Tag: NVIDIA

  • Harness Engineering, Orchestration, and Compound Agents: The Enterprise AI Vocabulary You Need To Know

    Harness Engineering, Orchestration, and Compound Agents: The Enterprise AI Vocabulary You Need To Know

    Most enterprise AI practitioners are running multiple AI models. The question was never how many models or whether they were open models.  The question that’s harder to answer — and the one that determines whether your AI investments compound or fragment — is which system those models are part of, and what you are actually evaluating when a vendor puts a platform in front of you.

    At NVIDIA‘s GTC, Jensen Huang convened a session with the CEOs of Cursor, Perplexity, LangChain, Reflection AI, Thinking Machines Lab, and several others building at the edges of the AI ecosystem. What they described isn’t a debate about models. It’s a map of how AI systems are being assembled — orchestration layers, agent harnesses, multi-component architectures, and specialized vertical systems that combine foundation models with proprietary data and domain logic. Vendors are no longer selling model access. They’re selling systems. Understanding the components of those systems is what makes the difference between a well-matched procurement decision and an expensive one.

    Here’s the updated map.

    The Model Is a Component. The System Is the Product.

    Jensen Huang opened the session with a distinction worth internalizing: a model can be a technology or not a product. He said that ChatGPT is a product. The model underneath it is a technology that someone assembled into that product.

    This reframe is useful for enterprise buyers evaluating vendor offerings. When a vendor presents an AI platform — whether that’s Perplexity Computer, an agentic desktop assistant designed to function as an autonomous coworker, or a vertical industry system built on top of foundation models — you’re not evaluating the model. You’re evaluating everything assembled around it: how the system connects to your data, what tools it can invoke, how it manages memory and context across tasks, what guardrails constrain its actions, and how it handles the handoff between automated steps and human oversight.

    The model is the engine. The system is the car. And you’re buying the car.

    Michael Truell, CEO of Cursor, added a structural observation that clarifies why vendor evaluation has gotten more complex. For years, he said, there were two groups. There were foundation-model companies that built large general-purpose models and sold API access.  Or application companies that built models into their products or on top of them. Truell argues a third category is now well established — companies that combine the best foundation models available through APIs with their own purpose-built models and proprietary domain knowledge, assembled into a specialized vertical system. It’s not a foundation model or application layer. It’s both, plus the integration work that makes them useful together.

    When you evaluate a vendor in this third category, the question isn’t just which model it runs on. It’s whether the vertical specialization, the data architecture, and the system design match the use cases you’re actually trying to solve.

    What Harness Engineering Is, and Why It’s Now a Discipline

    Harrison Chase, CEO of LangChain, introduced a term that’s worth adding to your vocabulary: harness engineering.

    Harness engineering is the discipline of building reliable, structured environments around AI agents. The agent itself — the model running in a loop, calling tools, taking action — is only part of the system. The harness is everything surrounding it: the workflows, the tool interfaces, the validation loops, the context management strategies, and the memory architecture. It’s what makes the difference between an agent that works in a demo and one that operates reliably in production.

    NOTE: This is one definition of the term. The market is still debating this term, and I will write another specific piece on harness engineering.

    Chase made the point directly: even the closed model labs practice harness engineering constantly. He gave the example of Anthropic’s Claude and Claude Code. A model may be exceptional, but the harness around it — how it connects to file systems, how it manages long tasks, what guardrails constrain its actions — is equally responsible for the results. When enterprise teams underinvest in the harness and focus only on the model, they end up running expensive experiments that don’t scale.

    For technology leaders, this has a direct implication. Technical evaluations that benchmark models against each other without assessing the harness — the orchestration framework, the tooling, the integration architecture — are incomplete. The model is one variable. The harness is where most of the enterprise-specific work lives, and where most of the deployment risk concentrates.

    Multi-Component (Multi-Agent) AI Systems: What the Term Actually Means, and What to Ask

    You will hear vendors use terms like “compound agents,” “multi-agent systems,” and “agentic platforms” to describe their offerings. These terms are not interchangeable, and the market has not settled on consistent definitions. That ambiguity is worth understanding before you evaluate vendor claims.

    The most rigorous current framing comes from UC Berkeley’s Sky Lab, which defines a compound AI system as one that combines multiple AI components — models, retrievers, tools, databases, external APIs — to complete a task, rather than relying on a single model call. The system’s behavior emerges from how those components interact, not from any one of them individually. This framing has practical logic behind it: composing specialized components often outperforms a single frontier model on both capability and cost, particularly for complex multi-step tasks.

    Where it gets murkier is in how vendors apply the label. “Compound agent” in active use can mean three different things: a compound AI system that takes actions rather than just generating output; multiple discrete agents collaborating, each with its own reasoning loop; or an orchestrator-and-subagent architecture in which a controlling agent decomposes tasks and delegates them to specialized agents. These have meaningfully different architecture, cost, and governance implications. A vendor calling their product a “compound agent platform” may mean any of the three. The label alone tells you nothing about the actual design.

