Measure-Theoretic Markov Modeling of Blind Mass and Oversight Burden in Agentic AI

Forum topic · 小凯 · 2026-03-27

Summary

This arXiv paper (2603.24582) by Santanu Bhattacharya frames agentic AI in organizations as a sequential decision problem constrained by reliability and oversight cost. When deterministic workflows are replaced by stochastic policies over actions and tool calls, the author argues the key question is not whether a next step appears plausible, but whether the resulting trajectory remains statistically supported, locally unambiguous, and economically governable. The paper develops a measure-theoretic Markov framework for this setting. Its core quantities include state blind-spot mass B_n(tau), state-action blind mass B^SA_{pi,n}(tau), an entropy-based human-in-the-loop escalation gate, and an expected oversight-cost identity defined over the workflow visitation measure. The framework aims to quantify where agent trajectories leave statistically supported regions and how much oversight burden they generate, offering formal tools for governing AI agents in enterprise deployments.

Paper Overview

Field: AI Author: Santanu Bhattacharya Published: 2026-03-25 arXiv: 2603.24582

Abstract

Agentic artificial intelligence (AI) in organizations is a sequential decision problem constrained by reliability and oversight cost. When deterministic workflows are replaced by stochastic policies over actions and tool calls, the key question is not whether a next step appears plausible, but whether the resulting trajectory remains statistically supported, locally unambiguous, and economically governable. We develop a measure-theoretic Markov framework for this setting. The core quantities are state blind-spot mass B_n(tau), state-action blind mass B^SA_{pi,n}(tau), an entropy-based human-in-the-loop escalation gate, and an expected oversight-cost identity over the workflow visitation measure.

Key Concepts

State blind-spot mass B_n(tau): quantifies the probability mass of agent states falling outside statistically supported regions.
State-action blind mass B^SA_{pi,n}(tau): extends the blind-spot notion to the joint state-action space under a stochastic policy.
Entropy-based escalation gate: a criterion for when to trigger human-in-the-loop oversight.
Expected oversight-cost identity: an expression of expected supervision cost over the workflow visitation measure.

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*Auto-collected on 2026-03-27. Original forum post in Chinese; abstract translated and annotated for English readers.*