Quantifying Trust: Financial Risk Management for Trustworthy AI Agents

Abstract

Prior work on trustworthy AI emphasizes model-internal properties such as bias mitigation, adversarial robustness, and interpretability. As AI systems evolve into autonomous agents deployed in open environments and increasingly connected to payments or assets, the operational meaning of trust shifts to end-to-end outcomes: whether an agent completes tasks, follows user intent, and avoids failures that cause material or psychological harm. These risks are fundamentally product-level and cannot be eliminated by technical safeguards alone because agent behavior is inherently stochastic. To address this gap between model-level reliability and user-facing assurance, we propose a complementary framework based on risk management. Drawing inspiration from financial underwriting, we introduce the \textbf{Agentic Risk Standard (ARS)}, a payment settlement standard for AI-mediated transactions. ARS integrates risk assessment, underwriting, and compensation into a single transaction framework that protects users when interacting with agents. Under ARS, users receive predefined and contractually enforceable compensation in cases of execution failure, misalignment, or unintended outcomes. This shifts trust from an implicit expectation about model behavior to an explicit, measurable, and enforceable product guarantee. We also present a simulation study analyzing the social benefits of applying ARS to agentic transactions. ARS's implementation can be found at https://github.com/t54-labs/AgenticRiskStandard.

Figures (9)

• Figure 1: ARS is a transaction-layer assurance standard for agentic services that converts stochastic, outcome-level risk into explicit settlement rules. Without ARS, users must prepay agents (and in fund-moving tasks, also hand over execution capital), exposing them to non-delivery, misexecution, and downstream harms. With ARS, service fees are locked in an escrow vault and released only upon successful evaluation; for fund-moving tasks, users can optionally purchase underwriting coverage while the service provider posts collateral before principal is released. This shifts trust from model-internal reliability to auditable, enforceable guarantees over product-level outcomes.
• Figure 2: The requestor first sends a task specification to the business agent. Both parties may then enter a negotiation loop to refine the agreement. Once consensus is reached, the finalized agreement is recorded and a hash code is generated for future reference.
• Figure 3: Fee track in the transaction phase: The requestor locks the service fee in an escrow vault before execution. After the business agent submits the execution evidence, an evaluator checks its validity. If the evidence passes evaluation, the escrowed fee is released to the business agent; otherwise, the funds are refunded to the requestor.
• Figure 4: Principal Track in the transaction phase: When execution involves user funds (principal), additional financial risk is introduced. ARS therefore introduces an underwriter to provide protection. If the requestor agrees to pay the premium and the business agent locks the required collateral, the underwriter guarantees compensation to the user according to the protection policy in case the task fails.
• Figure 5: AP2+ARS. AP2 provides authorization evidence and bounded delegation, and ARS adds settlement semantics over the authorized transaction.