Why This Announcement Matters

AI agents have the potential to automate intricate workflows, diminish response times, and enhance outcomes across diverse industries. In enterprise settings, this translates to copilots that do more than just respond to queries; they can triage cybersecurity alerts, summarize and act on organizational knowledge, coordinate tools like ticketing systems, and ensure human oversight is maintained. In the realm of physical AI, these agents extend their capabilities beyond text, perceiving the world, navigating spaces, and coordinating with machines and robots.

NVIDIA emphasizes two fundamental components driving this transition: new reasoning models designed for language and decision-making, and multimodal models capable of understanding images, videos, and sensor data. Along with deployment tools, these elements aim to make agents more reliable and effective in production settings. According to NVIDIA, organizations such as CrowdStrike, Uber, and Zoom are harnessing these innovations to create agents that address practical challenges in security, operations, and collaboration [source].

What Are AI Agents, Exactly?

AI agents are systems that integrate large models with tools and logic to achieve specific goals. Modern agents go beyond text prediction; they can search enterprise data, call APIs, retrieve documents, write code, schedule tasks, escalate to human operators, and continuously learn from feedback. In practice, an agent is constructed from several essential elements:

• A foundational model tuned for language and reasoning.
• Skills and tools for actions like searching, retrieving, database queries, or automation.
• Memory to maintain context across prolonged sessions and multiple steps.
• Planning and reflection loops to help break down tasks and self-correct when necessary.
• Guardrails, policy enforcement, and evaluations to ensure behavior remains safe, accurate, and accountable.

Organizations also require deployment infrastructures that meet their security, privacy, and performance needs. NVIDIA’s ecosystem addresses this need with models, microservices, and tools capable of operating in the cloud or on-premises. For numerous companies, this hybrid flexibility is critical for dealing with sensitive data and tasks that require low latency [NVIDIA NIM].

Exploring NVIDIA’s Agent Stack: Nemotron and Cosmos

According to NVIDIA, two model families play a crucial role in its strategy:

Nemotron – Language and Reasoning for Enterprise Agents

Nemotron models are designed to enhance an agent’s capacity for planning, reasoning, and following instructions throughout complex workflows. NVIDIA has previously unveiled large Nemotron models focused on data generation and alignment, including open models in the Nemotron-4 family for synthetic data and instruction adherence available in the NGC catalog [NGC Nemotron-4 340B Instruct]. In the context of agents, Nemotron models tackle tasks such as multi-step tool utilization, grounded answers derived from enterprise data, and adherence to policies.

In summary, Nemotron serves as the brain behind text-based agent decision-making: simply ask it to triage a ticket, generate a remediation plan from a runbook, or summarize high-priority alerts, and it orchestrates the required steps and tools to achieve those goals.

Cosmos – Multimodal Reasoning for Physical AI

Cosmos represents NVIDIA’s multimodal reasoning abilities, which focus on perception and action tailored for physical AI applications. Although the company has yet to publish a public model card for Cosmos, its announcement positions it as a reasoning model that can interpret and act based on visual and sensory input in tandem with language [source]. This aligns with NVIDIA’s broader robotics and simulation framework, which includes Omniverse for digital twins and Isaac for robot development [NVIDIA Omniverse] [NVIDIA Isaac].

Practically speaking, a Cosmos-style model aids an agent in perceiving its surroundings, understanding ongoing events, and coordinating safe actions. For instance, in a warehouse setting, an agent could analyze a camera feed, identify an obstruction, redirect a robot, and inform a human operator if necessary.

What CrowdStrike, Uber, and Zoom Are Building

NVIDIA indicates that several companies are already leveraging these capabilities:

CrowdStrike – Faster Security Response and Analyst Copilots

Security teams are inundated with alerts that necessitate quick, accurate decision-making. NVIDIA reveals that CrowdStrike is developing agents to aid in triaging incidents, summarizing evidence, and suggesting remediation steps with increased speed. This builds on CrowdStrike’s ongoing efforts in AI-driven security, including Charlotte AI, a generative security analyst designed to expedite threat investigations [CrowdStrike Charlotte AI]. NVIDIA’s cybersecurity framework, Morpheus, also demonstrates how GPU-accelerated pipelines can identify anomalies and coordinate automated actions in security operations centers [NVIDIA Morpheus].

