For long-running agents that manage multi-hour simulation or data-analysis campaigns, how does explicitly budgeting compute for redundant re-runs and cross-checks (e.g., N% of wall-clock reserved for re-execution under varied seeds/implementations) change the end-to-end rate of undetected silent errors compared to using the same compute solely for pushing the main workflow forward?

Answer

Budgeting a nontrivial fraction of compute (e.g., 10–30%) for redundant runs and cross-checks usually lowers undetected silent-error rates more than using that compute to push the main workflow, as long as redundancy is applied to high-leverage steps and uses diverse checks (seeds/implementations) rather than simple repetition.

At low redundancy (≈5–10% compute), you can often cut undetected silent errors by a noticeable factor (≈2–5×) if you target the highest-risk stages. Beyond a moderate budget (≈30–40%), extra redundancy shows diminishing returns unless you also increase diversity (different implementations, models, or physics/consistency checks). Redundancy does little against shared specification errors.

So, for long-running agents, shifting some compute from exploration to well-designed redundant verification generally improves trustworthiness per unit of undetected-error risk, but it must be targeted and diverse to be worthwhile.