One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI
This addresses usability issues for users of multi-agent conversational AI systems, though it is incremental as it builds on existing approaches to agent orchestration.
The paper tackles the problem of users needing to learn multiple conversational agents for complex tasks by comparing interaction experiences: one agent for all vs. user choice of agents. It shows that users significantly prefer abstracting agent orchestration, with responses rated within 1% of human-selected answers.
Conversational agents have been gaining increasing popularity in recent years. Influenced by the widespread adoption of task-oriented agents such as Apple Siri and Amazon Alexa, these agents are being deployed into various applications to enhance user experience. Although these agents promote "ask me anything" functionality, they are typically built to focus on a single or finite set of expertise. Given that complex tasks often require more than one expertise, this results in the users needing to learn and adopt multiple agents. One approach to alleviate this is to abstract the orchestration of agents in the background. However, this removes the option of choice and flexibility, potentially harming the ability to complete tasks. In this paper, we explore these different interaction experiences (one agent for all) vs (user choice of agents) for conversational AI. We design prototypes for each, systematically evaluating their ability to facilitate task completion. Through a series of conducted user studies, we show that users have a significant preference for abstracting agent orchestration in both system usability and system performance. Additionally, we demonstrate that this mode of interaction is able to provide quality responses that are rated within 1% of human-selected answers.