Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates

The field of artificial intelligence is currently abuzz with discussions surrounding "agentic AI" or "AI agents." However, despite the widespread excitement, the term agent itself often lacks a precise, universally agreed-upon definition within these conversations. Recently, significant focus has shifted towards agents built upon large language models (LLMs), leveraging some reasoning and language understanding capabilities to execute complex tasks, interact with external tools, and learn from feedback [53, 56, 63, 66, 67]. This move towards more autonomous, goal-directed LLM systems represents a promising yet challenging frontier in AI development. During this time, AI algorithms have also reached superhuman performance in numerous tasks such as game playing [9,57,62,65] and text and image processing [2, 15, 51]. On the other hand, there are still significant obstacles that modern AI has yet to overcome. Grosz [21] proposed a revised Turing Test to create: "A computer team member that can behave, over the long term and in uncertain, dynamic environments, in such a way that people on the team will not notice that it is not human."