Evaluations

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

Improved + raw documents and rate table, full experiment

Experiment: Improved + raw documents and rate table, full, Finished, 2026-10-30 14:30 UTC Choose another
Decisions matching the underwriter
117
of 120 normal cases
Accepted without edits
94.2%
of normal cases
Field accuracy
100.0%
facts that match the answer key
Premium error
0.6%
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 agreement97.5%Share of normal cases where the AI and the underwriter chose the same appetite, meaning whether the insurer wants the risk.
Accepted as is94.2%Share of normal cases the underwriter could approve without an edit.
Corrections per case0.06Average number of edits an underwriter would make to one normal case.
Schedule within 2 points96.7%Share of normal cases where the schedule change is within 2 points of the underwriter's.
Premium error, median0.6%Typical gap between the AI's premium and the underwriter's premium.
Latency, median and 95th15.3 s and 20.7 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 Underwriters57 of 6095.0%95.0%1.2%
Beacon Programs60 of 60100.0%93.3%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