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As enterprises move AI agents from single-task assistants into systems that chain multiple actions together, the cost conversation is shifting from "per API call" thinking to something closer to workforce economics.
A different kind of unit economics
Unlike a traditional software subscription, an AI agent's cost scales with how much reasoning it does, not just how many users touch it. Finance teams evaluating agent deployments are increasingly asking for cost-per-completed-task metrics rather than cost-per-seat, a shift that mirrors how call centers price outsourced labor.
Where the budget actually goes
Inference costs get the headlines, but early enterprise deployments report that orchestration, monitoring, and human-in-the-loop review add substantially to total cost of ownership - often more than the underlying model calls themselves.
For business leaders, the practical takeaway is to budget AI agent programs like a new operating function, not a software line item.