Building the enterprise environment for agentic AI
MIT Technology Review AI Keegan Sheedy, Lucas Melo
Intel conducted thousands of experiments on agentic AI workloads in enterprise environments and identified five practical lessons for running software agents that automate business tasks across systems. The company extended Terminal-Bench, an open-source benchmarking tool, to measure agent performance beyond just language model inference, using deterministic record-replay of LLM responses across a task mix including compilation, database operations, and machine learning training. Enterprises should plan capacity using agent density per vCPU rather than agent count, monitor task latency instead of average CPU utilization, and scale out systems by default to support production workloads on established business processes like code creation and ticket triaging.
Why it matters
For the enterprise, the promise of agentic AI is much more than just a better chatbot. It is software agents that execute business tasks end-to-end across people, business workflows, data, and systems. The platform best-suited to run agents is built with proper CPU capacity, resilient data access, policy-aware tool use, observability, memory management, and the…
