Artificial intelligence

Learn how AI observability works with MLflow, an open source tool that can trace an agentic query and find the root cause of discrepancies between AI assistant responses and dashboard data. This post discusses the practical value of tracing an agent and what MLflow adds to an observability stack.

Learn how to generate AI-assisted Ansible playbooks with Red Hat's VS Code extension for faster, more accurate code generation.

Learn how to fine-tune Qwen3-4B for correct tool calls using GRPO on Red Hat OpenShift AI.

Learn how to fine-tune large language models on Red Hat OpenShift AI with Ray and Training Hub for optimized SQL generation using the LoRA algorithm.

Streamline Red Hat Ansible development with the MCP server: Reduce context switching and non-deterministic behaviors for reliable automation.

Secure Claude Code plug-ins: Learn how to protect your repository and implement review discipline for safer third-party code.

Discover the Red Hat OpenShift AI Dashboard for Models-as-a-Service architecture, streamlining AI model deployment on Red Hat OpenShift.

Explore LLM inference on Kubernetes using Red Hat AI on EKS. Trace requests from the Envoy gateway through the EPP scheduler down to individual vLLM pods.

Optimize LLM deployment with Neural Navigator on Red Hat OpenShift AI, reducing cost overruns and latency spikes.

Confidently deploy LLMs with Red Hat support: Learn how to determine if your model is supported by Red Hat's vLLM community.

Battle-test your AI agents before production. Discover how MiDojo's open source framework red-teams tool calls for real-world security and utility.

See how a team upgraded Red Hat OpenShift AI 3.3.2 three to four times faster with an AI coding assistant, reducing engineering effort by 60%.

Reduce observability costs with Red Hat OpenShift AI summarizer, bridging the interpretation gap for cloud-native architectures.

Explore Kubernetes resources for Red Hat AI Inference on Amazon EKS, enabling intelligent routing for your model serving.

Stop guessing RAG settings. Discover how AutoRAG uses fast evaluation sweeps to optimize chunking and retrieval precision for small LLMs on your data.

Learn how to run isolated Llama 3.1 8B workloads on a single NVIDIA H100 GPU using OpenShift, Kubernetes dynamic resource allocation, and NVIDIA MIG.

Improve model reliability at inference time with its_hub: Learn how to select accurate outputs without retraining.

Optimize GPU efficiency with Red Hat OpenShift AI 3.4's flow control for llm-d, ensuring priority-based request queuing and fairness policies.

Learn behavioral testing for agents, catching non-deterministic failures with golden queries and YAML files.

Learn how to build a distributed RAG pipeline with Ray Data on OpenShift AI for high-performance parsing, embedding, and writing to a vector database.

Learn how to preserve existing model knowledge with Orthogonal Subspace Fine-Tuning (OSFT) for fine-tuning large language models in Training Hub.

Boost AI coding agent performance with AGENTS.md and Agent Skills. Learn how to standardize project context, structure tasks, and share skills across teams.

Dive into the Q2’26 edition of Camel integration quarterly digest, covering the

Boost AI and analytics workloads with Kove:SDM on Red Hat OpenShift, enabling applications to access memory resources beyond local node limits.

Combine OpenShift and OpenShell to improve AI agent security, protecting against data exfiltration and container escapes.