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title: What's new in Red Hat OpenShift 5 for developers | daily.dev
description: Red Hat OpenShift 5 is now generally available, built on CRI-O 1.36 and Kubernetes 1.36. It adds an MCP server and gateway so AI agents get RBAC-governed,...
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og:description: Red Hat OpenShift 5 is now generally available, built on CRI-O 1.36 and Kubernetes 1.36. It adds an MCP server and gateway so AI agents get RBAC-governed,...
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# What's new in Red Hat OpenShift 5 for developers

**[Red Hat Developer](https://daily.dev/sources/rhdev)** · 9 min read · 0 upvotes · 0 comments

## Summary

Red Hat OpenShift 5 is now generally available, built on CRI-O 1.36 and Kubernetes 1.36. It adds an MCP server and gateway so AI agents get RBAC-governed, read-only-by-default access to cluster resources, plus dynamic GPU resource allocation that lets a single GPU be split into claimable slices. An Agent Sandbox feature, in Technology Preview, runs untrusted AI agent code in VM-isolated Kata containers. Developer Hub 2.1 extends support windows from 6 to 10 months, Podman Desktop gains Podman 6 support, and the console adds OIDC login and 3 new UI themes.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://developers.redhat.com/articles/2026/11/09/red-hat-openshift-5-for-developers>

## Questions this post answers

### What does the MCP server for Red Hat OpenShift actually restrict AI agents from doing?

It defaults to read-only access and enforces standard Kubernetes RBAC, so agents can inspect pipeline runs, query service mesh telemetry, or monitor virtualization resources without being able to modify them. It also integrates with OAuth and OIDC identity providers and tags every action for audit logging. A separate MCP gateway screens traffic for PII, prompt injection, and unsafe content before requests reach MCP servers.

_Teams wiring AI agents into cluster operations can track MCP governance patterns like these on daily.dev._

### How does dynamic resource allocation (DRA) let OpenShift split a single GPU between workloads?

Partitionable devices allow one GPU or network card to be divided into logical slices that can be independently allocated, while consumable capacity lets workloads claim specific portions, such as memory or bandwidth, through individual ResourceClaim requests. Pod status also exposes real-time device health, with device taints, tolerations, and binding conditions supporting finer scheduling control.

_Engineers planning GPU-sharing strategies for AI workloads can follow DRA developments like this on daily.dev._

### How does Red Hat's Agent Sandbox isolate untrusted AI agent code from the rest of the cluster?

Agent Sandbox, available in Technology Preview, runs AI agents in short-lived, VM-isolated execution environments using sandboxed Kata containers rather than standard containers. It keeps pre-provisioned warm pools ready to remove startup delays for interactive agent sessions, and a sandbox router provides access to agent browsers, notebooks, and IDEs.

_Developers evaluating how to run agentic AI code safely can keep up with sandboxing approaches like this on daily.dev._

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---

Tags: [#kubernetes](https://daily.dev/tags/kubernetes), [#mcp](https://daily.dev/tags/mcp), [#openshift](https://daily.dev/tags/openshift)

[View this post on daily.dev](https://daily.dev/posts/what-s-new-in-red-hat-openshift-5-for-developers-pw7pk8ddt)

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