- Trust in AI agents lags behind their enterprise adoption
Eighty-five percent of enterprises are running AI agent pilots, but only 5% have moved those agents into production. In an exclusive interview at RSA Conference 2026, Cisco President and Chief Product Officer Jeetu Patel said that the gap comes down to one thing: trust — and that closing it separates market dominance from bankruptcy. He also disclosed a mandate that will reshape Cisco's 90,000-person engineering organization.
The problem is not rogue agents. The problem is the absence of a trust architecture.
The trust deficit behind a 5% production rate
A recent Cisco survey of major enterprise customers found that 85% have AI agent pilot programs underway. Only 5% moved those agents into production. That 80-point gap defines the security problem the entire industry is trying to close. It is not closing.
"The biggest impediment to scaled adoption in enterprises for business-critical tasks is establishing a sufficient amount of trust," Patel told VentureBeat. "Delegating versus trusted delegating of tasks to agents. The difference between those two, one leads to bankruptcy and the other leads to market dominance."
He compared agents to teenagers. "They're supremely intelligent, but they have no fear of consequence. They're pretty immature. And they can be easily sidetracked or influenced," Patel said. "What you have to do is make sure that you have guardrails around them and you need some parenting on the agents."
The comparison carries weight because it captures the precise failure mode security teams face. Three years ago, a chatbot that gave the wrong answer was an embarrassment. An agent that takes the wrong action can trigger an irreversible outcome. Patel pointed to a case he cited in his keynote where an AI coding agent deleted a live production database during a code freeze, tried to cover its tracks with fake data, and then apologized. "An apology is not a guardrail," Patel said in his keynote blog. The shift from information risk to action risk is the core reason the pilot-to-production gap persists.
Defense Claw and the open-source speed play with Nvidia
Cisco's response to the trust deficit at RSAC 2026 spanned three categories: protecting agents from the world, protecting the world from agents, and detecting and responding at machine speed. The product announcements included AI Defense Explorer Edition (a free, self-service red teaming tool), the Agent Runtime SDK for embedding policy enforcement into agent workflows at build time, and the LLM Security Leaderboard for evaluating model resilience against adversarial attacks.
The open-source strategy moved faster than any of those. Nvidia launched OpenShell, a secure container for open-source agent frameworks, at GTC the week before RSAC. Cisco packaged its Skills Scanner, MCP Scanner, AI Bill of Materials tool, and CodeGuard into a single open-source framework called Defense Claw and hooked it into OpenShell within 48 hours.
"Every single time you actually activate an agent in an Open Shell container, you can now automatically instantiate all the security services that we have built through Defense Claw," Patel told VentureBeat. The integration means security enforcement activates at container launch without manual configuration. That speed matters because the alternative is asking developers to bolt on security after the agent is already running.
That 48-hour turnaround was not an anomaly. Patel said several of the Defense Claw capabilities Cisco launched were built in a week. "You couldn't have built it in longer than a week because Open Shell came out last week," he said.
A six-to-nine-month product lead and an information asymmetry on top of it
Patel made a competitive claim worth examining. "Product wise, we might be six to nine months ahead of most of the market," he told VentureBeat. He added a second layer: "We also have an asymmetric information advantage of, I'd say, three to six months on everyone because, you know, we, by virtue of being in the ecosystem with all the model companies. We're seeing what's coming down the pipe." The 48-hour Defense Claw sprint supports the speed claim, though the lead margin is Cisco's own characterization; no independent benchmarks were provided.
Cisco also extended zero trust to the agentic workforce through new Duo IAM and Secure Access capabilities, giving every agent time-bound, task-specific permissions. On the SOC side, Splunk announced Exposure Analytics for continuous risk scoring, Detection Studio for streamlined detection engineering, and Federated Search for investigating across distributed data environments.
The zero-human-code engineering mandate
AI Defense, the product Cisco launched a year before RSAC 2026, is now 100% built with AI. Zero lines of human-written code. By the end of 2026, half a dozen Cisco products will reach the same milestone. By the end of calendar year 2027, Patel's goal is 70% of Cisco's products built entirely by AI.
"Just process that for a second and go: a $60 billion company is gonna have 70% of the products that are gonna have no human lines of code," Patel told VentureBeat. "The concept of a legacy company no longer exists."