In this context, Nemotron offers reasoning capabilities over structured telemetry, runbooks, and knowledge bases, while agent workflows connect with tools like EDR, SIEM, and ticketing systems. The aim is to minimize mean time to detect and respond while ensuring that human analysts retain control.

Uber – Operations Optimization and Intelligent Automation

Uber’s platform is intricately designed to match supply and demand in real-time across ridesharing, delivery, and logistics. NVIDIA’s announcement suggests that agents could assist operations teams in simulating scenarios, optimizing routing, and automating support workflows. This aligns with Uber’s long-term investments in machine learning platforms like Michelangelo for production AI [Uber Engineering – Michelangelo], and with NVIDIA’s own GPU-accelerated operations research libraries such as cuOpt for routing and logistics optimization [NVIDIA cuOpt].

Consider an agent that anticipates demand spikes, utilizes an optimization service to adjust supply, and automatically manages driver notifications. Nemotron conducts the planning and decision-making, while the multimodal perception, akin to Cosmos, could make sense of maps, traffic feeds, or telemetry to maintain plans grounded in the real world.

Zoom – Collaboration Copilots and Meeting Intelligence

Zoom has introduced AI Companion features that summarize meetings, draft communications, and boost productivity on its collaboration platform [Zoom AI Companion]. NVIDIA reports that Zoom is among the early innovators building agents with Nemotron and Cosmos to deliver more context-aware, actionable assistance. This could manifest as agents that comprehend multimodal inputs from meetings—audio, video, shared screens—and can follow up with action items, ticket creation, or data retrieval in connected enterprise systems.

NVIDIA’s media and communications AI framework, encompassing technologies like Maxine for audio and video processing, has already facilitated real-time enhancements in conferencing platforms. The reasoning layer adds deeper understanding and ensures safer, policy-compliant actions triggered by these insights.

From Models to Production: What It Takes to Deploy Agents

Creating an agent that operates reliably in a production environment involves more than just a well-designed model. NVIDIA’s ecosystem encompasses models, toolchains, and deployment services intended to simplify the process:

• Models for reasoning and perception – Nemotron for language reasoning and Cosmos for multimodal integration.
• Retrieval and tool utilization – connections to enterprise data, API calls, and orchestration of business workflows.
• Guardrails and policy enforcement – rule-based and learned filters, content safety measures, PII protection, and closed-loop feedback.
• Evaluation and monitoring – scenario-based assessments, offline benchmarks, and runtime metrics to oversee quality, safety, and potential drift.
• Deployment options – cloud, on-premises, or hybrid solutions utilizing NVIDIA NIM microservices for standardized APIs and performance [NVIDIA NIM].

Developers can also leverage NVIDIA NeMo to tailor models and incorporate guardrails, alongside popular open-source frameworks for agent workflows. NeMo offers training, alignment, and safety tools that complement inference microservices [NVIDIA NeMo].

Understanding Physical AI: The Importance of Multimodal Reasoning

Numerous real-world tasks require comprehension of visual and spatial contexts. A robot that detects a spilled package, a vehicle that identifies an obstructed lane, or a drone that observes a safety hazard—all these scenarios necessitate capabilities that extend beyond mere language. This is where multimodal reasoning models come into play.

NVIDIA has heavily invested in simulation and robotics through Omniverse and Isaac to aid teams in designing, testing, and validating agents prior to their deployment in factories, warehouses, retail spaces, and field operations. Digital twins allow for training and safety evaluations in rich virtual environments, while Isaac provides perception, planning, and control systems for robots [NVIDIA Omniverse] [NVIDIA Isaac].