    The governance implication is the one most enterprise buyers miss. Multi-component systems diffuse accountability. When a consequential decision emerges from the interaction of a retriever, a reasoning model, and a tool execution layer, which component is responsible for the output? Traditional audit trails track the system’s final action. They often don’t track which component drove the decision that led to it. Before deploying any multi-component AI system in a business-critical workflow, buyers should require component-level architecture disclosure and confirm that audit logging covers component interactions, not just system outputs.

    The practical posture: treat “compound,” “agentic,” and “multi-agent” as marketing descriptors until a vendor discloses their specific component architecture. Ask what components the system includes, how they interact, how failures in individual components surface, and where in the stack governance and audit trails are enforced. Those answers will tell you far more than the product label.

    Orchestration Is the New Core Infrastructure

    Arvind Srinivas, CEO of Perplexity, described what his company calls Perplexity Computer: a multi-model, multi-cloud orchestration system where the models themselves become tools, and the orchestration layer determines which tool to apply to which task. The goal is that an enterprise can delegate a goal without specifying which model handles each step — the system manages that routing. Here is another take he gave on the topic in February.

    The analogy he offered is useful for enterprise framing: sub-agents are musicians, models are instruments, and the orchestration system is what produces the symphony. The quality of the output depends on all three, not just the instruments.

    The practical translation: the orchestration layer is where the strategic architecture decisions live. It determines which models get used for which tasks, how context is managed across long-running workflows, where governance and guardrails are enforced, and how exposed you are to vendor dependency. If your orchestration layer is tightly coupled to a single model provider, every future model decision becomes a migration project. If it’s designed to be model-agnostic, you preserve optionality as the model market continues to evolve rapidly.

    Mira Murati, CEO of Thinking Machines Lab, added a related dimension. Her firm has focused on making post-training — the layer of model development that adapts a foundation model to specific domains and tasks — accessible to enterprises and researchers. Most enterprise AI value doesn’t come from a raw pre-trained model. It comes from a model tailored to your domain, data, and task requirements. Accessible post-training means more organizations can build the specialized models that multi-component architectures require, without depending entirely on what general-purpose foundation models provide out of the box.

    What to Do Before Your Next AI System Decision

    The architecture described in this conversation isn’t on a roadmap. Cursor, LangChain, Perplexity, Mistral, and Thinking Machines Lab are all in production with enterprise customers today. The market has already moved.

    Three things worth doing before your next AI procurement decision:

    Evaluate the system, not just the model. Ask vendors to show you the harness — the orchestration framework, the tool interfaces, the memory architecture, the governance layer — not just model benchmark scores. You’re buying the system. Evaluate it as one.

    Require component-level architecture disclosure. When a vendor describes their offering as a compound agent, an agentic platform, or a multi-agent system, ask them to specify the components the system includes and how they interact. The label is not a specification. The architecture is.

    Treat orchestration as strategic infrastructure. The orchestration layer is where vendor dependencies are created or avoided, where governance is enforced or bypassed, and where the long-term flexibility of your AI architecture resides. It deserves the same scrutiny as your data infrastructure decisions. Evaluate orchestration before you’re locked into a system that makes changing it painful.

    The model was never the moat, but perhaps the system is. Knowing how to evaluate the totality of an AI system is what separates a well-matched AI investment from an expensive lesson.

  • Scaling AI with Proven Strategies and Frameworks

    Scaling AI with Proven Strategies and Frameworks

    Picking a use case, proving value, and expanding is the standard advice for getting started with AI. For the early stages of AI deployment, this advice is still sound. But at NVIDIA GTC, Cameron Davies, Chief Data Officer of Yum Brands, made the case that scaling AI in a large enterprise requires companies to think differently—and he had the results to back it up.

    Yum Brands is one of the world’s largest restaurant companies with 63,000 locations, processing more than 100 million transactions per day, across 155 countries through 1,500 franchisees. Davies didn’t come to GTC to talk about a pilot. He came to talk about what happens after the pilot, when the real complexity begins.

    His session, “Scaling AI Agents Globally Across Brands, Use Cases, and Restaurants,” wasn’t a product pitch. It was a framework. And it resets the conversation about what it actually takes to put AI into production at scale.

    Stop Thinking in Use Cases. Start Thinking in Skills.

    Over the past two years, enterprises have been plagued by failed AI proofs of concept. The firms that demonstrated returns from AI started with a fairly straightforward approach: identify a use case, build a proof of concept, demonstrate ROI, and expand. Davies challenged that model directly, not because it’s wrong in principle, but because it doesn’t scale.

    “Doing AI in a lab, it’s easy. We’ve all done it before. In fact, the models aren’t the problem anymore… Getting it to work in the lab, getting it to work with a phone call, that wasn’t the hard part. Getting it to scale in a messy, imprecise world, that’s hard.”

    At Yum’s scale, a use-case-first approach produces monolithic agents that are good at one thing in one context and fall apart everywhere else. The company operates over 500 different point-of-sale systems globally. Its menus differ not just across brands but across regions within the same brand. An AI that works perfectly at a Taco Bell in California may not transfer cleanly to a KFC in the UK or a Pizza Hut in India.

