Paying for Outcomes Is an Adjudication Problem

Outcome-based pricing for AI agents is presented as an adjudication challenge rather than a usage-metering problem, because buyers, vendors, auditors, and accountants need a trusted way to determine whether an agent achieved a durable, legitimate result. The proposal recommends compiling a task-specific success specification before work begins, then using a pinned and calibrated “decider bundle” of models, rules, evidence sources, thresholds, and pricing terms to assess each work unit. Its architecture prioritizes independently verified system-of-record facts, escalates ambiguous cases from fast probabilistic models to deeper model review and human panels, and records every decision in an append-only, bitemporal, hash-chained ledger that supports replay, disputes, reversals, and auditability. Outcomes mature only after a defined window for late evidence such as reopened tickets, reverted code, or chargebacks, while deterministic contract functions translate finalized fulfillment grades and AI work share into prices. Drawing lessons from energy, healthcare, advertising, telecom, and other outcome-based industries, the design stresses pre-agreed baselines, neutral measurement, safeguards against gaming, and explicit allocation of ambiguity. The document proposes experiments to test whether independent evidence access, tenant-specific calibration, and a hybrid facts-plus-model cascade can achieve low overbilling error while automatically deciding a large share of coding and other agent tasks, while acknowledging unresolved issues around accounting treatment, privacy, shared-credit measurement, data rights, regulatory restrictions, and the cost of expert labeling.

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