He connected that mandate to a cultural shift inside the engineering organization. "There's gonna be two kinds of people: ones that code with AI and ones that don't work at Cisco," Patel said. That was not debated. "Changing 30,000 people to change the way that they work at the very core of what they do in engineering cannot happen if you just make it a democratic process. It has to be something that's driven from the top down."
Five moats for the agentic era, and what CISOs can verify today
Patel laid out five strategic advantages that will separate winning enterprises from failing ones. VentureBeat mapped each moat against actions security teams can begin verifying today.
Moat
Patel's claim
What CISOs can verify today
What to validate next
Sustained speed
"Operating with extreme levels of obsession for speed for a durable length of time" creates compounding value
Measure deployment velocity from pilot to production. Track how long agent governance reviews take.
Pair speed metrics with telemetry coverage. Fast deployment without observability creates blind acceleration.
Trust and delegation
Trusted delegation separates market dominance from bankruptcy
Audit delegation chains. Flag agent-to-agent handoffs with no human approval.
Agent-to-agent trust verification is the next primitive the industry needs. OAuth, SAML, and MCP do not yet cover it.
Token efficiency
Higher output per token creates a strategic advantage
Monitor token consumption per workflow. Benchmark cost-per-action across agent deployments.
Token efficiency metrics exist. Token security metrics (what the token accessed, what it changed) are the next build.
Human judgment
"Just because you can code it doesn't mean you should."
Track decision points where agents defer to humans vs. act autonomously.
Invest in logging that distinguishes agent-initiated from human-initiated actions. Most configurations cannot yet.
AI dexterity
"10x to 20x to 50x productivity differential" between AI-fluent and non-fluent workers
Measure the adoption rates of AI coding tools across security engineering teams.
Pair dexterity training with governance training. One without the other compounds the risk.
The telemetry layer the industry is still building
Patel's framework operates at the identity and policy layer. The next layer down, telemetry, is where the verification happens. "It looks indistinguishable if an agent runs your web browser versus if you run your browser," CrowdStrike CTO Elia Zaitsev told VentureBeat in an exclusive interview at RSAC 2026. Distinguishing the two requires walking the process tree, tracing whether Chrome was launched by a human from the desktop or spawned by an agent in the background. Most enterprise logging configurations cannot make that distinction yet.
A CEO's AI agent rewrote the company's security policy. Not because it was compromised. Because it wanted to fix a problem, lacked permissions, and removed the restriction itself. Every identity check passed. CrowdStrike CEO George Kurtz disclosed that incident and a second one at his RSAC keynote, both at Fortune 50 companies. In the second, a 100-agent Slack swarm delegated a code fix between agents without human approval.
Both incidents were caught by accident
Etay Maor, VP of Threat Intelligence at Cato Networks, told VentureBeat in a separate exclusive interview at RSAC 2026 that enterprises abandoned basic security principles when deploying agents. Maor ran a live Censys scan during the interview and counted nearly 500,000 internet-facing agent framework instances. The week before: 230,000. Doubling in seven days.
Patel acknowledged the delegation risk in the interview. "The agent takes the wrong action and worse yet, some of those actions might be critical actions that are not reversible," he said. Cisco's Duo IAM and MCP gateway enforce policy at the identity layer. Zaitsev's work operates at the kinetic layer: tracking what the agent did after the identity check passed. Security teams need both. Identity without telemetry is a locked door with no camera. Telemetry without identity is footage with no suspect.
Token generation as the currency for national competitiveness
Patel sees the infrastructure layer as decisive. "Every country and every company in the world is gonna wanna make sure that they can generate their own tokens," he told VentureBeat. "Token generation becomes the currency for success in the future." Cisco's play is to provide the most secure and efficient technology for generating tokens at scale, with Nvidia supplying the GPU layer. The 48-hour Defense Claw integration demonstrated what that partnership produces under pressure.
Security director action plan
VentureBeat identified five steps security teams can take to begin building toward Patel's framework today:
Audit the pilot-to-production gap. Cisco's own survey found 85% of enterprises piloting, 5% in production. Mapping the specific trust deficits keeping agents stuck is the starting point — the answer is rarely the technology. Governance, identity, and delegation controls are what's missing. Patel's trusted delegation framework is designed to close that gap.
Test Defense Claw and AI Defense Explorer Edition. Both are free. Red-team your agent workflows before they reach production. Test the workflow, not just the model.
Map delegation chains end-to-end. Flag every agent-to-agent handoff with no human approval. This is the "parenting" Patel described. No product fully automates it yet. Do it manually, every week.