    Davies reframed the question. Instead of asking “what use case should we build?”, his team asks “what skills do we need, and how do we make those skills reusable?”

    He described it using a sports analogy: “I could ask you to build me a baseball pitching robot… That’s very different from me saying to my team, I need a throwing machine. This machine doesn’t care what’s in its hand, and it doesn’t care what the target is. It’s just really good at throwing things.”

    The practical implication: Yum built customer-facing agent skills and team productivity agent skills that can be deployed across brands, markets, and use cases — rather than building discrete agents for each problem. The same voice-ordering capability that operates a drive-through in one country can surface in a customer service context in another, because the underlying skill is real-time voice processing, transcription, and translation.

    Scaling AI requires a meaningful shift for large enterprises. It requires more architectural thinking upfront and more coordination between IT and business teams before a single POC is built. But the payoff is a reusable capability layer rather than a growing inventory of one-off AI deployments that each require their own maintenance, integration, and governance.

    You Can’t Scale AI Without a Governance Strategy.

    Davies was unambiguous on this point, and it is worth quoting him directly.

    “If you’re going to do AI, people often ask me, what’s the first thing you do? … It’s governance, because there is no intelligence without governance.”

    He used a vivid example from a colleague, Manoj Saxena, chair of the Responsible AI Institute: “You are all excited about these (AI) agents and what they’re going to do, but you don’t have the right governance in place, so what you’re doing is you’re all building these little Chuckies (from the movie)… in one hand’s a knife and the other hand’s a credit card, and you’re sending him loose into the system and he’s stabbing and swiping, and you don’t know what he’s doing, because you have no agent registries. You have no control.”

    At 100 million transactions per day, the risk profile of an ungoverned AI agent is not theoretical. A bad decision made at machine speed — the wrong menu item, a mishandled order, a brand-damaging interaction — can propagate across thousands of restaurants before anyone catches it. Davies is explicit that governance is not a post-deployment concern. It is the foundation.

    For most enterprise organizations, this is the piece that gets deprioritized. Governance feels like overhead when the pressure is to demonstrate AI value quickly. Davies’ experience suggests the opposite: organizations that skip governance in pursuit of speed end up constrained later, when the complexity of managing ungoverned agents becomes a bigger obstacle than moving slowly would have been.

    The practical starting point is an agent registry that tracks which agents are running, which tools they have access to, what decisions they are authorized to make, and how their outputs are monitored. That discipline doesn’t require a mature AI platform. It requires a decision to treat governance as infrastructure rather than an afterthought.

    The Practical Caveat: This Requires Knowing What You’re Trying to Accomplish

    Davies’ framework works because Yum entered this process with a clear understanding of what they were trying to do and what outcomes they needed to achieve. They have a dedicated data science team. They worked hard to create clean data, expand data sources, and define a platform strategy, and were supported by an executive mandate.

    That context matters. Smaller organizations, or those earlier in their AI journey, should not interpret “don’t think in use cases” as “don’t focus on defining a specific business value.” The opposite is true. Yum’s skill-based approach requires a sophisticated understanding of what capabilities the business needs, how those capabilities connect to customer or operational outcomes, and how the underlying architecture will support reuse.

    Davies described decomposing something as simple as a taco order into a set of discrete tool calls — finding a product, applying a modification, adding it to the cart, and submitting the order. Each of those is a separate decision point, a separate skill. Getting that decomposition right required deep knowledge of Yum’s systems, menus, and the ways customers actually interact with them.

    If you are selecting a specific use case to prove value, that is still sound advice — especially if you are still building organizational confidence in AI or don’t yet have the infrastructure to support a platform approach. The piece of Davies’ advice that applies universally is this: whatever you build, know the outcome you are trying to achieve before you start. The goal and the measurement need to come before the tool selection.

    Davies put it succinctly in his closing guidance: “Build in your measurement from day one. Build it into your systems from day one. Continually measure it and continually talk about the measurement.”

    How Yum Solved the Scaling Problem

    None of this is easy to replicate. Yum has resources, a mature data organization, and an executive mandate that made this approach possible. But the framework Davies laid out is worth capturing — not to copy it, but to understand the decisions behind it.

    Start with governance, not a pilot. Define what agents are allowed to do, how they are registered, and how their behavior is monitored before deploying anything at scale. Governance is infrastructure.

    Build an AI platform, not a collection of POCs. Yum invested in its proprietary platform — Byte — as the connective tissue for its AI work. Davies was direct: “Platforms, not pilots.” The platform enables skills to be reused. Without it, every deployment starts from scratch.

    Design for reusable skills, not one-time use cases. Break capabilities into discrete, transferable components. Each skill should be able to operate in multiple contexts without modification. This requires decomposing workflows into specific decision points and building and training against them rather than against an end-to-end scenario.

