Opening the black box: Profiling a secured agentic pipeline on Red Hat OpenShift AI

As enterprises move to autonomous agentic pipelines, 2 critical questions emerge: Can I add security to my agents without killing performance? And, Where should I focus optimization effort to get the most out of my agentic system?Most inference benchmarks test the model in isolation—a raw request to vLLM, a prompt in, tokens out. But production agentic systems don't work that way: Every request passes through an agentic harness that assembles context, manages sessions, injects tool schemas, and optionally provisions a…

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