Establish agent behavioral baselines. Before any agent reaches production, define what normal looks like: API call patterns, data access frequency, systems touched, and hours of activity. Without a baseline, the observability that Patel's moats require has nothing to compare against.
Close the telemetry gap in your logging configuration. Verify that your SIEM can distinguish agent-initiated actions from human-initiated actions. If it cannot, the identity layer alone will not catch the incidents Kurtz described at RSAC. Patel built the identity layer. The telemetry layer completes it.
- When your agents work faster than your access controls can keep up.
A doctor in a hospital exam room watches as a medical transcription agent updates electronic health records, prompts prescription options, and surfaces patient history in real time. A computer vision agent on a manufacturing line is running quality control at speeds no human inspector can match. Both generate non-human identities that most enterprises cannot inventory, scope, or revoke at machine speed.
That is the structural problem keeping agentic AI stuck in pilots. Not model capability. Not compute. Identity governance.
Cisco President Jeetu Patel told VentureBeat at RSAC 2026 that 85% of enterprises are running agent pilots while only 5% have reached production. That 80-point gap is a trust problem. The first questions any CISO will ask: which agents have production access to sensitive systems, and who is accountable when one acts outside its scope? IANS Research found that most businesses still lack role-based access control mature enough for today's human identities, and agents will make it significantly harder. The 2026 IBM X-Force Threat Intelligence Index reported a 44% increase in attacks exploiting public-facing applications, driven by missing authentication controls and AI-enabled vulnerability discovery.
Why the trust gap is architectural, not just a tooling problem
Michael Dickman, SVP and GM of Cisco's Campus Networking business, laid out a trust framework in an exclusive interview with VentureBeat that security and networking leaders rarely hear stated this plainly. Before Cisco, Dickman served as Chief Product Officer at Gigamon and SVP of Product Management at Aruba Networks.
Dickman said that the network sees what other telemetry sources miss: actual system-to-system communications rather than inferred activity. "It's that difference of knowing versus guessing," he said. "What the network can see are actual data communications … not, I think this system needs to talk to that system, but which systems are actually talking together." That raw behavioral data, he added, becomes the foundation for cross-domain correlation, and without it, organizations have no reliable way to enforce agent policy at what he called "machine speed."
The trust prerequisite that most AI strategies skip
Dickman argues that agentic AI breaks a pattern he says defined every prior technology transition: deploy for productivity first, bolt on security later.
"I don't think trust is one of those things where the business productivity comes first, and the security is an afterthought," Dickman told VentureBeat. "Trust actually is one of the key requirements. Just table stakes from the beginning."
Observing data and recommending decisions carries consequences that stay contained. Execution changes everything. When agents autonomously update patient records, adjust network configurations, or process financial transactions, the blast radius of a compromised identity expands dramatically.
"Now more than ever, it's that question of who has the right to do what," Dickman said. "The who is now much more complicated because you have the potential in our reality of these autonomous agents."
Dickman breaks the trust problem into four conditions. The first is secure delegation, which starts by defining what an agent is permitted to do and maintaining a clear chain of human accountability. The second is cultural readiness; he pointed to alert fatigue as a case study. The traditional fix, Dickman noted, was to aggregate alerts, so analysts see fewer items. With agents capable of evaluating every alert, that logic changes entirely.
"It is now possible for an agent to go through all alerts," Dickman said. "You can actually start to think about different workflows in a different way. And then how does that affect the culture of the work, which is amazing."
The third is token economics: Every agent’s action carries a real computational cost. Dickman sees hybrid architectures as the answer, where agentic AI handles reasoning while traditional deterministic tools execute actions. The fourth is human judgment. For example, his team used an AI tool to draft a product requirements document. The agent produced 60 pages of repetitive filler that immediately provided how technically responsive the architecture was, yet showed signs of needing extensive fine-tuning to make the output relevant. "There's no substitute for the human judgment and the talent that's needed to be dextrous with AI," he said.
What the network sees that endpoints miss
Most enterprise data today is proprietary, internal, and fragmented across observability tools, application platforms, and security stacks. Each domain team builds its own view. None sees the full picture.
"It's that difference of knowing versus guessing," Dickman said. "What the network can see are actual data communications. Not 'I think this system needs to talk to that system,' but which systems are actually talking together."
