SEAM: Reproducibility, Auditability, and Stability for Agentic LLM Systems

Agentic systems need reproducible executions and evidence explaining why fresh decisions disagree. We present SEAM (Seal, Explain, Abstain, Mechanize), a client-side architecture that normalizes execution order, replays recorded interactions, tests explanations of divergence, withholds unsupported stability claims, and selects deterministic replacements. Controlled studies connect each operation to measurable outcomes. On a 100-slide CAMELYON17 lymph-node subset, each processed six times per arm, ordered admission restores bitwise agreement in 500 of 500 reference-run heatmap comparisons; a separate two-run analysis removes 203 matched-lesion size-class changes. Across four recordings of a model-directed ledger loop, ordering reduces cases with varying decisions from 4–5 of 6 to 0–1. A diagnostic panel with 30 cases and eight repeats exposes the cost of treating repeated calls as independent: the sealed arm’s nominal one-sided 95% pair-risk upper bound is 0.068, while a finite-sample case-level bound for the same target is 0.262. On 300 three-factor attribution instances, sequential testing raises answer coverage from 51.0% to 85.3% with pruning held fixed; every returned sequential explanation matches the injected cause. A stricter procedure bounds erroneous returns under explicit sampling and separation assumptions. Greedy selection misses a beneficial joint replacement on one panel case. SEAM makes repeatability an execution property and stability claims accountable to recorded evidence.