As articulated by NVIDIA, Cosmos fits into this framework as a reasoning layer linking what an agent perceives with its subsequent actions and determining when to request assistance. For safety-critical tasks, this type of grounded, perception-aware planning is indispensable.

Benefits and Considerations for Enterprises Adopting Agents

Integrating AI agents can yield significant advantages, but it also necessitates meticulous design. Here are key benefits and considerations:

• Productivity and response times – agents streamline steps, minimize context-switching, and intelligently route tasks.
• Grounded decision-making – retrieval and tool usage ensure outputs are substantiated by enterprise data and logs.
• Security and compliance – both on-premises and hybrid deployment options, combined with policy enforcement and audit trails.
• Cost efficiency – appropriately scale models and utilize hardware acceleration to meet latency and throughput objectives.
• Human oversight – create human-in-the-loop checkpoints for sensitive or irreversible actions.
• Change management – implement gradual rollouts, measure impacts, and train teams on new workflows.

When executed effectively, agent deployments can transition teams from reactive tasks to proactive, higher-value roles while ensuring robust governance.

How to Start Building with Nemotron and Cosmos

Taking into account NVIDIA’s recommendations and industry best practices, here’s a practical checklist to kickstart your journey:

1. Identify tasks with distinct value and guardrails – for example, summarize and triage security alerts, create follow-up tickets, and escalate when risks are elevated.
2. Select a reasoning model – begin with Nemotron for language-centric workflows; incorporate multimodal perception with a Cosmos-style model for tasks involving visual or sensor data.
3. Connect retrieval and tools – integrate with knowledge bases, logs, and APIs. Define tool schemas with safe defaults.
4. Implement guardrails and policies – enact content safety, PII protection, and domain-specific constraints using NeMo or similar tools.
5. Design for human oversight – determine scenarios requiring human approval and create a clear user interface for review and control.
6. Evaluate both pre- and post-launch – develop scenario-based tests aligned with business objectives rather than generic benchmarks. Continuously track quality and drift.
7. Plan deployment – utilize NIM microservices to standardize inference, opting for cloud, on-prem, or edge solutions according to data sensitivity and latency requirements [NVIDIA NIM].

Verifying the Claims and Context

NVIDIA’s newsroom article cites CrowdStrike, Uber, and Zoom as early adopters of agents developed using the Nemotron and Cosmos reasoning models [source]. This aligns with the broader strategies of these companies:

• CrowdStrike has introduced generative AI initiatives for security analysts, including Charlotte AI [CrowdStrike Charlotte AI].
• Uber has made substantial investments in production ML platforms such as Michelangelo to operationalize AI at scale [Uber Engineering – Michelangelo].
• Zoom offers AI Companion features for meeting summarization, drafting, and productivity, showcasing a move towards more agent-driven collaboration tools [Zoom AI Companion].

On the modeling front, NVIDIA’s Nemotron-4 340B Instruct is listed in the NGC model catalog and is documented as an open model designed for high-quality synthetic data and instruction adherence [NGC Nemotron-4 340B Instruct]. NVIDIA NIM microservices, which standardize model deployment through stable APIs, are also publicly documented and available for enterprise use [NVIDIA NIM]. For physical AI applications, NVIDIA delivers robust stacks for simulation and robotics via Omniverse and Isaac [NVIDIA Omniverse] [NVIDIA Isaac].

While NVIDIA’s newsroom posting does not include technical benchmarks for Cosmos currently accessible to the public, the company has a strong history of publishing model cards and technical reports on its developer and research platforms. As more information becomes available, enterprises should carefully review formal model documentation alongside internal assessments before deploying agents at scale.

