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Introduction
The artificial intelligence landscape has officially crossed the threshold from experimentation to production-scale operations, and the industry is waking up to a hard truth: building models is no longer the challenge—operating them at scale is. As AI workloads explode across hybrid cloud environments, edge deployments, and geo-distributed GPU clusters, enterprises are realizing that traditional IT operations frameworks simply cannot handle the complexity. Enter AI Ops systems: a new class of unified operating layers designed to manage data, compute, and intelligent systems at scale. This article explores three groundbreaking AI Ops systems that are redefining how organizations build, deploy, and govern AI infrastructure in 2026.
Learning Objectives
- Understand the architectural shift from traditional IT operations to AI-1ative operational frameworks
- Master the deployment and configuration of agentic AI operations platforms across hybrid environments
- Implement security-hardened AI Ops workflows with proper governance, observability, and compliance controls
You Should Know
- GreenLake Intelligence: HPE’s Agentic AI Framework for Hybrid Cloud Operations
HPE has made a decisive move at HPE Discover 2026, positioning GreenLake Intelligence as the unified operating model for the agentic enterprise. This platform introduces a centralized agent registry, intelligent planning and orchestration capabilities, and governance controls designed to coordinate AI agents across infrastructure, applications, and operational workflows.
What This Does: GreenLake Intelligence transforms how organizations manage AI agents by providing visibility into AI utilization, token consumption tracking, and operational cost visibility across multi-vendor AI factories and workloads. The OpsRamp Operations Copilot uses telemetry correlation and AI-driven root cause analysis to proactively identify operational issues and accelerate troubleshooting workflows.
Step‑by‑Step Implementation:
- Deploy GreenLake Intelligence – Access through HPE GreenLake portal; enable the agentic AI framework for your hybrid cloud environment
- Configure Agent Registry – Register all AI agents across your infrastructure using the centralized agent registry; assign governance policies and permission matrices
- Set Up OpsRamp Operations Copilot – Deploy the Operations Copilot to monitor AI agents and LLMs; configure utilization thresholds and token consumption alerts
- Integrate with ServiceNow – Leverage the HPE-ServiceNow partnership to create a single source of truth for agentic IT operations, spanning infrastructure observability to autonomous service delivery
Linux Verification Commands:
Check GreenLake agent status on Linux nodes curl -X GET https://<greenlake-endpoint>/api/v1/agents/status \ -H "Authorization: Bearer $GREENLAKE_TOKEN" | jq . Monitor GPU utilization across AI workloads nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv Validate agent governance policies kubectl get policies -1 ai-agents --context=greenlake-cluster
Windows PowerShell Equivalent:
Query GreenLake agent status
Invoke-RestMethod -Uri "https://<greenlake-endpoint>/api/v1/agents/status" `
-Headers @{Authorization="Bearer $env:GREENLAKE_TOKEN"} | ConvertTo-Json
Monitor GPU metrics (with NVIDIA Windows driver)
nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv
2. DRACONEX: The Federated Autonomous Agent Operating System
Perhaps the most ambitious entry in the AI Ops space is DRACONEX, a Federated Autonomous Agent Operating System that collapses six historically-separate operational disciplines—DevSecOps, IT Operations, Security Operations, Governance Risk and Compliance (GRC), Site Reliability Engineering, and IT Service Management—into a single coordinated autonomous platform.
What This Does: DRACONEX converts natural-language operator intent into validated, policy-gated, compliance-evidenced infrastructure actions across hybrid cloud, on-premises, and air-gapped environments. The architecture comprises a strategic “Captain” layer powered by a 70-billion-parameter dense transformer (DRACONEX-70B-v1) and ten domain-specialized 8-billion-parameter agent “brains” (DRACONIDs), each operating as an independent inference process with its own tool registry, permission matrix, and autonomy loop.
Key Differentiator: Unlike Mixture-of-Agents systems that share a single model and require continuous network connectivity, DRACONIDs are first-class processes with persistent state, independent failure domains, and the ability to continue operations when disconnected from the strategic brain. This makes it ideal for air-gapped and edge environments.
Step‑by‑Step Deployment:
- Deploy the Captain Layer – Spin up the DRACONEX-70B-v1 instance on an NVIDIA RTX Pro 6000 Blackwell GPU (or equivalent) with FP8 precision
- Initialize DRACONID Agents – Deploy the ten domain-specialized 8B-parameter agents; each agent handles a specific operational domain (Security, ITSM, GRC, etc.)
