Thousands of Intel experiments on agentic AI workloads found enterprise performance depends on the full system—not just LLM inference—and produced five lessons for moving agents into production.
Six metrics emerged as the most useful operating view: task success rate, cost per task, time per task, throughput, agent density per vCPU, and latency, with P95 task latency flagged as a better warning signal than average CPU use.
Intel said capacity planning should center on agents per vCPU rather than raw agent count, because similar density on 8-vCPU and 16-vCPU systems can produce similar behavior.
Scale-out was the default winner in Intel's tests, boosting capacity and availability while preserving target density; scale-up fit heavier per-agent compute, shared-state limits, memory-locality needs, or licensing constraints.
The company said agentic AI is likely to create value first in rule-based workflows with measurable service levels, including code generation, regression testing, ticket triage, market analysis, and security review.