"What Are You Doing?": Effects of Intermediate Feedback from Agentic LLM In-Car Assistants During Multi-Step Processing
Agentic AI assistants that autonomously perform multi-step tasks raise open questions for user experience: how should such systems communicate progress and reasoning during extended operations, especially in attention-critical contexts such as driving? We investigate feedback timing and verbosity from agentic LLM-based in-car assistants through a controlled, mixed-methods study (N=45) comparing planned steps and intermediate results feedback against silent operation with final-only response. Using a dual-task paradigm with an in-car voice assistant, we found that intermediate feedback significantly improved perceived speed, trust, and user experience while reducing task load - effects that held across varying task complexities and interaction contexts. Interviews further revealed user preferences for an adaptive approach: high initial transparency to establish trust, followed by progressively reducing verbosity as systems prove reliable, with adjustments based on task stakes and situational context. We translate our empirical findings into design implications for feedback timing and verbosity in agentic assistants, balancing transparency and efficiency.
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Johannes Kirmayr
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2026-02-18T09:08:12.567Z
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Johannes Kirmayr
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johanneskirmayr
6995681c8d17d1ee8c10ed0c
Raphael Wennmacher
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2026-02-20T09:05:34.653Z
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Raphael Wennmacher
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rpgraffi
6995681c8d17d1ee8c10ed0d
Khanh Huynh
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2026-02-23T09:46:24.433Z
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Khanh Huynh
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khahuynh
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Lukas Stappen
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2026-02-18T09:08:13.970Z
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Lukas Stappen
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lstappen
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Elisabeth André
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2026-02-20T09:05:46.083Z
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Elisabeth André
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ElisabethAndre
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Florian Alt
admin_assigned
2026-02-20T09:05:52.291Z
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Florian Alt
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floffi
"What Are You Doing?": Effects of Intermediate Feedback from Agentic LLM In-Car Assistants During Multi-Step Processing
Agentic AI assistants that autonomously perform multi-step tasks raise open questions for user experience: how should such systems communicate progress and reasoning during extended operations, especially in attention-critical contexts such as driving? We investigate feedback timing and verbosity from agentic LLM-based in-car assistants through a controlled, mixed-methods study (N=45) comparing planned steps and intermediate results feedback against silent operation with final-only response. Using a dual-task paradigm with an in-car voice assistant, we found that intermediate feedback significantly improved perceived speed, trust, and user experience while reducing task load - effects that held across varying task complexities and interaction contexts. Interviews further revealed user preferences for an adaptive approach: high initial transparency to establish trust, followed by progressively reducing verbosity as systems prove reliable, with adjustments based on task stakes and situational context. We translate our empirical findings into design implications for feedback timing and verbosity in agentic assistants, balancing transparency and efficiency.
Users prefer adaptive feedback mechanisms in in-car AI assistants, starting with high transparency to build trust and then reducing verbosity as reliability increases, particularly in attention-critical driving scenarios.
agentic AI assistants multi-step tasks user experience feedback timing verbosity dual-task paradigm in-car voice assistant task complexity interaction contexts adaptive approach transparency efficiency