Aglet

Prioritize Agent Abstention Cases

Abstention testing should focus on mistakes that change what a user believes or does. Prioritize unsupported precise claims and missed answerable requests, then ambiguous cases that expose the response boundary. Keep stylistic differences outside the first queue during abstention review.

Decide where the work belongs

  1. Rank decision consequence

    Describe what happens if the agent guesses, refuses, or gives a partial answer in each case. Give weight to decisions based on missing evidence, conflicting context, and requests where a wrong confidence signal is hard to detect.

  2. Compare boundary difficulty

    Review answerable, incomplete, conflicting, ambiguous, out-of-scope, and partial-help cases. Identify which case distinguishes uncertainty from inability. Prefer examples with explicit evidence and user next-action expectations for a fair boundary judgment.

  3. Choose an abstention queue

    Select case, answerability rule, owner, evidence request, and review date. Defer low-consequence wording variations with a trigger. If context provenance is weak, prioritize fixing the evidence boundary before judging the response.

What to carry forward

The priority output is an abstention queue tied to decision consequence, boundary difficulty, evidence quality, and user reliance. Start where confidence or refusal could change the user outcome. Keep tone polish separate from the choice to answer, qualify, or decline in this evaluation.

Technical background: Google DeepMind evaluation research.

Keep the decision with the work.

Use a Work Item in Aglet to record the problem, the evidence you have, and the next decision. Add an owner and priority, then keep updates in the discussion so the next person can follow the reasoning.

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