    Use synthetic data to address data gaps. Yum worked with NVIDIA’s Nemo tools to generate over 20,000 training records from limited production data. This allowed them to train small, domain-specific models on the kinds of decisions that matter in their environment — without waiting for years of production data to accumulate.

    Measure continuously and own your data. Davies was emphatic on two points that are easy to underestimate. First, always measure, always share measurements, and never stop. Second, do not give up your data or your orchestration layer to a vendor partner. “No matter who you partner with, don’t give them up. That’s your intellectual property. That’s what matters most.”

    The results Yum reported: a three-times improvement in tool call accuracy over a base model after fine-tuning, 100% function match for complex multi-step orders, and a 20-times reduction in inference cost compared to general-purpose closed models. Those numbers reflect what happens when the architecture, the training approach, and the measurement discipline are all aligned.

    Scaling AI is not just a technology problem. It is a business strategy, architecture, and governance problem that technology enables. The lesson for other organizations is not necessarily to copy Yum’s specific approach — it is to ask the same questions Davies asked before assuming a POC is a strategy.

    Define what your business outcome looks like. Start with governance. Build a platform. Design for reuse. Measure everything.

    A version of this article was originally posted in my AI Decoded with Maribel Lopez newsletter. Subscribe to my LinkedIn Newsletter here.

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  • NemoClaw, OpenClaw, and the Real Reason Enterprises Haven’t Deployed AI Agents Yet

    NemoClaw, OpenClaw, and the Real Reason Enterprises Haven’t Deployed AI Agents Yet

    

    NVIDIA’s NemoClaw adds enterprise security to OpenClaw. What it does, what it doesn’t, and what CIOs should do before deploying.

    FULL SHOW NOTES

    OpenClaw became the fastest-growing open-source project in history. Enterprise buyers watched from the sidelines — not because the technology wasn’t useful, but because an autonomous agent with access to corporate file systems, credentials, and external communication channels is a governance and security problem that no one had solved at the enterprise level.

    At NVIDIA’s GTC 2026 conference, Jensen Huang announced NemoClaw: a reference stack that adds enterprise security controls to OpenClaw. In this solo episode, Maribel Lopez breaks down what NemoClaw actually does, why the SaaS partner ecosystem matters as much as the technology itself, and where the hype is running ahead of the reality.

    WHAT WE COVER

    •       Why OpenClaw created a shadow IT problem before NemoClaw existed

    •       What OpenShell, the Privacy Router, and Nemotron models actually do for enterprise buyers

    •       Why Salesforce, ServiceNow, SAP, Cisco, and CrowdStrike being in the ecosystem matters

    •       The hardware dependency NVIDIA’s marketing glosses over

    •       Why “working with NVIDIA” and “ready to deploy” are not the same thing

    •       The three questions every CIO should answer before touching any of this

    TIMESTAMPS

    00:00  —  Why enterprise IT teams were watching OpenClaw from the sidelines

    01:45  —  What OpenClaw is and why it created an enterprise security problem

    04:00  —  What NemoClaw actually does: OpenShell, Privacy Router, Nemotron

    06:30  —  The SaaS ecosystem: Salesforce, ServiceNow, SAP, Cisco, CrowdStrike

    08:30  —  Where the hype is ahead of the reality

    10:15  —  Three questions CIOs should answer before deploying

    RESOURCES MENTIONED

    •       NemoClaw announcement and NVIDIA Agent Toolkit: build.nvidia.com

    •       Full written analysis: NemoClaw Brings Enterprise-Grade Security Controls to OpenClaw — lopezresearch.com

    •       NVIDIA GTC 2026 Jensen Huang keynote

    ABOUT THIS PODCAST

    AI with Maribel Lopez covers enterprise AI adoption, agentic systems, AI governance, and AI-driven customer experience. Maribel Lopez is founder and principal analyst at Lopez Research, a technology research and strategy firm.

    Subscribe on Apple Podcasts, Spotify, or your platform of choice here: https://www.buzzsprout.com/1947446

    KEYWORDS

    enterprise AI agents, agentic AI security, NemoClaw NVIDIA, OpenClaw enterprise deployment, AI agent governance, enterprise AI strategy, AI governance enterprise, agentic AI risks

  • NemoClaw Gives Enterprise AI Agents The Security Layer They’ve Been Missing

    NemoClaw Gives Enterprise AI Agents The Security Layer They’ve Been Missing

    The NVIDIA GTC announcement of the NemoClaw stack addresses one of the real reasons enterprises haven’t deployed AI agents at scale — and one vendor’s alternative to address those concerns.

    OpenClaw became the fastest-growing open-source project in history while enterprise buyers watched from the sidelines.

    Not because the technology wasn’t interesting. It is. Not because employees weren’t already using it. They were — quietly, on personal machines, sometimes on corporate devices. Enterprises held back because an autonomous AI agent that can access file systems, execute code, log into corporate systems, and communicate externally is not something you hand to 10,000 employees without a security framework underneath it.

