Synthèse
Average load: It rarely tells the full story. A platform can appear stable while quietly absorbing constant internal work that never surfaces to users. Agentic AI introduces that kind of persistent demand. An agent continues operating in the background even after a user interaction ends. It checks for new inputs, revisits its internal state, then prepares the next action. That ongoing loop changes how infrastructure is consumed. Instead of handling activity in bursts, systems begin to carry a st
Average load: It rarely tells the full story. A platform can appear stable while quietly absorbing constant internal work that never surfaces to users. Agentic AI introduces that kind of persistent demand. An agent continues operating in the background even after a user interaction ends. It checks for new inputs, revisits its internal state, then prepares the next action.
That ongoing loop changes how infrastructure is consumed. Instead of handling activity in bursts, systems begin to carry a steady baseline load. Compute usage becomes continuous rather than intermittent. This shift exposes inefficiencies that might otherwise go unnoticed. A slow query or a fragmented data source affects far more than a single request.
The same issue repeats continuously, which increases both cost and energy consumption over time. This matters even more as inference workloads scale, with projections suggesting they could account for more than 40% of data center demand by 2030.
Source originale
mckinsey.com
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