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Building Enterprise AI Agents on Red Hat OpenShift: From Prototype to Production

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 the Application

Package the AI agent into a container image and store it in an enterprise container registry.

5. Deploy on OpenShift

Deploy the container as a Kubernetes Deployment with:

  • Services and Routes for access

  • ConfigMaps and Secrets for configuration

  • Resource limits and autoscaling

  • Persistent storage where needed

6. Enable Observability

Monitor the AI platform using:

  • Prometheus for metrics

  • Grafana for dashboards

  • Loki for logs

  • OpenTelemetry for distributed tracing

7. Automate with GitOps

Use Argo CD or OpenShift GitOps to automate deployments, updates, rollbacks, and version control across environments.

Why Build AI Agents on OpenShift?

The benefits go far beyond simply running an LLM.

  • Enterprise Security – RBAC, OAuth integration, Secrets management, and Network Policies protect AI workloads.

  • High Availability – Kubernetes automatically reschedules pods if a node fails, keeping the agent available.

  • Scalability – Scale inference workloads horizontally based on demand.

  • GPU Optimization – Efficient scheduling and sharing of GPU resources through the NVIDIA GPU Operator.

  • Portability – Run consistently across on-premises, private cloud, and public cloud environments.

  • GitOps Automation – Reliable, repeatable deployments with version-controlled infrastructure.

  • Observability – Full visibility into application health, performance, and model behavior.

  • Multi-tenancy – Safely host multiple teams and AI applications on the same platform.

  • Compliance – Built-in enterprise controls support regulated industries.

Final Thought

An AI agent is only as reliable as the platform it runs on. OpenShift transforms AI from an experimental proof of concept into an enterprise-grade service by combining Kubernetes orchestration, security, automation, GPU management, and observability.

As Platform Engineers, our role is no longer limited to managing clusters—we are building the secure, scalable foundation that powers the next generation of intelligent applications.

#OpenShift #OpenShiftAI #Kubernetes #PlatformEngineering #AIAgents #GenAI #MLOps #CloudNative #GitOps #EnterpriseAI #RedHat #AIInfrastructure

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