How do you evaluate whether an agent selected the right cases for review?
Evidence quality depends on selecting the correct review population before assembling support, especially when exclusions can hide the cases that matter.
A polished evidence package can still answer the wrong question if the agent starts with an incomplete review population. In retail operations, a refund review may cover all transactions meeting a supplied rule, including reversals and reopened cases. Selecting only records labelled closed can remove precisely the cases that need attention.
Evaluate population selection before assessing the attached evidence. Supply a generated transaction population, a review boundary and explicit inclusion rules. Keep the expected membership available to the evaluator so omissions remain visible even when every selected case is well supported.
Useful failure categories include excluding awkward cases, including records outside the review boundary and applying an exception without evidence that it qualifies. These failures should remain separate from missing attachments within a correctly selected case.
Ask whether the agent can explain why each excluded category falls outside scope. Where sampling is authorised, inspect whether it follows the supplied sampling rule and reports the resulting limits. Do not let a convincing narrative compensate for an unjustified change to the population.
Evaluate population coverage separately from the quality of evidence collected for individual cases.
- Audience:
- Agent developers
- Content type:
- Explanation
- Buyer question:
- How to evaluate it
Content release: r-a220eb6e099b243b
Related questions
- Which evaluation hooks reveal how an agent assembled its evidence?
- How can an evidence task test resistance to instructions inside retrieved records?
- Which quality checks reveal shortcuts that could undermine an evidence dataset?
- Must an agent's assessment match one reference answer exactly?
- What quality checks should precede an agent evaluation?
- How should an evaluation handle an agent that discovers its own earlier mistake?