@inproceedings{he-etal-2026-impatient,
    title = "Impatient Users Confuse {AI} Agents: High-fidelity Simulations of Human Traits for Testing Agents",
    author = "He, Muyu  and
      Kumar, Anand  and
      Bakshi, Soumyadeep  and
      Zou, James  and
      Rajani, Nazneen",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1743/",
    pages = "37569--37602",
    ISBN = "979-8-89176-390-6",
    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 TraitBasis, 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 {\ensuremath{\tau}}-Bench to {\ensuremath{\tau}}-bench, where user behaviors are altered via controlled trait vectors. We observe an average 4{\%}{--}20{\%} performance degradation on {\ensuremath{\tau}}-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 {\ensuremath{\tau}}-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."
}