Reversibility-Aware Staged Delegation for Enterprise Agentic AI: A Real-Options and Resilience Framework for Irreversible Actions

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• Semantic Scholar2026-07-22 07:15:10
  Reversibility-Aware Staged Delegation for Enterprise Agentic AI: A Real-Options and Resilience Framework for Irreversible Actions

  Enterprise agentic artificial intelligence (AI) increasingly converts model outputs into consequential actions involving payments, records, customer communications, infrastructure, and regulated decisions. Existing safeguards commonly emphasize refusal, confidence thresholds, expected loss, or human approval, but they insufficiently distinguish a recoverable task failure from an irreversible or externally propagated harm. This paper develops Reversibility-Aware Staged Delegation (RASD), a multidisciplinary decision framework integrating AI governance, resilience engineering, transaction processing, real-options reasoning, and human-centered automation. RASD introduces an Action Recoverability Index, Non-Recoverable Exposure, and an option-value decision rule that allocates each proposed action among direct execution, staged commit, human review, and block/defer modes. The staged mode separates preparation, validation, commitment, and compensation so that an agent can make progress while preserving the organization’s ability to inspect, reverse, or contain side effects. A formal dominance condition shows when staging creates greater expected value than direct execution. The framework is evaluated in a Monte Carlo design comprising 120,000 synthetic enterprise tasks across 240 episodes, including a controlled distribution shift. RASD achieved a mean net value of 7.408 normalized units per task, compared with 5.735 for a confidence-threshold policy and 5.282 for an expected-loss gate. Its severe-incident rate was 0.390%, versus 5.937% and 4.166%, respectively, while preserving positive value after distribution shift. RASD had a higher raw task-failure rate than the expected-loss gate, demonstrating that failure frequency alone is an inadequate safety metric when recovery and consequence containment differ. The findings support a shift from binary autonomy decisions toward recoverability-preserving execution architectures and provide operational guidance for auditability, human escalation, and risk-adjusted enterprise value creation.