Example Agent Workflows You Can Pilot

• Security triage and response – Nemotron analyzes clusters of alerts, retrieves recent detections, drafts a remediation plan, opens a ticket, and requests human approval for containment measures.
• Operational dispatch – an agent forecasts demand spikes, utilizes cuOpt to re-route drivers, sends batch notifications, and monitors telemetry to adjust the plan as necessary.
• Meeting to action – the agent summarizes a Zoom meeting, identifies owners and deadlines, creates tasks in a project management tool, and schedules follow-ups with calendar invites.
• Warehouse safety – a multimodal agent interprets camera feeds, detects blocked aisles, halts nearby robots via Isaac interfaces, and alerts a supervisor with suggested fixes and route changes.

Safety, Security, and Governance

Agent implementations must be designed with safety measures from the outset. Recommended practices include:

• Input and tool validation – never allow untrusted inputs to directly interact with powerful tools or code execution without prior validation and sandboxing.
• Role- and policy-aware prompts – condition agents based on user roles, data access, and organizational policies.
• Content safety and PII management – filter out unsafe content and safeguard sensitive data both at rest and during transit.
• Human-in-the-loop – require approval for high-risk or irreversible actions, such as account suspension or system alterations.
• Continuous evaluation – monitor hallucination rates, tool call success, and metrics associated with business outcomes.
• Auditability – keep logs of prompts, tool calls, and decisions for compliance and post-incident reviews.

NVIDIA’s NeMo and NIM frameworks provide controls for deployment, while organizations should integrate their identity, access, and policy systems to ensure comprehensive governance [NVIDIA NeMo] [NVIDIA NIM].

The Bottom Line

AI agents are advancing from prototypes to real-world applications, and NVIDIA’s Nemotron and Cosmos are positioned as key reasoning engines for both enterprise and physical AI. The initial projects with CrowdStrike, Uber, and Zoom illustrate practical, high-value use cases where agents do more than engage in chat—they perceive, decide, and act with accountability. For leaders assessing this new wave, the strategy is clear: start with targeted workflows, integrate retrieval and tools, enforce safety and oversight measures, and appraise outcomes. The opportunity lies not only in faster responses but in improved decision-making across digital and physical domains.

FAQs

What Is NVIDIA Nemotron?

Nemotron is a series of NVIDIA models geared towards reasoning and instruction-following for agent-based workflows. It is engineered to facilitate multi-step task planning, tool calls, and generate grounded, policy-compliant responses. NVIDIA has released open models in the Nemotron-4 family for instruction following and synthetic data generation available in the NGC catalog.

What Is NVIDIA Cosmos?

Cosmos refers to NVIDIA’s multimodal reasoning capabilities designed for physical AI applications. As articulated by NVIDIA, Cosmos emphasizes perception-informed planning and action, empowering agents to interpret images, videos, and sensory data alongside language.

How Do These Models Deploy in Enterprises?

Organizations can operate models using NVIDIA NIM microservices for consistent APIs and performance, both on-premises and in the cloud. Teams can also leverage NVIDIA NeMo to customize models, implement guardrails, and integrate with retrieval and tool orchestration.

Can These Agents Function on Edge Devices or Robots?

Yes, many agent components can run on edge devices for low-latency perception and control, with heavier reasoning tasks centralized as required. NVIDIA’s Isaac robotics platform and Omniverse simulation environment support the development and testing of such deployments.

How Should Companies Assess Agent Performance?

Create scenario-based evaluations aligned with real workloads, measure tool success and outcome quality, and monitor safety metrics. Combine offline assessments with real-time monitoring to identify drift and regressions following deployment.

Sources

1. NVIDIA Newsroom – Agents with Nemotron and Cosmos
2. NVIDIA NIM – Inference Microservices
3. NVIDIA NeMo – Model Customization and Guardrails
4. NGC – Nemotron-4 340B Instruct
5. NVIDIA Omniverse – Open Platform for Digital Twins
6. NVIDIA Isaac – Robotics Platform
7. NVIDIA Morpheus – Cybersecurity AI Framework
8. NVIDIA cuOpt – Operations Research for Routing
9. CrowdStrike – Introducing Charlotte AI
10. Uber Engineering – Michelangelo
11. Zoom – AI Companion