When Tools Dominate, When Strategies Matter: A Causal Study of Agentic Computational Biology

Agentic AI increasingly automates computational biology workflows, yet performance varies widely and inconsistently across benchmarks, and observational studies cannot separate tool heterogeneity from planning-strategy differences. We introduce Dissect-Audit, a causal-study framework that isolates the two via controlled factorial experiments on single-cell RNA-seq analysis, using a shared registry of validated operations to independently vary the tool layer and planning strategy under do-calculus logic. Across our 23-task core benchmark, tool-layer unification alone eliminates inter-strategy variance (Wilcoxon $p=1.22\times 10^{-4}$) for all eight frontier LLM planners tested: every planner gains quality and sheds run-to-run variance ($+0.08$ to $+0.20$, mean $+0.15$; variance reduced 42–100%), and planners sharply separated under native tools become statistically indistinguishable (Kruskal–Wallis $p=0.61$). Added validation yields no further gain, marking validators as reactive rather than proactive. Boundary experiments show tool quality binds only for in-distribution tasks with a canonical workflow; on out-of-distribution tasks hinging on a methodological decision, inter-strategy variance persists ($0.125\to 0.135$) and a runtime oracle revives the strategy advantage ($+0.57$) — so the governing boundary is oracle $\times$ IID/OOD, not bio-vs-general. We package these diagnoses into a reusable protocol whose transplant uplift self-targets lower-quality agents ($\text{corr}=-0.86$, $p=0.006$).