Ilmu Komputer & AI editorial
Open AccessOA2026
The Delegation Blind Spot: Auditing Product Decisions from Agent Choices
A decision-specific audit workflow that maps observation channels and product-value contrasts to compatible intervals and witness populations, revealing unresolved decisions despite high execution accuracy.
Shivam Guptaยท 2026ยท DOI 10.48550/arXiv.2609.26642
The core problem
The paper addresses a critical blind spot in evaluating AI agents: successful execution does not guarantee that the agent's choices reveal which product improvements users would value. The authors frame this as a decision-specific audit problem, where the goal is to map a declared observation channel and a product-value contrast to compatible intervals and witness populations. The work builds on established identification and decision theory, but its main contribution is an executable measurement workflow and a controlled study of its limits. The central question is whether agent behavior can be used to infer user preferences for product decisions, and under what conditions such inferences are valid. The authors motivate the need for auditing because current evaluations focus on execution accuracy, which may be orthogonal to decision-relevant identification. They propose a source-labeled decision receipt and an offline viewer to inspect audits, emphasizing the importance of diagnosing why a decision is unresolved before collecting more telemetry. The study is entirely synthetic, with no human participants or real customer outcomes, ensuring ethical and reproducible experimentation.
Innovation
The primary result is stark: all 36 conservative primary intervals remain unresolved despite different execution accuracy across models. This means that even when agents execute tasks with high accuracy, their choices do not identify which product improvement users value. The exploratory follow-up with supplied preferences resolves only three of nine comparisons per model, indicating that additional preference data helps but is insufficient. In contrast, the deterministic extractor resolves seven of nine comparisons without any model calls or calibration observations, exposing unnecessary uncertainty introduced by model-generated reports. The multinomial simulations further reveal that structural ambiguity, weak identification, and finite calibration precision each contribute to unresolved decisions. The authors provide a source-labeled decision receipt that records the provenance of each decision-relevant input, and an offline viewer for inspecting the audit. The results motivate preserving decision-relevant structured input and diagnosing why a decision is unresolved before collecting more telemetry. The study contains no human participants or real customer outcomes, and full pro
The paper addresses a critical blind spot in evaluating AI agents: successful execution does not guarantee that the agent's choices reveal which product improvements users would value. The authors frame this as a decision-specific audit problem, where the goal is to map a declared observation channel and a product-value contrast to compatible intervals and witness populations. The work builds on established identification and decision theory, but its main contribution is an executable measurement workflow and a controlled study of its limits. The central question is whether agent behavior can be used to infer user preferences for product decisions, and under what conditions such inferences are valid. The authors motivate the need for auditing because current evaluations focus on execution accuracy, which may be orthogonal to decision-relevant identification. They propose a source-labeled decision receipt and an offline viewer to inspect audits, emphasizing the importance of diagnosing why a decision is unresolved before collecting more telemetry. The study is entirely synthetic, with no human participants or real customer outcomes, ensuring ethical and reproducible experimentation.
The methodology centers on a decision-specific audit that formalizes the relationship between agent choices and product-value contrasts. Let
be the set of agent actions,
the set of product improvements, and
the user value function. The observation channel
maps actions to observable outcomes. The audit seeks to identify the sign of
for two product variants , using only observations from . The authors define compatible intervals for given the observed data and a witness populationโa set of user types consistent with the observations. The workflow involves: (1) declaring the observation channel and product-value contrast, (2) collecting agent choices under controlled conditions, (3) computing compatible intervals and witness populations, and (4) diagnosing unresolved decisions. The controlled study uses a frozen experiment with 4,800 requests to two pinned model snapshots on shared synthetic tasks. An exploratory follow-up of 2,400 calls records supplied preferences and resolves three of nine comparisons per model. A deterministic extractor resolves seven of nine without model calls or calibration observations. Additionally, 14,400 controlled multinomial simulations distinguish structural ambiguity from weak identification and finite calibration precision. The audit is implemented as an executable workflow with an offline viewer for inspection.
Why it matters
The analysis highlights a fundamental blind spot in agent evaluation: execution accuracy is not a proxy for decision-relevant identification. The authors argue that the field must shift from measuring task success to auditing whether agent choices can resolve product decisions. The deterministic extractor's success suggests that much of the uncertainty is artificial, introduced by model-generated reports rather than inherent in the data. This implies that preserving structured input and using deterministic extraction can dramatically improve decision resolution. The multinomial simulations provide a framework for diagnosing the sources of unresolved decisions: structural ambiguity (the observation channel cannot distinguish between product variants), weak identification (the data are insufficient to narrow the interval), and finite calibration precision (noise in the calibration process). The authors propose a source-labeled decision receipt as a practical tool for auditing, which records the origin of each decision-relevant input and enables reproducibility. The offline viewer allows stakeholders to inspect the audit and understand why a decision is unresolved. The paper concludes that before collecting more telemetry, practitioners should diagnose the reason for unresolved decisions and consider whether the observation channel is adequate. The work has implications for AI product development, where agent behavior is often used to infer user preferences, and for the design of evaluation protocols that prioritize decision-relevant identification over execution accuracy.
Who should read this
CS practitioners and researchers
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