RunPod published a guide to agentic AI workflows and the infrastructure patterns behind them. The post argues that agents behave differently from ordinary single model calls because they plan, loop, and burst across tasks.
Those behavior patterns affect GPU usage, orchestration needs, and cost predictability.
For infrastructure teams, the takeaway is that agent workloads need capacity planning around workflows and retries, not just per-request inference throughput.