Strategic Decision Support for AI Agents
Traditionally, decision support studies how humans use ML models to make better decisions. In modern agentic systems, AI agents act on behalf of users, reversing roles. This paper proposes a framework to minimize support usage while controlling counterfactual missed-support error. Optimal policy is a threshold rule; online algorithm with adaptive thresholding and calibration-on-the-fly reduces unnecessary support. Experiments show reliable error control and reduced support usage.
[2606.12587] Strategic Decision Support for AI Agents
[Submitted on 10 Jun 2026]
Title:Strategic Decision Support for AI Agents
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Abstract:Traditionally, decision support studies how humans use machine learning models to make better decisions. In modern agentic systems, this division of roles is increasingly reversed: AI agents act on behalf of users, while humans and tools becomes support mechanisms around them. This role reversal brings reliability concerns to the forefront, since agentic errors can be consequential and agent behavior must remain aligned with human goals and constraints. Departing from the classical view of decision support, we revisit its two basic principles, the cost--value tradeoff of seeking support and the role of uncertainty quantification, in a setting where AI agents are the central actors. We propose a framework for strategic decision support for AI agents through an optimization problem that minimizes support usage subject to controlling a counterfactual missed-support error: the probability that the agent acts alone on instances where support would have materially improved its output. At the population level, we show that the optimal policy is a threshold rule on the value of support. Building on this structure, we develop an online algorithm that adaptively thresholds such a score and uses randomized exploration to control missed-support error without distributional assumptions. We further introduce a calibration-on-the-fly method that reduces unnecessary support calls online. We instantiate this framework across diverse scenarios, including information gathering, human--AI collaboration, and tool use, showing how each can be modeled through the same strategic decision-support lens. Experiments across these settings show that our method reliably controls the target error while substantially reducing support usage in practice.
Subjects:
Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2606.12587 [cs.AI]
(or arXiv:2606.12587v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.12587
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sima Noorani [view email] [v1] Wed, 10 Jun 2026 18:34:36 UTC (3,860 KB)
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