That telemetry grows more valuable as IoT and physical AI proliferate. Computer vision agents analyzing shopper behavior and running factory-floor quality control generate highly sensitive data that demands precise access controls.
"All of those things require that trust that we started with, because this is highly sensitive data around like who's doing what in the shop or what's happening on the factory floor," Dickman said.
Why siloed agent data misses the signal
"It's not only aggregation, but actually the creation of knowledge from the network," Dickman said. "There are these new insights you can get when you see the real data communications. And so now it becomes what do we do first versus second versus third?"
That last question reveals where Dickman’s focus lands: the strategic challenge is sequencing, not capability.
"The real power comes from the cross-domain views. The real power comes from correlation," Dickman said. "Versus just aggregation and deduplication of alerts, which is good, but it's a little bit basic."
This is where he sees the most common pitfall. Team A builds Agent A on top of Data A. Team B builds Agent B on top of Data B. Each silo produces incrementally useful automation. The cross-domain insight never materializes.
Independent practitioners validate the pattern. Kayne McGladrey, an IEEE senior member, told VentureBeat that organizations are defaulting to cloning human user profiles for agents, and permission sprawl starts on day one. Carter Rees, VP of AI at Reputation, identified the structural reason. "A significant vulnerability in enterprise AI is broken access control, where the flat authorization plane of an LLM fails to respect user permissions," Rees told VentureBeat. Etay Maor, VP of Threat Intelligence at Cato Networks, reached the same conclusion from the adversarial side. "We need an HR view of agents," Maor told VentureBeat at RSAC 2026. "Onboarding, monitoring, offboarding."
Agentic AI trust gap assessment
Use this matrix to evaluate any platform or combination of platforms against the five trust gaps Dickman identified. Note that the enforcement approaches in the right column reflect Cisco's framework.
Trust gap
Current control failure
What network-layer enforcement changes
Recommended action
Agent identity governance
IAM built for human users cannot inventory, scope, or revoke agent identities at machine speed
Agentic IAM registers each agent with defined permissions, an accountable human owner, and a policy-governed access scope
Audit every agent identity in production. Assign a human owner. Define permitted actions before expanding the scope
Blast radius containment
Host-based agents and perimeter controls can be bypassed; flat segments give compromised agents lateral movement
Microsegmentation enforces least-privileged access at the network layer, limiting blast radius independent of host-level controls
Implement microsegmentation for every agent-accessible system. Start with the highest-sensitivity data (PHI, financial records)
Cross-domain visibility
Siloed observability tools create fragmented views; Team A's agent data never correlates with Team B's security telemetry
Network telemetry captures actual system-to-system communications, feeding a unified data fabric for cross-domain correlation
Unify network, security, and application telemetry into a shared data fabric before deploying production agents
Governance-to-enforcement pipeline
No formal process connecting business intent to agent policy to network enforcement
Policy-to-enforcement pipeline translates governance decisions into machine-speed network rules
Establish a formal pipeline from business-intent definition to automated network policy enforcement
Cultural and workflow readiness
Organizations automate existing workflows rather than redesigning for agent-scale processing
Network-generated behavioral data reveals actual usage patterns, informing workflow redesign
Run a 30-day telemetry capture before designing agent workflows. Build around observed data, not assumptions
A broken ankle and a microsegmentation lesson
Dickman grounded his framework in a scenario from his own life. A family member recently broke an ankle, which put him in a hospital exam room watching a medical transcription agent update the EHR, prompt prescription options, and surface patient history in real time. The doctor approved each decision, but the agent handled tasks that previously required manual entry across multiple systems.
The security implications hit differently when it is a loved one's records on the screen.
"I would call it do governance slowly. But do the enforcement and implementation rapidly," he said. "It must be done in machine speed."
It starts with agentic IAM, where each agent is registered with defined permitted actions and a human accountable for its behavior.
"Here's my set of agents that I've built. Here are the agents. By the way, here's a human who's accountable for those agents," Dickman said. "So if something goes wrong, there's a person to talk to."
That identity layer feeds microsegmentation — a network-enforced boundary Dickman says enforces least-privileged access and limits blast radius.
"Microsegmentation guarantees that least-privileged access," Dickman said. "You're not relying on a bunch of host agents, which can be bypassed or have other issues."
If the governance model works for a medical transcription agent handling patient records in an emergency department, it scales to less sensitive enterprise use cases.
