AI agents are currently attracting companies with quick pilot projects, in which it’s relatively easy to verify initial benefits. The true value only becomes apparent when the agent proves itself in day-to-day practice and the company can safely integrate it with its data, systems, and internal policies. This article explains how the demands on performance, monitoring, and infrastructure surrounding the model itself change during the transition from testing to production. It will help clarify when a workstation or a smaller AI server is sufficient for a company and when it needs a more robust solution.
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