Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing Agents

ICLR 2026 Conference Submission14807 Authors

Keywords: mechanistic interpretability, LLM robustness, AI Agents, Quality Assurance, Simulations, RL Environments, Benchmark, Adversarial testing

TL;DR: TraitBasis is a data efficient method for creating high-fidelity, realistic user traits that can be applied to stress testing conversational AI agents.

Abstract:

Despite rapid progress in building conversational AI agents, robustness is still largely untested. Small shifts in user behavior, such as being more impatient, incoherent, or skeptical, can cause sharp drops in agent performance, revealing how brittle current AI agents are. Today’s benchmarks fail to capture this fragility: agents may perform well under standard evaluations but degrade spectacularly in more realistic and varied settings.
We address this robustness testing gap by introducing \ours, a lightweight, model-agnostic method for systematically stress testing AI agents. TraitBasis learns directions in activation space corresponding to steerable user traits (e.g., impatience or incoherence), which can be controlled, scaled, composed, and applied at inference time without any fine-tuning or extra data. Using TraitBasis, we extend τ-Bench to τ-bench, where user behaviors are altered via controlled trait vectors. We observe an average 4%–20% performance degradation on τ-bench across frontier models, highlighting the lack of robustness of current AI agents to variations in user behavior.
Together, these results highlight both the critical role of robustness testing and the promise of TraitBasis as a simple, data-efficient, and compositional tool. By powering simulation-driven stress tests and training loops, TraitBasis opens the door to building AI agents that remain reliable in the unpredictable dynamics of real-world human interactions. We plan to open-source τ-bench, across four domains: airline, retail, telecom, and telehealth, so the community can systematically QA their agents under realistic, behaviorally diverse intents and trait scenarios.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 14807