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Agentic Workflows for Conversational Human-AI Interaction Design

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arxiv 2501.18002 v1 pith:LYDLPNCH submitted 2025-01-29 cs.HC

Arthur Caetano , Kavya Verma , Atieh Taheri , Radha Kumaran , Zichen Chen , Jiaao Chen , Tobias Höllerer , Misha Sra This is my paper · ORCID
classification cs.HC
keywords usersdesignchaiworkflowsagenticchallengesconversationalgoals
verification ladder T0 review T1 audit T2 compute T3 formal
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Conversational human-AI interaction (CHAI) have recently driven mainstream adoption of AI. However, CHAI poses two key challenges for designers and researchers: users frequently have ambiguous goals and an incomplete understanding of AI functionalities, and the interactions are brief and transient, limiting opportunities for sustained engagement with users. AI agents can help address these challenges by suggesting contextually relevant prompts, by standing in for users during early design testing, and by helping users better articulate their goals. Guided by research-through-design, we explored agentic AI workflows through the development and testing of a probe over four iterations with 10 users. We present our findings through an annotated portfolio of design artifacts, and through thematic analysis of user experiences, offering solutions to the problems of ambiguity and transient in CHAI. Furthermore, we examine the limitations and possibilities of these AI agent workflows, suggesting that similar collaborative approaches between humans and AI could benefit other areas of design.

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