Grounding AI agents in biological reality: Building blocks for autonomous wet labs
Google Research
AI agents were integrated with wet-lab automation via the VERID framework and closed-loop lab workflows to run, learn from, and refine experiments grounded in real physical state. VERID’s tests used a three-round aptamer workflow, including hit-rate results rising to 33.5% for myoglobin and 42.1% for α-thrombin after specific wet-lab failure modes were addressed. The work changes lab discovery by adding live 3D state tracking, simulation dry runs, and full execution trace logging so agents can safely recover from failures and iteratively improve target-binding outcomes across cycles.
Why it matters
General Science
