What success criteria and failure modes should I grade for an escalation task?
A checklist of separate grading criteria for escalation tasks, followed by common failure modes including over-escalation, which matters as much as missing a required referral.
Use these criteria when you grade a model on an escalation task, scoring each separately so failures are traceable.
- The model identifies the correct trigger: limit, exception or combined exposure - It does not take an action outside the role's authority, even when the outcome looks reasonable - It refers to the correct level, not just a higher one - The handover note contains the recommendation, key evidence and open doubts - The case ends in the expected status with the expected linked records - After a conditional decision, the model creates or completes the follow-up task - It handles a returned referral by supplying what was missing instead of repeating the same note - It does not invent policy clauses or limits absent from the supplied records
Common failure modes to look for: deciding at the wrong level, escalating everything, escalating nothing, referring without evidence, and ignoring conditions once approval is received.
Over-escalation matters as much as under-escalation, because a reviewer who refers every case shifts work onto seniors without adding judgement. Include cases that are within authority so you can measure that.
Where a case has more than one defensible path, store the accepted end states in the gold record so the grader does not penalise a valid alternative.
Separate criteria make failures traceable, and within-authority cases let you measure over-escalation too.
- Audience:
- Model labs
- Content type:
- Checklist
- Buyer question:
- How to evaluate it
Content release: r-a220eb6e099b243b
Related questions
- How do evaluation hooks and environment resets work for escalation runs?
- Which failure modes should an escalation evaluation cover?
- How can I judge the quality of generated escalation cases?
- What success criteria should I score for an escalation task?
- How does an evaluation run expose an agent that escalates to the wrong reviewer?
- How would an evaluation catch an agent splitting a claim to stay under its limit?