For businesses and developers considering AI agents, the token consumption reality demands a pragmatic shift in how we evaluate their utility. The promise of autonomous agents—handling complex workflows without human intervention—is compelling, but the associated compute costs can quickly undermine return on investment. A task that seems simple, like scheduling a meeting or generating a report, might incur significant cloud expenses if executed by an agent due to hidden context overhead. Successful adoption requires moving beyond fascination with capability to rigorous cost-benefit analysis: measuring not just what an agent can do, but what it costs to do it reliably at scale. This means investing in observability tools to track token usage per agent, setting strict budgets, and carefully scoping agent applications to high-value tasks where the autonomy gains justify the expense—rather than deploying agents broadly for tasks better handled by simpler, cheaper automation or human oversight.