From Biased Chatbots to Biased Agents: Examining Role Assignment Effects on LLM Agent Robustness

Researchers have published a new study on arXiv (February 12, 2026) examining how demographic-based persona assignments affect Large Language Model (LLM) agent performance, revealing that biases in autonomous agents pose more direct operational risks than previously documented biases in text generation. The systematic case study represents the first comprehensive investigation into how persona-induced biases impact agent task performance beyond text generation, as LLMs increasingly deploy as autonomous agents capable of real-world actions. This research addresses a critical gap in understanding the operational consequences of biased AI systems that interact with the physical world. The study comes at a time when LLMs are rapidly transitioning from text-generation tools to autonomous agents capable of performing complex tasks with tangible consequences. While previous research has extensively documented biases in chatbot responses when assigned specific personas, the operational impacts of these biases when agents make decisions or take actions in the real world remain underexplored. The researchers specifically examined how demographic-based persona assignments influenced agent robustness and task performance across various scenarios. The findings suggest that biased persona assignments can significantly compromise agent reliability and decision-making quality, potentially leading to discriminatory outcomes or operational failures in critical applications. This research carries significant implications for organizations deploying LLM agents in fields such as healthcare, finance, customer service, and autonomous systems, where biased decision-making could have serious consequences. The study underscores the urgent need for bias mitigation strategies and rigorous testing protocols before deploying autonomous LLM agents in real-world applications with substantial impact.