The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence
Source: arXiv:2603.24582v1 | Announce Type: New
Abstract
Agentic artificial intelligence (AI) in organizations presents a complex sequential decision problem constrained by factors such as reliability and oversight cost. As deterministic workflows transition to utilizing stochastic policies for actions and tool interactions, a crucial question emerges: rather than simply assessing whether the next step appears plausible, we must determine whether the resulting trajectory remains statistically robust, locally unambiguous, and economically manageable.
Framework Development
In this context, we introduce a measure-theoretic Markov framework specifically designed for evaluating agentic AI systems. The framework includes several core components:
- State Blind-Spot Mass: B_n(tau)
- State-Action Blind Mass: B^SA_{pi,n}(tau)
- Entropy-Based Human-in-the-Loop Escalation Gate
- Expected Oversight-Cost Identity: Over the workflow visitation measure
Empirical Instantiation
We applied this framework to the Business Process Intelligence Challenge 2019 purchase-to-pay log, which comprises 251,734 cases, 1,595,923 events, and 42 distinct workflow actions. A log-driven simulated agent was constructed using an 80/20 chronological split of the process data.
Main Findings
The primary empirical insight gained from this study is that a large workflow can appear statistically supported at the state level while still exhibiting significant blind mass regarding next-step decisions. Notably, refining the operational state to encompass case context, economic magnitude, and actor class leads to an expansion of the state space from 42 to 668. This refinement increases the state-action blind mass from 0.0165 at tau=50 to 0.1253 at tau=1000.
Accuracy Tracking
On the held-out data split, the measure m(s) = max_a pi-hat(a|s) demonstrated a tracking capability for realized autonomous step accuracy within an average margin of 3.4 percentage points. This finding underscores the potential for statistical rigor in assessing agentic AI systems.
Implications of the Framework
The metrics and quantities defined within this framework not only delineate statistically credible autonomy but also inform the expected oversight burden. The practical application of this framework was showcased through a large-scale enterprise procurement workflow and is intended for direct implementation in engineering processes where operational event logs are readily available.
Conclusion
This study presents a robust Markovian framework aimed at enhancing the reliability and oversight of agentic artificial intelligence in organizational settings. By focusing on the interplay between stochastic policies and economic governance, we pave the way for more accountable and effective AI deployment strategies.