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This research addresses the critical safety challenge of managing cumulative risk in fleets of autonomous LLM agents, proposing an "irreversibility budget" to prevent systemic overdraws caused by individually authorized actions. By treating irreversible effects like code deployment or financial transactions as a first-class resource with a shared limit, the framework aims to stop aggregate harm even when local safety gates pass. The study highlights that while current per-effect controls can allow fleet-level risk breaches up to 48 times the tenant's limit, accurate pricing remains a complex open problem due to heterogeneous and adversarial effects.
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The Irreversibility Budget: Fleet-Level Risk Accounting and Admission Control for Agent Operating Systems

arXiv2 min read

arXiv:2609.00275v1 Announce Type: new Abstract: Fleets of LLM agents now externalize effects that cannot be fully undone: they move money, deploy code, delete data, and disclose information. Current controls check one effect at a time, so a fleet of individually authorized agents can overdraw its principal's risk under a shared trigger while every local gate stays correct. We propose the irreversibility budget, a cumulative account of residual value-at-risk that a trusted runtime maintains for each principal across agents, workflows, and tenants. Treating irreversibility as a first-class resource, the runtime charges each effect its residual loss below the agent and denies the marginal effect once the aggregate would overdraw the budget. Getting the price right is hard, because effects are heterogeneous, adversarially declared, and correlated. We perform a controlled study in which per-effect gates admit fleet-level overdraws of up to 48 times the tenant's risk limit while the budget holds every correctly charged run within that limit. Conservative, dependency-aware pricing remains the central open problem for a deployable design.

Abstract:Fleets of LLM agents now externalize effects that cannot be fully undone: they move money, deploy code, delete data, and disclose information. Current controls check one effect at a time, so a fleet of individually authorized agents can overdraw its principal's risk under a shared trigger while every local gate stays correct. We propose the irreversibility budget, a cumulative account of residual value-at-risk that a trusted runtime maintains for each principal across agents, workflows, and tenants. Treating irreversibility as a first-class resource, the runtime charges each effect its residual loss below the agent and denies the marginal effect once the aggregate would overdraw the budget. Getting the price right is hard, because effects are heterogeneous, adversarially declared, and correlated. We perform a controlled study in which per-effect gates admit fleet-level overdraws of up to 48 times the tenant's risk limit while the budget holds every correctly charged run within that limit. Conservative, dependency-aware pricing remains the central open problem for a deployable design.

Artificial Intelligence (cs.AI)

arXiv:2609.00275 [cs.AI]

(or arXiv:2609.00275v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.00275 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 257 words

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