Smart Enough, Fast Enough: Choosing the Right Models for Agentic Work
Red Hat, Thursday, September 17th, 2026
Red Hat on matching model size and latency to the demands of agentic AI workloads.
Red Hat examines how to select models for agentic workloads where a task is decomposed into many chained calls.
The post argues that maximum model capability is often the wrong optimization target because latency and cost compound across agent steps.
It discusses where smaller or distilled models are sufficient, and how to route work between tiers of models within an agent. The guidance is framed around running these workloads on OpenShift AI in production.