The paper "Apple’s Knowledge Navigator: Why Doesn’t that Conversational Agent Exist Yet?" by Amanda K. Newendorp and colleagues explores the vision of a sophisticated digital personal assistant presented in Apple's 1987 Knowledge Navigator (KN) video. The authors use three theoretical frameworks—Distributed Cognition for Teamwork (DiCoT), Human-Agent Team Game Analysis Framework, and Flows of Power (FoP)—to analyze the human-agent interactions and capabilities shown in the video. They find that while some barriers to creating such agents are technological, others arise from privacy concerns, social and situational factors, trust issues, and financial business models. The paper discusses the need for a new term to describe these agents, which do not rely on traditional human social relationship metaphors. The research provides designers of conversational agents with a roadmap to build more capable and trusted non-human teammates, highlighting the gap between current chatbot technology and the capabilities demonstrated in the KN video. The analysis reveals that the KN agent's advanced conversational skills, such as understanding shared context and maintaining conversation threads, are not yet feasible with today's technology. The paper also explores the implications of these findings for human-agent teaming (HAT) research and design, emphasizing the importance of addressing privacy, trust, and technological constraints to advance the development of conversational agents.The paper "Apple’s Knowledge Navigator: Why Doesn’t that Conversational Agent Exist Yet?" by Amanda K. Newendorp and colleagues explores the vision of a sophisticated digital personal assistant presented in Apple's 1987 Knowledge Navigator (KN) video. The authors use three theoretical frameworks—Distributed Cognition for Teamwork (DiCoT), Human-Agent Team Game Analysis Framework, and Flows of Power (FoP)—to analyze the human-agent interactions and capabilities shown in the video. They find that while some barriers to creating such agents are technological, others arise from privacy concerns, social and situational factors, trust issues, and financial business models. The paper discusses the need for a new term to describe these agents, which do not rely on traditional human social relationship metaphors. The research provides designers of conversational agents with a roadmap to build more capable and trusted non-human teammates, highlighting the gap between current chatbot technology and the capabilities demonstrated in the KN video. The analysis reveals that the KN agent's advanced conversational skills, such as understanding shared context and maintaining conversation threads, are not yet feasible with today's technology. The paper also explores the implications of these findings for human-agent teaming (HAT) research and design, emphasizing the importance of addressing privacy, trust, and technological constraints to advance the development of conversational agents.