Agentic Selection Without Feedback: An LLM Agent Designs Cancer Panels by Knowing What Not to Sequence

Designing the target panel for a cell-free DNA cancer assay is a budgeted selection problem with no execution feedback. The panel fixes which genomic regions are sequenced, it dominates development cost, and its only test is a clinical study that arrives after the design is frozen. Current practice mines labeled case-control cohorts, so panels inherit the population they were trained on and can degrade in populations with a shifted cfDNA background, such as patients under with chronic liver disease, where labeled positives are scarce. We ask whether selection from prior information alone can compete with supervised design. We present an LLM agent that constructs a multi-cancer methylation panel for a chronic liver disease population from three inputs, the cancer literature, a reference cell-type methylation atlas, and unlabeled plasma from cancer-free donors in the target population. No tumor sample and no detector response is available at any design stage. On a titration benchmark with 700 background controls and a single fixed detector, the frozen panel reaches 0.7323 AUC against 0.7050 for the strongest commercial panel at matched sequencing cost, and exceeds all comparators under paired tests. Ablations suggests the gain is a function of the panel as a whole and not any specific markers. The agent's contribution is the systematic exclusion of weak regions, which requires prior knowledge but no outcome data. The agent were highly reproducible with any two of five independent runs share 86% to 92% of their selected CpGs. The agent provide unique region when substituting the target population shifts selection far beyond run-to-run variation (Jaccard index 0.73 vs. 0.92) supporting on demand per population panel design. These results suggest that, for budgeted decisions made without feedback, an agent's value lies in reliably rejecting poor candidates rather than in identifying the best ones.