Strategic Decision Support for AI Agents

2026-06-10

AI Summary

This work addresses the critical challenge of ensuring reliable, human-aligned decision-making in AI-agent-dominated systems while minimizing reliance on external support. It proposes the first AI-agent-centric decision support framework that reduces support invocation through a counterfactual “missed-support” error rate control mechanism and a support-value-driven dynamic thresholding strategy. Key contributions include an online adaptive algorithm that operates without distributional assumptions, a real-time calibration mechanism, and a decision optimization approach grounded in counterfactual error modeling and online stochastic exploration. Empirical evaluations demonstrate that the method significantly lowers support usage across diverse scenarios—including information gathering, human–agent collaboration, and tool invocation—while strictly maintaining target error rates.

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.

Problem

• AI agents
• decision support
• reliability
• support usage
• missed-support error

Innovation

• strategic decision support
• AI agents
• counterfactual missed-support error
• adaptive thresholding
• online calibration