Building the enterprise environment for agentic AI
At a glance
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
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- MIT Technology Review — Read original article
- Published
- Topic
- Technology
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 ability to predictably plan and scale agents.
To better understand some of these dependencies, Intel performed thousands of agentic AI workload experiments.
Our initial findings create and support five practical lessons for enterprise leaders: Agentic AI is a larger systems problem, not just one of inference. The majority of existing agentic AI harnesses are limited and do not measure overall system performance.
Plan capacity is done using agents per virtual CPU (vCPU) density, not agent count. Monitor agent task latency, not just average CPU utilization.
Default to scale-out for systems hosting agents. Reserve scale-up for workloads with heavier per-agent compute or architectural constraints.
Beyond inference: Agents as workflow automation Agentic AI is more than LLM inference. Its enterprise value depends on the full syste
This summary comes from MIT Technology Review. Read the full article at the original source.
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