Asimov described an AI agent workflow aimed at automating expression vector design for cell line development, linking bio-foundation models to computer-aided genetic design. The approach targets a bottleneck in converting binder design into manufacturable molecules, including the screening-heavy path from lead optimization through stable pools. In the company’s webinar outline, Asimov said AI-designed vectors outperformed prior best designs and that rapid stable pool data helped determine partner lead advancement. It also cited model-predicted chain pairing improvements for multispecific formats without reducing titer. For process development and platform teams, the emphasis is on shifting vector selection from empiricism toward measurement-guided, in-cell model learning—while the real test will be reproducibility across molecules, formats, and manufacturing conditions.