Throughout history, knowledge has shifted from oral tradition to writing and then to digital archives, each shift accompanied by new preservation conventions. Today, as laboratory experiments are increasingly carried out by intelligent agents, knowledge is crossing another boundary: from human‑readable records to machine‑readable ones.\ \ This paper introduces VerifiedLab, a multi‑agent harness framework that facilitates this human‑to‑machine transition. VerifiedLab converts research output from passive papers into active systems that accelerate downstream use. In the framework, a team of AI scientists assumes distinct roles: a principal investigator plans the experiments, a librarian extracts evidence directly from the records, specialists draft the narrative, and a gate together with a critic checks each claim against the measured reliability of its data before deciding to release or withhold it.\ \ Extensive experiments on perovskite solar cells demonstrate the approach. VerifiedLab recovers known physical trends, anticipates devices reported a year later, and provides a trust metric for each inherited claim, allowing AI scientists to work within a quantified reliability envelope. By scaling verification compute at inference time, VerifiedLab opens a new pathway for knowledge dissemination and machine‑driven collaboration among AI co‑scientists.\ \ Review