Five priorities before agents reach production
1. Force cross-functional alignment now. Define what the organization expects from agentic AI across line-of-business, IT, and security leadership. Dickman sees the human coordination layer moving more slowly than the technology. That gap is the bottleneck.
2. Get IAM and PAM governance production-ready for agents. Dickman called out identity and access management and privileged access management specifically as not mature enough for agentic workloads today. Solidify the governance before scaling the agents. "That becomes the unlock of trust," he said. "Because when the technology platform is ready, you then need the right governance and policy on top of that."
3. Adopt a platform approach to networking infrastructure. A platform strategy enables data sharing across domains in ways fragmented point solutions cannot. That shared foundation is what makes the cross-domain correlation in the trust gap assessment above operationally real.
4. Design hybrid architectures from the start. Agentic AI handles reasoning and planning. Traditional deterministic tools execute the actions. Dickman sees this combination as the answer to token economics: it delivers the intelligence of foundation models with the efficiency and predictability of conventional software. Do not build pure-agent systems when hybrid systems cost less and fail more predictably.
5. Make the first use cases bulletproof on trust. Pick two or three high-value use cases and build them with role-based access control, privileged access management, and microsegmentation from day one. Even modest deployments delivered with best practices intact build the organizational confidence that accelerates everything after.
"You can guarantee that trust to the organization, and that will unleash the speed," Dickman said.
That is the structural insight running through every section of this conversation. The 85% of enterprises stuck in pilot mode are not waiting for better models. They are waiting for the identity governance, the cross-domain visibility, and the policy enforcement infrastructure that makes production deployment defensible. Whether they build on Cisco’s platform or assemble their own, Dickman’s framework holds: identity governance, cross-domain visibility, policy enforcement. None of those prerequisites is optional.
The organizations that satisfy them first will deploy agents at a pace the rest cannot match, because every new agent inherits the trust architecture the first ones required. The ones still debating whether to start will watch that gap widen. Theoretical trust does not ship.
- How enterprises can turn AI experiments into real-world impact
Presented by Nutanix
Across industries, organizations are focused on how to move from AI pilots, proofs of concept, and cloud-based experimentation to deploying it at scale — across real workloads, for real users, in real business environments. VentureBeat spoke with Tarkan Maner, president and chief commercial officer at Nutanix, and Thomas Cornely, EVP of product management, about what that transition demands, and what it will take to get it right.
“AI in general is shifting everything we do, not only in technology, but across all vertical industries, from regulated industries like banking, health care, government, education to non-regulated industries like manufacturing and retail,” Maner said. “As a complete platform company, we welcome this change. It’s creating more opportunities for us as a company to serve our customers in better ways as we move forward.”
But there’s still a practical gap between experimentation and production, Cornely said.
“It’s one thing to do an experiment, to do a prototype. It’s a different thing to take that prototype and deploy it for 10,000 employees,” he explained. “We went from people focusing on training models to chatbots to now doing agents, where the demand and pressures on AI infrastructure are growing exponentially.”
Agentic AI introduces a new layer of enterprise complexity
The rise of agentic AI is what makes this transition especially consequential. These systems introduce multi-step workflows across applications and data sources, along with a degree of autonomy that creates new operational demands.
Enterprises now have to contend with multiple agents running simultaneously, unpredictable and real-time workloads, and the need to coordinate access to infrastructure across teams.
“OpenClaw is making it very easy now for anybody to build agents and run with agents,” Cornely said. “You want those agents to be running on premises with your data. You need to have the right constructs around it to protect the enterprise from what an agent could do.”
As these systems become more autonomous, the challenge extends beyond how they operate to how they interact with enterprise data, systems, and teams.
AI is augmenting human work, not replacing it
Agentic AI is fundamentally an amplifier of human capability rather than a substitute for it, Maner said. The goal for enterprises is not to eliminate human work but to find the right balance between human decision-making, AI-driven automation, and agent-based workflows.
“We believe that there’s going to be love, peace, and harmony between AI, agentic tools, and robotics systems, and human capital,” Maner said. “That harmony can be optimized for better outcomes for businesses, enterprises, governments, and public sector organizations, if the right vendors provide the right tooling and the right services.”
How enterprises are getting started with AI at scale
In practice, the move from experimentation into real-world deployment is where the challenges become most visible. Despite the momentum, many are still working through how to scale AI beyond initial use cases.
As they do, organizations quickly run into practical constraints. Many start in the cloud because of easy access to resources and services, but practical considerations like data, governance and control, and cost quickly come to the forefront.