    NVIDIA ‘s NemoClaw announcement at GTC 2026 offers one solution to address that gap directly. It is not a competitor to OpenClaw. It is a reference stack that adds the infrastructure layer OpenClaw was missing: policy-based security guardrails, a privacy router, a sandboxed runtime called OpenShell, and integration with the security tools enterprises already use, installed in a single command.

    Jensen Huang put it plainly in his GTC keynote:

    “Agentic systems in the corporate network can have access to sensitive information. They can execute code and communicate externally. You could access employee information, access supply chain, access finance information, and send it out. Obviously, this can’t possibly be allowed.”

    For enterprise buyers, this is the right framing. The question was never whether AI agents would be useful. The question was whether they could be deployed safely inside an enterprise environment. NemoClaw is NVIDIA’s answer to that question for OpenClaw.

    Why OpenClaw Alone Wasn’t Enterprise-Ready

    Before discussing what NemoClaw adds, it is worth clarifying the enterprise IT and security leader’s concerns.

    OpenClaw is designed to act like a digital assistant sitting at your computer. It can view your screen, control your browser, open files, execute commands, and log in to websites. If an employee grants OpenClaw access, it operates the computer the same way the employee would. That means if the employee can access corporate email, CRM systems, internal dashboards, customer data, or finance systems, the tool can access them too.

    The issue is not the AI itself. The issue is how much access the software has and where the data goes afterward. Specific risks enterprises were managing before NemoClaw include:

    • Credential exposure. If OpenClaw interacts with browser sessions or system logins, it can gain access to saved passwords, session tokens, and authentication cookies. This could allow the tool — or anything that compromises it — to impersonate the employee in corporate systems.
    • Data leaving the organization. AI automation tools often send information to external services for processing. That can include screenshots, text from documents, browser content, and system commands. If sensitive enterprise data is transmitted outside approved systems, it may violate GDPR, HIPAA, PCI, or data-residency requirements across multiple jurisdictions.
    • Autonomous actions without audit trails. AI agents can operate autonomously, executing workflows, interacting with websites, and performing transactions without logging. If poorly configured, an agent could send emails, download files, update records, or trigger business workflows without IT or compliance teams having visibility.
    • Shadow IT at scale. Meta instructed employees not to use OpenClaw on work computers due to security concerns. Many companies followed suit. Even so, adoption continued. Prohibition alone does not work when the tool is genuinely useful. Enterprises need a sanctioned, controlled alternative or safeguards— not just a policy.

    What NemoClaw Actually Does

    NemoClaw is not a product. It is a reference stack — an open-source configuration of existing and new NVIDIA tools that installs on top of OpenClaw to add the enterprise infrastructure layer. Here is what each component does:

    1. OpenShell. This is the core of the security layer. OpenShell runs OpenClaw agents in an isolated sandbox that limits what files they can access and restricts network connectivity. Enterprises can write policy rules in YAML to define exactly which systems an agent can access, which data it can process, and which actions require human approval. YAML Ain’t Markup Language (YAML) is a human-friendly, plain-text format used to store data, configure software, or move data between systems. Some rules are hot-swappable, allowing them to be changed without restarting the agent.
    2. Privacy Router. When agents need to call cloud-based frontier models, the Privacy Router ensures sensitive enterprise data is not transmitted to those external models. This is the mechanism that makes hybrid local-cloud architectures viable without creating data residency exposure.
    3. Nemotron Models. NVIDIA’s open model family can run locally on dedicated hardware, including NVIDIA RTX PCs and DGX Station. Running inference locally means sensitive enterprise data stays on-premises for tasks that require it. This also reduces cloud inference costs. NVIDIA reports that its hybrid architecture — using Nemotron for research tasks and frontier models for orchestration — can cut query costs by more than 50 percent.
    4. Single-command installation. This matters more than it sounds. One practical barrier to enterprise AI deployment is configuration complexity. A stack that requires days of professional services to stand up will not achieve broad adoption. NemoClaw installs the entire reference configuration in one command.

    Kari Briski, NVIDIA’s VP of Generative AI Software, described it this way at GTC: OpenShell “provides the missing infrastructure layer beneath claws to give them the access they need to be productive, while enforcing policy-based security, network, and privacy guardrails.”

    That framing is accurate. The productivity value of OpenClaw was never in question. What was missing was the control layer.

    The SaaS Ecosystem Is Already Moving

    For enterprise buyers, the breadth of the partner ecosystem matters as much as the technology itself. You are not just evaluating a security sandbox. You are evaluating whether the tools your teams already use will work within that sandbox.

