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Enterprise AI: how to assess agent output, permissions and inference costs

What can you use from AI deployment reports when planning staffing, permissions and operating costs? I examine Anthropic’s employment findings alongside Radha Basu’s account of iMerit’s specialist evaluator network. Higher output and more agent activity do not establish an equivalent gain in human productivity. Similarly, iMerit’s selection and oversight model is not independent proof of better evaluation quality.

At Bayer Crop Science, the reported question-resolution rate belongs to a service with scoped, read-only tools and structured validation. I connect that boundary with Hina Shamsi’s description of Morgan Stanley’s workflows and Socure’s distinction between detecting an agent, identifying its principal and authorizing an action. Nexus AI’s underground tuberculosis screening adds a related operational question: how does an AI flag lead to a separate confirmatory test?