AEGIS: An Auditable Evidence-Governed Interface for Cost-Aware AI Harness Selection in Finance

In bank onboarding, an accurate model output may remain non-executable until required evidence and approvals are in place. Obtaining them consumes money, compute, and human capacity and adds latency, yet may unlock a higher-value action. We introduce the Auditable Evidence-Governed Interface for Selection (AEGIS), a cost-aware framework that selects among versioned, pre-commit-mediated harnesses by conditioning jointly on task information, policy, evidence, budget, deadline, and runtime health. At fixed resource and risk prices under a stationary cell law, our frontier theorem identifies the support gap \(D(\theta)\) as the per-decision value of governance information. This value vanishes exactly when each task fibre has a common cost-adjusted optimum; otherwise, task-only routing incurs \(T D(\theta)\) expected structural pseudo-regret. LP duality yields auditable supporting prices for resources and risk; independent read-once evidence packages admit an exact assurance-option rule. Five repeated outer-CV evaluations of 1,000 borrowers show that governance-conditioned routing lowers normalised loss .099 versus task-conditioned routing (borrower-by-repeat 95\% sensitivity interval [.081,.116]) and .090 versus a training-selected fixed policy [.066,.111]; operations loss falls 33.2\% for .018 higher business loss. Across 629 matched cells in four tool-use suites, AEGIS supplies nine of ten cell-weighted and 14 of 15 equal-suite observed nondominated safety--risk--latency--call points. At one resource price, descriptive held-out-goal estimates show 7.31 points higher safe completion, .79 points lower attack success, 1.788 fewer seconds, and .113 fewer calls than suite-fixed. Signed evidence certifies lineage and specified checks, not factual correctness.