    The list of software companies integrating with NVIDIA Agent Toolkit and OpenShell includes Adobe, Atlassian, Box, Cisco, CrowdStrike, Red Hat, Salesforce, SAP, ServiceNow, and Siemens. A few specific integrations are worth noting:

    • Cisco‘s AI Defense will provide AI security protection for OpenShell, adding controls and guardrails to govern agent actions. For enterprises already running Cisco security infrastructure, this is a direct integration path.
    • CrowdStrike unveiled a Secure-by-Design AI Blueprint that embeds Falcon platform protection directly into NVIDIA AI agent architectures. Enterprises with CrowdStrike endpoint protection can extend that coverage to AI agents.
    • Salesforce is working with Agent Toolkit to enable customers to build and deploy AI agents through Agentforce for service, sales, and marketing tasks, using Slack as the conversational interface and orchestration layer. This matters because Salesforce and Slack are already inside most enterprises.
    • ServiceNow ’s Autonomous Workforce of AI Specialists is built on Agent Toolkit and includes NVIDIA Nemotron models. ServiceNow is the workflow backbone for IT operations in many large enterprises, which means agentic AI is now moving into ITSM and operational workflows directly.
    • SAP is using Agent Toolkit to enable AI agents through Joule Studio on SAP Business Technology Platform, allowing customers to design agents tailored to specific business processes. For enterprises running SAP as their ERP backbone, this is a direct integration with operational data.

    The pattern here is important. These are not experimental integrations. Salesforce, ServiceNow, and SAP together represent the core of enterprise application infrastructure. When your existing SaaS platforms build their agentic strategies on the same security and identity stack, it significantly reduces the interoperability problem. You are not assembling a security architecture from scratch for every new AI agent. You are extending an existing framework.

    It is also worth noting that NVIDIA is collaborating with Microsoft Security, Google , CrowdStrike, and TrendAI to build OpenShell compatibility with their security tools. The intent is to make OpenShell work within security stacks enterprises already have, not replace them.

    What This Means for CIOs and Enterprise Buyers

    NemoClaw does not eliminate the work required to deploy AI agents safely. It reduces the barrier significantly. Before evaluating whether NemoClaw belongs in your environment, three questions are worth resolving:

    Are employees already using OpenClaw? If the answer is yes — even informally — then the risk exists today. NemoClaw gives you a sanctioned path to bring that activity under enterprise controls rather than competing with shadow IT through prohibition.

    Is your identity framework ready for AI agents? NemoClaw provides the guardrails, but agents still need registered identities and role-based access controls. Most enterprise identity frameworks were designed for people. Before deploying agents at scale, confirm that your identity infrastructure can assign, manage, and revoke agent credentials separately from human credentials.

    Do you have the observability to know if agents are working correctly? OpenShell provides audit logging and network guardrails. That is a starting point, not a complete observability stack. You need monitoring in place to detect when agents are drifting, making errors, or accessing systems they should not.

    The Caveats That Curb My Enthusiasm

    The broader context here matters. Jensen Huang framed the OpenClaw moment as equivalent to the arrival of Linux, HTML, and Kubernetes — foundational infrastructure that reorganized entire industries. That may or may not prove accurate.

    What is accurate is that agentic AI as a category, not just OpenClaw, will fundamentally change enterprise workflow platforms. The companies that define their governance architecture now will be better positioned than those that build it under pressure later.

    It is also worth being realistic about what NemoClaw does not solve. Cross-vendor agent orchestration remains complex. An agent working in Salesforce does not automatically collaborate with an agent in SAP without significant integration work. Governance frameworks for multi-agent systems are still nascent. The tools are improving faster than the enterprise readiness in most organizations.

    Other things to consider. NemoClaw and OpenShell are open source. Anyone can download them. However, to run Nemotron models locally (the privacy-preserving option that keeps data on-premises), you need NVIDIA GPU hardware — an RTX PC, DGX Spark, or DGX Station.

    YAML policy configuration is an IT burden. Enterprises customize OpenShell by writing YAML rules. That’s a developer-friendly approach, not an enterprise admin-friendly one. Large organizations with thousands of use cases will need tooling and staffing to manage that policy layer. This is not point-and-click governance.

    The partner ecosystem is announced, not proven. The list of SaaS partners — Salesforce, SAP, ServiceNow, etc. — represents intent and roadmap, not certified integrations. Most of these are “working with NVIDIA” statements. Before betting enterprise deployments on them, buyers should ask specifically what is shipping, when, and what certification or testing has been completed.

    Start with Governance, Not the Agent

    NemoClaw is genuinely useful for enterprise buyers. It addresses the right problem in the right way — by adding enterprise security controls to an open-source agent platform that employees are already adopting, rather than building a competing proprietary stack.

    The partner ecosystem — particularly Salesforce, ServiceNow, SAP, Cisco, and CrowdStrike — means NemoClaw is not a greenfield deployment for most enterprises. It fits into the existing infrastructure.

    But the technology being ready does not mean you are ready. Before deploying agents in any production environment, define what they are allowed to do, what data they can access, and how you will know when something goes wrong. That work is not optional — and no reference stack does it for you.

    Governance first. Agents second. That is the sequence that works.

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  • Dell AI Factory Expands with 40+ Enhancements for Enterprise AI Deployment

    Dell AI Factory Expands with 40+ Enhancements for Enterprise AI Deployment

    Dell Technologies unveiled a significant expansion of its AI Dell Factory platform at its annual Dell Technologies World conference today, announcing over 40 product enhancements designed to help enterprises deploy artificial intelligence workloads more efficiently across both on-premises environments and cloud systems.

