Strategic Decision Support for AI Agents
Quick Answer
This paper proposes a framework for strategic decision support in AI agents, focusing on minimizing support usage while controlling missed-support errors.
Quick Take
An online algorithm is developed to adaptively manage support thresholds, demonstrating effectiveness across various scenarios like human-AI collaboration and information gathering, ultimately reducing unnecessary support calls and maintaining alignment with human goals.
Key Points
- Framework minimizes support usage while controlling counterfactual missed-support errors.
- Optimal policy is a threshold rule based on the value of support.
- Online algorithm uses randomized exploration to manage support thresholds.
- Calibration-on-the-fly method reduces unnecessary support calls.
- Experiments show reliable error control and significant support reduction.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 12587v1 Announce Type: new 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 , 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.
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