Ibrar Aziz -Technology Enthusiast

  You Have ACM. You Have ACS. So Why Is Your Platform Still Flying Blind? Most OpenShift platforms have both installed. ACM — Advanced Cluster Management. Manages the fleet. ACS — Advanced Cluster Security. Secures the fleet. Most teams treat them as two separate tools that happen to live on the same Hub. That is the mistake. And the consequences show up quietly — until they show up loudly. What happens when they are not tied together Scenario 1: The CVE that travelled for 11 days. A critical vulnerability lands in a base image running across nine clusters. Without ACM and ACS wired together — someone manually hunts which clusters are affected, raises tickets, chases teams, waits for patches, re-scans. That process took one platform eleven days. The image ran in production, exposed, for eleven days. With both tools tied: ACS spots the affected image across all nine clusters in minutes. ACM Policy flags every cluster non-compliant. The admission controller blocks new deploy...

  Your GPU cluster is running a hotel. Most engineers are still managing it like a house rental. Here's a stat that should bother every platform architect: enterprise GPU utilization commonly sits at 20-30%. Not because demand is low — because the allocation model underneath most AI platforms was built for a world where one workload gets one whole GPU, full stop. Red Hat OpenShift AI just closed that gap architecturally. Dynamic Resource Allocation (DRA) went GA in OpenShift 4.21, and it quietly rewrites how GPUs get shared across a cluster. If you're designing AI infrastructure in 2026, this is the shift to understand. The old model: renting the whole house For years, Kubernetes handled GPUs through the Device Plugin framework. A GPU request was just an integer: "give me 1 GPU." No nuance about size, memory, or whether a fraction would do. To get any sharing at all, teams used static MIG (Multi-Instance GPU) configuration — an admin pre-carves a physical GPU i...

Everyone is building AI demos. The real challenge is building AI agents that are secure, scalable, and production-ready . This is where Red Hat OpenShift AI and Kubernetes provide a significant advantage. Instead of treating AI as a standalone application, OpenShift enables AI agents to run as cloud-native workloads with enterprise-grade security, automation, and observability. How to Build an AI Agent on OpenShift 1. Select the Foundation Model Choose an LLM such as Llama, Mistral, Granite, or another enterprise model, and deploy it using OpenShift AI model serving. 2. Create the AI Agent Use frameworks like LangGraph, LangChain, CrewAI, or Semantic Kernel to define the agent's reasoning, memory, and workflow. 3. Connect Enterprise Data Integrate the agent with: Internal APIs Databases Vector databases for RAG Document repositories Knowledge bases This allows the agent to answer using your organization's data rather than relying only on pretrained knowledge. 4. Containerize t...