Once AI raises research output by an order of magnitude, the new problem is trust: how do these conclusions earn confidence? Line-by-line human checking doesn't scale, and 'just trust the AI' is worse.
Reproduction tasks, difference isolation, definition rulings
Settlement files, NAV, dashboards and alerts
Reproduction reports and incident post-mortems, archived
@replication rebuilds the full backtest with zero context and catches a price-adjustment definition bug in the first round.
After the fix, three independent implementations align daily NAV to five decimal places before the definition freezes for production.
On the first live day, two Agents compute NAV independently — one from settlement files, one from position detail — reconciling item by item to the cent.
The post-close backstop checks required artifacts automatically; it once caught a hidden gap where the job ran but the NAV never rolled forward — fixed the same day.
Incident post-mortems go into the self-check list and alert rules the same day; from then on, machines watch for that class of problem.
Scheduled checks on the day's artifact completeness and freshness, alerting on gaps.
A third-party Agent periodically reproduces a core result with zero context.
Every definition ruling's basis and conclusion, archived and traceable.