Evaluations

How well the agent drafts ordinary submissions, scored against the underwriter's decisions. Attacks are not counted here.

Improved agent, quick experiment

Experiment: Improved agent, quick, Finished, 2026-10-30 13:16 UTC Choose another
Decisions matching the underwriter
12
of 12 normal cases
Accepted without edits
91.7%
of normal cases
Field accuracy
100.0%
facts that match the answer key
Premium error
0.0%
median gap from the underwriter

Scores

One row for each measure, over the normal cases in this experiment.

MeasureResultWhat it means
Field accuracy100.0%Share of facts pulled from the documents that match the answer key.
Appetite agreement100.0%Share of normal cases where the AI and the underwriter chose the same appetite, meaning whether the insurer wants the risk.
Accepted as is91.7%Share of normal cases the underwriter could approve without an edit.
Corrections per case0.08Average number of edits an underwriter would make to one normal case.
Schedule within 2 points100.0%Share of normal cases where the schedule change is within 2 points of the underwriter's.
Premium error, median0.0%Typical gap between the AI's premium and the underwriter's premium.
Latency, median and 95th12.1 s and 15.3 sTime from the submission arriving to the draft being finished.

By MGA

An MGA is a managing general agent, the agency that writes policies for an insurer.

MGANormal cases rightAppetite agreementAccepted as isPremium error
Harbor Specialty Underwriters7 of 7100.0%100.0%1.1%
Beacon Programs5 of 5100.0%80.0%0.0%

Normal cases right, by full experiment

Each bar counts the normal cases where the AI's decision matched the underwriter's.

Original agent 2026-10-30-12-47 113 of 120
Improved agent 2026-10-30-13-01 117 of 120
Improved + refer all conflicts 2026-10-30-13-31 107 of 120
Improved + Harbor referral rule 2026-10-30-13-49 117 of 120
Improved agent 2026-10-30-14-10 114 of 120
Improved + raw documents and rate table 2026-10-30-14-30 117 of 120