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This research proposes a standardized framework, the Agent Reliability Profile, to validate and benchmark autonomous AI agents specifically within high-stakes financial environments. By defining clear operating boundaries and establishing a three-tier assurance ladder from institutional assertion to independent verification, the paper addresses critical trust gaps that currently hinder the scalable deployment of agentic systems. The work shifts focus from raw capability metrics to measurable safety guarantees, offering regulators and vendors a concrete method for assessing agent behavior before full-scale adoption.
Surse care acoperăarXiv, LessWrong
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Agent Reliability Profiles in Financial Services

arXiv3 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2610.04123v1 Announce Type: cross Abstract: AI agents can take actions. At times, those actions can go beyond what is intended. Agent reliability can be defined as assurance that an agent will stay within intended bounds and operate within limits. Today, there is no shared framework or language for describing, validating, and benchmarking the reliability of agentic deployments in financial services. This makes it difficult for financial institutions, vendors, and regulators to assess and trust agents at scale, thus limiting the pace of development and adoption. A standardized, shared representation of agent reliability would fill the gap. This paper introduces the Agent Reliability Profile, a per-agent unit of assurance evidence for agent deployments in financial services. Each Profile records a bounded, falsifiable claim, this agentic system reliably functions within its operating boundary. We define "operating boundary" as an agent having; (1) a defined autonomy tier, (2) a defined operational design domain, (3) defined classes of action, and (4) a defined control envelope. Production assurance progresses through three levels while the Profile schema remains constant: a Profile Builder compiles a Level 1 Asserted Profile from institutional evidence, a Profile Validator tests the deployment in its own environment to produce a Level 2 Validated Profile, and operation of the same tests by a qualified independent assessor produces a Level 3 Verified Profile. Separately a Benchmarked Profile reports results comparable across institutions under reference conditions. We describe the architecture, the artifact, the assurance ladder, the comparability flag, associated tools, an evaluation methodology, applications for financial institutions and supervisors, limitations, and a staged implementation program.

Abstract:AI agents can take actions. At times, those actions can go beyond what is intended. Agent reliability can be defined as assurance that an agent will stay within intended bounds and operate within limits. Today, there is no shared framework or language for describing, validating, and benchmarking the reliability of agentic deployments in financial services. This makes it difficult for financial institutions, vendors, and regulators to assess and trust agents at scale, thus limiting the pace of development and adoption. A standardized, shared representation of agent reliability would fill the gap. This paper introduces the Agent Reliability Profile, a per-agent unit of assurance evidence for agent deployments in financial services. Each Profile records a bounded, falsifiable claim, this agentic system reliably functions within its operating boundary. We define "operating boundary" as an agent having; (1) a defined autonomy tier, (2) a defined operational design domain, (3) defined classes of action, and (4) a defined control envelope. Production assurance progresses through three levels while the Profile schema remains constant: a Profile Builder compiles a Level 1 Asserted Profile from institutional evidence, a Profile Validator tests the deployment in its own environment to produce a Level 2 Validated Profile, and operation of the same tests by a qualified independent assessor produces a Level 3 Verified Profile. Separately a Benchmarked Profile reports results comparable across institutions under reference conditions. We describe the architecture, the artifact, the assurance ladder, the comparability flag, associated tools, an evaluation methodology, applications for financial institutions and supervisors, limitations, and a staged implementation program.

Computers and Society (cs.CY); Artificial Intelligence (cs.AI)

K.4.0; K.4.3; K.4.1

arXiv:2610.04123 [cs.CY]

(or arXiv:2610.04123v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2610.04123 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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