing infrastructure.
By building compatibility with major vendors, NVIDIA is making it easier for organizations to adopt agent-based AI without redesigning their entire security environment.
AI-Q: A Hybrid Architecture to Reduce AI Costs
Another key component of the toolkit is NVIDIA AI-Q, an agentic search blueprint built with LangChain.
AI-Q uses a hybrid architecture that combines different types of models to balance cost and performance.
In this setup:
- Frontier models handle orchestration
- NVIDIA Nemotron models perform research-heavy tasks
This design allows companies to use powerful AI when needed, while relying on more efficient models for routine work.
According to NVIDIA, this approach can reduce query costs by more than 50 percent while still achieving high accuracy. The company says AI-Q performs well on benchmarks such as DeepResearch Bench and DeepResearch Bench II.
Cost control is a major issue for enterprise AI. Many companies start with small pilot projects, only to discover that usage-based pricing becomes expensive when scaled.
By lowering compute costs, NVIDIA hopes to make agent-based AI more practical for large organizations.
Why Cost Matters for Enterprise AI Adoption
One of the biggest surprises for companies using AI is how quickly expenses can grow.
Consumption-based pricing means that every query, request, or computation adds to the bill.
In pilot projects, costs often look manageable. But when AI is used across departments, the total can become difficult to control.
This is especially true for agent-based systems, which may run continuously.
AI-Q’s hybrid design aims to solve this problem by using different models for different tasks.
This allows enterprises to scale AI without losing budget control.
For many buyers, predictable cost is just as important as performance.
Major Enterprise Software Companies Are Joining the Ecosystem
NVIDIA’s Agent Toolkit is not being developed in isolation. The company is working with a large group of enterprise software providers to build integrations.
Partners include:
- Adobe
- Atlassian
- SAP
- Salesforce
- ServiceNow
- Siemens
- Cisco
- CrowdStrike
- Red Hat
- Box
- Cadence
- Cohesity
- Dassault Systèmes
- IQVIA
- Synopsys
These partnerships show that agent-based AI is not limited to one industry. It is expected to affect software development, design, healthcare, manufacturing, finance, and more.
By supporting a wide ecosystem, NVIDIA is positioning the toolkit as a common foundation for enterprise AI.
Salesforce, Atlassian, and ServiceNow Use Cases
Several partners have already announced how they plan to use the toolkit.
Salesforce is building a reference architecture where employees interact with AI agents through Slack. These agents can access both cloud and on-premises data while running on NVIDIA infrastructure.
Atlassian is integrating the toolkit into its Rovo AI strategy, which will bring agent capabilities to Jira and Confluence.
ServiceNow is using the toolkit to support what it calls an Autonomous Workforce of AI Specialists, powered by NVIDIA AI-Q.
These examples show how agents may become part of everyday work tools.
Instead of switching between applications, users could rely on AI agents to handle tasks automatically.
Siemens and IQVIA Show Real-World Deployment
Some companies are already using agent-based AI in production environments.
Siemens introduced the Fuse EDA AI Agent, which uses NVIDIA Nemotron models to automate workflows in electronic design automation. The agent can manage processes from design creation to manufacturing approval.
IQVIA, a company that works with the pharmaceutical industry, has deployed more than 150 AI agents across internal teams and client projects.
These deployments include work with 19 of the top 20 pharmaceutical companies, showing that agent-based AI is already being used in highly regulated industries.
Real-world examples like these help demonstrate that autonomous agents are not just experimental technology.
They are becoming part of enterprise operations.
NVIDIA’s Bigger Goal: Becoming the Infrastructure Layer for AI Agents
The Agent Toolkit is part of a larger strategy.
NVIDIA is not only building hardware for AI, but also software platforms that sit underneath enterprise applications.
The company wants its technology to power:
- AI models
- Agent runtimes
- Data pipelines
- Security controls
- Cloud deployments
In this vision, the Agent Toolkit, OpenShell, Nemotron models, and AI-Q all work together as a stack.
Enterprise software would run on top of that stack, while NVIDIA provides the foundation.
This approach is similar to how operating systems support applications.
If successful, NVIDIA could become the standard infrastructure for agent-based AI.
Multi-Cloud Support Makes Deployment Easier
To support enterprise adoption, the toolkit is available across major cloud platforms.
Supported environments include:
- AWS
- Google Cloud
- Microsoft Azure
- Oracle Cloud Infrastructure
This allows companies to deploy agents without changing their cloud provider.
Multi-cloud support is important because most large organizations use more than one platform.
By working across clouds, NVIDIA reduces the barriers to adoption.
Trust Is the Key to the Future of Agentic AI
The main message behind the Agent Toolkit is that trust is the biggest obstacle to deploying autonomous AI.
Companies want the productivity benefits of agents, but they also need:
- Security
- Compliance
- Transparency
- Cost control
- Reliability
Without these, organizations will limit AI to small experiments.
With proper guardrails, agents can be used in real business workflows.
NVIDIA’s toolkit is designed to provide those guardrails.
The Next Phase of AI Will Be About Action, Not Just Answers
For the past few years, AI has focused on generating content and answering questions.
The next phase is about taking action.
AI agents will:
- Execute tasks
- Manage processes
- Make decisions
- Interact with software
- Work alongside humans
This shift requires new infrastructure, not just new models.
NVIDIA’s Agent Toolkit represents one of the first attempts to build that infrastructure at enterprise scale.
Conclusion
NVIDIA’s announcement at GTC 2026 highlights how quickly enterprise AI is evolving. Companies are moving beyond simple automation toward autonomous agents that can operate inside business systems.
However, trust, security, and cost remain major barriers.
The NVIDIA Agent Toolkit, with components like OpenShell, AI-Q, and Nemotron models, is designed to solve these problems by providing a secure, scalable, and cost-efficient platform for agent deployment.
With support from major enterprise software vendors and cloud providers, the toolkit could become a key part of the next generation of business technology.
As organizations look for ways to use AI safely, the ability to control autonomous agents may determine how fast the agentic era becomes reality.