- Configure the Federated Mesh – Establish knowledge-flow connections between the Captain and DRACONIDs across hybrid, on-prem, and air-gapped nodes
- Enable Compliance-as-Execution – The system produces ATO-grade audit evidence as a primary output of normal operation, eliminating separate artifact-collection phases
Security Hardening Commands:
Verify DISA STIG compliance (DRACONEX incorporates 206 real DISA STIGs and NIST 800-53 Rev. 5)
Run STIG compliance check on agent nodes
sudo oscap xccdf eval --profile stig --results /tmp/stig-results.xml /usr/share/xml/scap/ssg/content/ssg-ubuntu-2204-ds.xml
Validate the three-party CAGE-FLY-Operator handshake for infrastructure write access
Only agent class with infrastructure write access is gated behind this handshake
curl -X POST https://<draconex-endpoint>/api/v1/cage-handshake \
-H "Content-Type: application/json" \
-d '{"operator_id": "$OPERATOR_ID", "action": "provision", "environment": "air-gapped"}'
- k0smos Stack: Geo-Distributed AI Operations Across Fragmented Infrastructure
Modern AI architectures are built on the assumption of centralized, homogeneous data centers. In reality, infrastructure is messy. The k0smos stack—an open-source CNCF project—provides the architectural foundation for operating geo-distributed AI infrastructure across private clouds, research environments, edge deployments, and mixed generations of hardware.
What This Does: The k0smos stack divides responsibilities across three technical layers. At the core is k0s, a fully CNCF-conformant Kubernetes distribution packaged as a single, zero-dependency binary that runs natively on almost any Linux environment. k0smotron operates as the engine for hosted control planes, deploying k0s control planes as isolated, versioned pods inside a central management cluster. k0rdent provides the declarative management plane for multi-cluster lifecycle orchestration, establishing a GitOps-driven workflow where clusters are declared, versioned, and audited as infrastructure-as-code.
Step‑by‑Step Implementation:
- Deploy k0s on Edge Nodes – Install the zero-dependency k0s binary across fragmented edge nodes, bare-metal servers, and resource-constrained VMs:
curl -sSL https://get.k0s.sh | sudo sh sudo k0s install controller --single sudo k0s start
- Set Up k0smotron Management Cluster – Deploy k0smotron as a Kubernetes operator in your central management cluster; configure hosted control planes for each geo-distributed environment
- Configure k0rdent for Multi-Cluster Management – Define cluster templates using Kubernetes-1ative APIs; establish GitOps workflows:
k0rdent ClusterTemplate example apiVersion: k0rdent.k0sproject.io/v1beta1 kind: ClusterTemplate metadata: name: ai-training-cluster spec: providers:</li> </ol> - aws - on-prem nodePools: - name: gpu-pool size: 4 instanceType: p4d.24xlarge
4. Enable Cross-Site Networking – Configure multi-cluster service discovery and network policies to span on-prem clusters, cloud regions, and edge deployments
Kubernetes Verification Commands:
Verify multi-cluster connectivity kubectl get clusters --all-1amespaces --context=management-cluster Check GPU resource allocation across geo-distributed nodes kubectl get nodes -o wide | grep -E "gpu|nvidia" Validate GitOps sync status kubectl get applications -1 argocd
What Undercode Say:
- Key Takeaway 1: The industry is shifting from “build AI” to “operate AI” – organizations that fail to adopt AI Ops systems will struggle with agent sprawl, operational complexity, and governance failures as AI becomes the operating system of the enterprise
- Key Takeaway 2: The three AI Ops systems profiled represent distinct architectural approaches – GreenLake Intelligence (enterprise hybrid cloud), DRACONEX (federated autonomous agents with air-gap support), and k0smos (open-source geo-distributed Kubernetes) – each solving different operational challenges
Analysis: The convergence toward AI operating systems is not merely a technological trend; it represents a fundamental restructuring of how enterprises manage intelligence infrastructure. As Josh Salmanson, VP of Cyber Defensive Practice at Leidos Inc., noted, “The adversaries have picked up on the new technology a lot faster than most of the enterprises have”. This urgency demands that security teams embed AI Ops frameworks with zero-trust architecture, compliance-as-execution, and automated governance controls from day one. The platforms discussed here are not optional add-ons—they are becoming the control plane for enterprise intelligence. Organizations must evaluate these systems based on their ability to handle agent sprawl, enforce policy across hybrid environments, and provide auditable compliance evidence.
Prediction:
- +1 The AI Ops market will consolidate around three major architectural patterns by 2028: agentic AIOps platforms (like GreenLake Intelligence), federated autonomous agent OSes (like DRACONEX), and open-source Kubernetes-1ative stacks (like k0smos), creating a clear vendor landscape for enterprise buyers
- +1 Compliance-as-execution will become a regulatory requirement, with frameworks like NIST 800-53 and DISA STIGs being automatically enforced by AI Ops systems rather than manually audited
- -1 Organizations that delay AI Ops adoption face a 3-5 year competitive disadvantage as agent sprawl and operational debt compound, with Gartner predicting 40% of AI production failures will stem from operational issues rather than model quality by 2027
- -1 The skills gap in AI operations will widen dramatically – traditional IT operations teams lack the GPU cluster management, model governance, and agent orchestration expertise required to run these systems effectively
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