# What Are You Doing? Effects of Intermediate Feedback from Agentic LLM In-Car Assistants During Multi-Step Processing

arXiv: https://arxiv.org/abs/2602.15569 · HTML: https://mlsanity.com/paper/2602.15569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 87 references that are in the map.
- First posted on arXiv 17 Feb 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 13 of 13)

- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [What Large Language Models Know and What People Think They Know](https://mlsanity.com/paper/2401.13835/) (2024)
- [On the Calibration of Large Language Models and Alignment](https://mlsanity.com/paper/2311.13240/) (2023)
- [Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant](https://mlsanity.com/paper/2502.01390/) (2025)
- [Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods](https://mlsanity.com/paper/2205.00189/) (2022)
- [Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents](https://mlsanity.com/paper/2507.22352/) (2025)
- [CarMem: Enhancing Long-Term Memory in LLM Voice Assistants through Category-Bounding](https://mlsanity.com/paper/2501.09645/) (2025)
- [CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty](https://mlsanity.com/paper/2601.22027/) (2026)
- [Robot Capability and Intention in Trust-based Decisions across Tasks](https://mlsanity.com/paper/1909.05329/) (2019)
- [Integrating Generative Artificial Intelligence in Intelligent Vehicle Systems](https://mlsanity.com/paper/2305.17137/) (2023)
- [Agent2Agent Threats in Safety-Critical LLM Assistants: A Human-Centric Taxonomy](https://mlsanity.com/paper/2602.05877/) (2026)

## Built on by (most cited first, 1 of 1)

- [VoCa: Designing Speech-Canvas Interaction for Voice-Based Conversational Agents](https://mlsanity.com/paper/2610.04706/) (2026)

## Abstract (arXiv)

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.