The cloud can be used to experiment, with the ultimate goal of bringing applications back on premises as they move toward production, using platforms that solve for security and cost.
The use cases gaining the most traction include document search and knowledge retrieval, security and predictive threat detection, software development and coding workflows, and customer support and service operations. In the security realm, banking customers and others in Europe and the U.S. are deploying AI-driven tools including facial recognition and predictive threat detection. Meanwhile, there’s a growing focus on end-to-end, 360-degree customer engagement, from pre-sales through post-sales advocacy, in the customer support industry.
Industry-specific AI transformation is already underway
Across industries, the shift from experimentation to real deployment is already taking shape in distinct ways. In retail, AI is transforming store operations with cameras and robotics used for targeted in-aisle marketing at the moment of purchase decision, while cashier-less checkout is replacing traditional POS systems, and the human capital freed up is being redeployed to back-office and merchandising functions.
In healthcare, Nutanix works with customers on applications spanning diagnosis, treatment, remote health, and hospital operations, with cloud partners including AWS and Azure. In manufacturing and logistics, the transformation is equally significant.
The operational challenges of scaling enterprise AI
As AI use cases scale, enterprises are running into a new class of operational challenges. Managing multiple AI workloads and agents, coordinating infrastructure access across teams, ensuring security and governance, and integrating AI systems with existing business processes are now top-of-mind concerns for IT and business leaders alike.
The gap between AI developers pushing for speed and access, and infrastructure teams responsible for security, uptime, and governance, is one of the defining challenges of this moment.
“Now I’m running agents, and they’re all going to fight to get access to resources to solve my problems,” Cornely said. “What you want now is infrastructure that allows you to set constraints, govern resources.”
The AI factory: a shared platform for production AI
These challenges are driving demand for what Maner and Cornely describe as the AI factory: a shared infrastructure environment that supports multiple users and workloads simultaneously, enabling both experimentation and production while balancing developer agility with enterprise governance.
At GTC 2026, Nutanix announced the Nutanix Agentic AI Solution, a complete platform spanning core infrastructure, Kubernetes-based container services running on a topology-aware hypervisor, and advanced services for building and governing agents.
“We’re launching a complete platform, from core infrastructure through PaaS and advanced PaaS services to the whole management framework for your AI factories,” Cornely said. “Really enabling self-service for the teams that will build these applications in the enterprise.”
Hybrid environments are essential to enterprise AI strategy
Operating this kind of environment requires flexibility across infrastructure. Hybrid infrastructure is not a compromise, but a requirement. Some workloads will always run in the public cloud, while others must remain on premises due to security requirements, regulatory compliance, data sovereignty, or competitive IP considerations.
“Especially in the regulated industries, as sovereignty becomes a bigger issue, data gravity becomes a bigger issue, security, and also a lot of competitive differentiation in the industry, it’s going to depend on what the company wants for their own IP,” Maner said.
This is the foundation of Nutanix’s platform position, he added.
“We are the perfect harmony, bringing those applications, that data, and all the optimization for these use cases end to end, from on-prem to off-prem and in a hybrid mode,” he said. “Doing it not only in one cloud, but for multiple clouds.”
That flexibility also extends to the broader ecosystem. Nutanix works across hyperscalers including AWS, Azure, and Google Cloud, as well as regional service providers and emerging neoclouds. Nutanix offers neoclouds a full software stack to run their own clouds and deliver advanced AI services, giving enterprise customers already running Nutanix a simple extension of compute, networking, and AI capabilities.
Maner described the arrangement as a win for both sides. For enterprises, it means simplified access to hybrid AI services. For neoclouds, it means a proven platform to build on. It’s all automated and secure by default, Cornely added.
“All of those governance problems that now come up with agentic AI are the same problems we’ve been solving for the last 16 years for every other application running in your cloud,” he said.
From pilot to production: operationalizing AI across the enterprise
Ultimately, the goal is not to run a successful AI pilot, but to operationalize AI across real-world use cases, manage infrastructure as a shared resource, support collaboration between infrastructure teams and AI developers, and scale from initial projects to enterprise-wide deployment.
“There’s a massive gap right now between people building AI applications, those AI engineers, those agentic AI developers, and your classical infra teams,” Cornely said. “They need tooling to enable the infra teams, so they can support your AI engineers. That’s what we deliver with our agentic AI solution.”
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