mlsanityData to 14 Oct 2026

The Irreversibility Budget: Fleet-Level Risk Accounting and Admission Control for Agent Operating Systems

arXiv 2609.00275 ↗First posted 31 Aug 2026cs.AI cs.DC cs.OScited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 11 of its 24 references that are in the map. First posted on arXiv 31 Aug 2026.

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11 of 24 references.

References · 11

  1. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  2. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
  3. 2017Constrained Policy Optimizationcited by 983
  4. 2020Consistent Estimators for Learning to Defer to an Expertcited by 271
  5. 2017Predict Responsibly: Improving Fairness and Accuracy by Learning to Defercited by 233
  6. 2026Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflowscited by 30
  7. 2026Cordon: Semantic Transactions for Tool-Using LLM Agentscited by 22
  8. 2026Quantifying Trust: Financial Risk Management for Trustworthy AI Agentscited by 8
  9. 2026Insuring Every Action: An Authority Frontier Framework for Runtime Actuarial Control of Autonomous AI Agentscited by 4
  10. 2026When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwritingcited by 3
  11. 2026The Controllability Trap: A Governance Framework for Military AI Agentscited by 1

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  3. 2026Safety Invariants for Agents Orchestrating Irreversible State Transitions: A Four-Dimensional Formalism Evaluated on Public Ledgers ↗
  4. 2026Token Budgets: An Empirical Catalog of 63 LLM-Agent Budget-Overrun Incidents, with an Affine-Typed Rust Mitigation as a Case Study
  5. 2026Cost-Aware Speculative Execution for LLM-Agent Workflows: An Integrated Five-Dimension Method
  6. 2026Spawn Freely, Act Sparingly: Progressive Risk Vesting for Recursive LLM-Agent Trees
  7. 2026The Harness as the Only Mutable Surface: Compliance-Bounded Self-Evolution of LLM Agents in Credit Pipelines, with a Measured Admission Gate
  8. 2026Fault-Tolerant Budget Conservation in Distributed Multi-Agent Delegation