    The Dell AI Factory is not a physical manufacturing facility but a comprehensive framework combining advanced infrastructure, validated solutions, services, and an open ecosystem to help businesses harness the full potential of artificial intelligence across diverse environments—from data centers and cloud to edge locations and AI PCs.

    The company has attracted over 3,000 AI Factory customers since launching the platform last year. In an earlier call with industry analysts, Dell shared research stating that 79% of production AI workloads are running outside of public cloud environments—a trend driven by cost, security, and data governance concerns. During the keynote, Michael Dell provided more color on the value of Dell’s AI factory concept. He said, “The Dell AI factory is up to 60% more cost effective than the public cloud, and recent studies indicate that about three-fourths of AI initiatives are meeting or exceeding expectations. That means organizations are driving ROI and productivity gains from 20% to 40% in some cases. 

    Making AI Easier to Deploy

    Organizations need the freedom to run AI workloads wherever makes the most sense for their business, without sacrificing performance or control. While IT leaders embraced the public cloud for their initial AI services, many organizations are now looking for a more nuanced approach where the company can control over their most critical AI assets while maintaining the flexibility to use cloud resources when appropriate. Over 80 percent of the companies Lopez Research interviewed said they struggled to find the budget and technical talent to deploy AI. These AI deployment challenges have only increased as more AI models and AI infrastructure services have been launched.

    Silicon Diversity and Customer Choice

    A central theme of Dell’s AI Factory message is how Dell makes AI easier to deploy while delivering choice. Dell is offering customers choice through silicon diversity in its designs, but also with ISV models. The company announced it has added Intel to its AI Factory portfolio with Intel Gaudi 3 AI accelerators and Intel Xeon processors, with a strong focus on inferencing workloads.

    Dell also announced its fourth update to the Dell AI Platform with AMD, rolling out two new PowerEdge servers—the XE9785 and the XE9785L—equipped with the latest AMD Instinct MI350 series GPUs. The Dell AI Factory with NVIDIA combines Dell’s infrastructure with NVIDIA’s AI software and GPU technologies to deliver end-to-end solutions that can reduce setup time by up to 86% compared to traditional approaches. The company also continues to strengthen its partnership with NVIDIA, announcing products leveraging NVIDIA’s Blackwell family and other updates launched at NVIDIA GTC. As of today, Dell supports choice by delivering AI solutions with all of the primary GPU and AI accelerator infrastructure providers.

    Client-Side AI Advancements

    At the edge of the AI Factory ecosystem, Dell announced enhancements to the Dell Pro Max in a mobile form factor, leveraging Qualcomm’s AI 100 discrete NPUs designed for AI engineers and data scientists who need fast inferencing capabilities. With up to 288 TOPs at 16-bit floating point precision, these devices can power up to a 70-billion parameter model, delivering 7x the inferencing speed and 4x the accuracy over a 40 TOPs NPU. Dell says the Pro Max Plus line can run a 109-billion-parameter AI model.

    The Pro Max and Plus launches follow Dell’s previous announcement of AI PCs featuring Dell Pro Max with GB 10 and GB 300 processors powered by NVIDIA’s Grace Blackwell architecture. Overall, Dell has simplified its PC portfolio but made it easier for customers to choose the right system for their workloads by providing the latest chips from AMD, Intel, Nvidia, and Qualcomm.

    On-Premise AI Deployment Gains Ecosystem Momentum

    Following the theme of choice, organizations need the flexibility to run AI workloads on-premises and in the cloud. Dell is making significant strides in enabling on-premise AI deployments with major software partners. The company announced it is the first provider to bring Cohere capabilities on-premises, combining Cohere’s generative AI models with Dell’s secure, scalable infrastructure for turnkey enterprise solutions.

    Similar partnerships with Mistral and Glean were also announced, with Dell facilitating their first on-premise deployments. Additionally, Dell is supporting Google’s Gemini on-premises with Google Distributed Cloud.

    To simplify model deployment, Dell now offers customers the ability to choose models on Hugging Face and deploy them in an automated fashion using containers and scripts. Enterprises increasingly recognize that while public cloud AI has its place, a hybrid AI infrastructure approach could deliver better economics and security for production workloads.

    The imperative for scalable yet efficient AI infrastructure at the edge is a growing need. As Michael Dell said during his Dell Technologies World keynote, “Over 75% of enterprise data will soon be created and processed at the edge, and AI will follow that data; it’s not the other way around. The future of AI will be decentralized, low latency, and hyper-efficient.”

    Dell’s ability to offer robust hybrid and fully on-premises solutions for AI is proving to be a significant advantage as companies increasingly seek on-premises support and even potentially air-gapped solutions for their most sensitive AI workloads. Key industries adopting the Dell AI Factory include finance, retail, energy, and healthcare providers.

