Ibrar Aziz -Technology Enthusiast

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...