Claude-enabled protein binder design showed a meaningful experimental signal in a TREM2 campaign, demonstrating progress in AI-assisted biotech tooling. In a hackathon, autonomous AI agents, including Claude Sonnet 4.6, submitted binder designs that were tested in a wet lab. The collective AI-designed set achieved a 34.3% binder hit rate, with 12 out of 35 designs binding to TREM2. This outcome aligns with previously cited ranges for Claude-enabled design work. However, this figure represents the combined performance of six agents, not Claude Sonnet 4.6 individually. Human designers achieved a slightly higher hit rate of 38.5%, with 25 out of 65 designs binding TREM2. While human designs yielded a higher affinity binder, the AI-generated binders also demonstrated high affinity. The experiment highlights that AI agents can produce usable experimental candidates, not just theoretical ideas. Crucially, wet-lab validation remains essential for assessing binding results. The TREM2 campaign demonstrates that AI-driven workflows can participate in a design-to-test loop, leading to wet-lab outcomes. However, a single target and short campaign timeframe do not establish performance across diverse targets or address critical aspects like safety and efficacy. While AI may streamline candidate generation, it does not inherently accelerate clinical development timelines or reduce associated risks. Governance and scientific accountability remain paramount as these workflows mature.