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, posted on zhichai.net, proposes a measure-theoretic Markov framework for governing agentic AI systems in organizations. The author frames agentic AI 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 critical question is whether generated trajectories remain statistically supported, locally unambiguous, and economically governable. The framework introduces four core quantities: 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 derived over the workflow visitation measure. These tools quantify where an agent's behavior lacks statistical support and provide a principled rule for escalating control to humans, along with an economic identity for anticipated oversight burden. The work targets researchers and practitioners concerned with reliability, safety, and governance of autonomous AI agents.

This paper, shared on zhichai.net under the [论文] (paper) tag, was authored by Santanu Bhattacharya and released on arXiv on 2026-03-25 (arXiv:2603.24582).

Overview

Agentic artificial intelligence (AI) in organizations is modeled 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 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. 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
An expected oversight-cost identity over the workflow visitation measure

Chinese Abstract (from the forum post)

The forum post includes a Chinese summary describing agentic AI in organizations as a sequential decision problem bounded by reliability and oversight cost, and introducing the same four core constructs: state blind-spot mass, state-action blind-spot mass, entropy-gated human escalation, and the expected oversight-cost identity based on the workflow visitation measure.

References

arXiv: 2603.24582

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