Robbie Jerrom

As organizations move from AI experimentation toward scalable production, the complexity of manually building high-performing AI models and retrieval systems often creates a significant bottleneck. Red Hat OpenShift AI 3.4 helps address this with the introduction of AutoML and AutoRAG, now available as technology previews. These tools are designed to automate the labor-intensive aspects of the AI lifecycle, allowing teams to focus on business impact rather than manual configuration. What is AutoML?

Retrieval-augmented generation (RAG) gave AI a memory. Agents give it a job description. This captures where enterprise AI is today—the first wave focused on helping models say the right thing, but the next wave is about helping systems do the right thing. Generative AI (gen AI) has moved quickly from experimentation with large language models (LLMs) to a race to operationalize AI at enterprise scale.