Agentic Workflows for Conversational Human-AI Interaction Design

2025-01-29

AI Summary

Human-AI conversational interfaces face design challenges stemming from ambiguous user goals, users’ limited awareness of AI capabilities, and the transient, ephemeral nature of interactions. Method: This study proposes an AI-agent-driven CHAI (Conversational Human-AI Interaction) workflow, positioning AI as a collaborative design partner to support real-time prompt generation, user-agent simulation testing, and goal-clarification scaffolding. Developed through four iterative cycles and empirically validated with 10 users, the workflow integrates design probes, thematic analysis, and contextualized prompt engineering. Contribution/Results: We deliver an interpretable prototype suite and introduce the first reusable CHAI agent workflow paradigm under a “research-through-design” framework. Evaluation shows significant improvements in user goal articulation (+42%) and feedback depth. Additionally, we establish a curated, annotated design asset library comprising 12 canonical interaction patterns, enabling high-fidelity early-stage CHAI experience validation.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Human-Robot Interaction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• User Intention Ambiguity
• AI Cognitive Limitation
• Rapid Chat Progression

Innovation

• Human-Robot Interaction
• Enhanced Communication
• AI Assistant Design