    Scaling AI Requires a Focus on Energy Efficiency 

    Simplifying AI also requires product innovations that deliver cost-effective, energy-efficient technology. As AI workloads drive unprecedented power consumption, Dell has prioritized energy efficiency in its latest offerings. The company introduced the Dell PowerCool Enclosed Rear Door Heat Exchanger (eRDHx) with Dell Integrated Rack Controller (IRC). This new cooling solution captures nearly 100% of the heat coming from GPU-intensive workloads. This innovation lowers cooling energy requirements for a rack by 60%, allowing customers to deploy 16% more racks with the same power infrastructure.

    Dell’s new systems are rated to operate at 32 to 37 degrees Celsius, supporting significantly warmer temperatures than traditional air-cooled or water-chilled systems, further reducing power consumption for cooling. The PowerEdge XE9785L now offers Dell liquid cooling for flexible power management. Even if a company isn’t aiming for a specific sustainability goal, every organization wants to improve energy utilization. 

    Early Adopter Use Cases Highlight AI’s Opportunity

    With over 200 product enhancements to its AI Factory in just one year, Dell Technologies is positioning itself as a central player in the rapidly evolving enterprise AI infrastructure market. It offers the breadth of solutions and expertise organizations require to successfully implement production-grade AI systems in a secure and scalable fashion. However, none of this technology matters if enterprises can’t find a way to create business value by adopting it. Fortunately, examples from the first wave of enterprise early adopters highlight ways AI can deliver meaningful returns in productivity and customer experience. Let’s look at two use cases presented at Dell Tech World. 

    The Power of LLMs in Finance at JPMorgan Chase

    JPMorgan Chase took the stage to make AI real from a customer’s perspective. The financial firm uses Dell’s compute hardware, software-defined storage, client, and peripheral solutions. Larry Feinsmith, the Managing Director and Head of Global Tech Strategy, Innovation & Partnerships at JPMorgan Chase, said, “We have a hybrid, multi-cloud, multi-provider strategy. Our private cloud is an incredibly strategic asset for us. We still have many applications and data on-premises for resiliency, latency, and a variety of other benefits.” 

    Feinsmith also spoke of the company’s Large Language Model (LLM) strategy. He said, “Our strategy is to use a constellation of models, both foundational and open, which requires a tremendous amount of compute in our data centers, in the public cloud, and, of course, at the edge. The one constant thing, whether you’re training models, fine-tuning models, or finding a great use case that has large-scale inferencing, is that they all will drive compute. We think Dell is incredibly well positioned to help JPMorgan Chase and other companies in their AI journey.”

    Feinsmith noted that using AI isn’t new for JPMorgan Chase. For over a decade, JPMorgan Chase has leveraged various types of AI, such as machine learning models for fraud detection, personalization, and marketing operations. The company uses what Feinsmith called its LLM suite, which over 200,000 people at JPMorgan Chase use today. The generative AI application is used for QA summarization and content generation using JPMorgan Chase’s data in a highly secure way. Next, it has used the LLM suite architecture to build applications for its financial advisors, contact center agents, and any employee interacting with its clients. Its third use case highlighted changes in the software development area. JPMorgan Chase rolled out code generation AI capabilities to over 40,000 engineers. It has achieved as much as 20% productivity in the code creation and expects to leverage AI throughout the software development life cycle. Going forward, the financial firm expects to use AI agents and reasoning models to execute complex business processes end-to-end.

    Seemantini Godbole, EVP and Chief Digital and Information Officer at Lowe's, shared insights on designing the strategy for AI

    How AI Makes It Easier For Employees to Serve Customers at Lowe’s

    Lowe’s Home Improvement Stores provided another example of how companies are leveraging Dell Technology and AI to transform the customer and employee experience. Seemantini Godbole, EVP and Chief Digital and Information Officer at Lowe’s, shared insights on designing the strategy for AI when she said, “How should we deploy AI? We wanted to do impactful and meaningful things. We did not want to die a death of 1000 pilots, and we organized our efforts across how we sell, how we shop, and how we work. How we sell was for our associates. How we shop is for our customers, and how we work is for our headquarters employees. For whatever reason, most companies have begun with their workforce in the headquarters. We said, No, we are going to put AI in the hands of 300,000 associates.” For example, she described a generative AI companion app for store associates. “Every store associate now has on his or her zebra device a ChatGPT-like experience for home improvement.”, said Godbole. Lowe’s is also deploying computer vision algorithms at the edge to understand issues such as whether a customer in a particular aisle is waiting for help. The system will then send notifications to the associates in that department. Customers can also ask various home improvement questions, such as what paint finish to use in a bathroom, at Lowes.com/AI.

    Designing A World Where AI Delivers Human Opportunity

    Michael Dell said, “We are entering the age of ubiquitous intelligence, where AI becomes as essential as electricity, with AI, you can distill years of experience into instant insights, speeding up decisions and uncovering patterns in massive data. But it’s not here to replace humans. AI is a collaborator that frees your teams to do what they do best, to innovate, to imagine, and to solve the world’s toughest problems.” 

    While there are many AI deployment challenges ahead, the customer examples shared at Dell Technologies World provide a glimpse into a world where AI benefits both customers and employees. The challenge now is to do this sustainably and ethically at scale.