Enterprises deploying agentic AI may need a new generation of AI servers as conventional GPU-centric infrastructure struggles to efficiently execute multi-step AI workflows, according to researchers from Microsoft Azure and the University of Texas at Austin. Drawing on production telemetry from Microsoft’s Azure cloud and experiments with representative open-source agent frameworks, the researchers found that AI agents spend far more time coordinating models, tools, and orchestration software than conventional inference systems assume. Rather than behaving like standalone LLM requests, agentic applications execute as dynamic workflows that repeatedly move work between CPUs, GPUs, and external services, exposing inefficiencies in today’s server designs.

Full report : Agentic AI could force a rethink of enterprise AI server design, researchers say.