Alex Bauer argues that business teams need more than confident model answers. Clear intent and structured company knowledge help agents understand what a task means, while citations make underlying assumptions easier to inspect. He illustrates this with a website project that needed a reliable product-capability reference before agents could produce useful copy.
Alex Bauer describes a librarian agent that supplies definitions, business rules, documentation and lessons from failed queries just when another agent needs them. A question about quarterly pipeline, for example, depends on the company's fiscal calendar and qualification rules rather than a generic interpretation of those words.
Alex Bauer also outlines a jury-and-judge workflow for questions without one empirically correct answer. Independent analysts gather cited evidence; a separate judge weighs their reasoning and expands the review when consensus is weak. He closes by arguing that important tasks need a capable model and harness, not merely an AI feature attached to a familiar application.
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