# When Users Are Happy but Agents Are Wrong: Multi-Dimensional Evaluation of Tool-Augmented Dialogue

arXiv: https://arxiv.org/abs/2510.19186 · HTML: https://mlsanity.com/paper/2510.19186/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023)
- [ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases](https://mlsanity.com/paper/2306.05301/) (2023)
- [MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback](https://mlsanity.com/paper/2309.10691/) (2023)
- [MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use](https://mlsanity.com/paper/2310.03128/) (2023)
- [Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy](https://mlsanity.com/paper/2507.01352/) (2025)
- [Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios](https://mlsanity.com/paper/2401.17167/) (2024)
- [Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey](https://mlsanity.com/paper/2503.22458/) (2025)
- [Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems](https://mlsanity.com/paper/2105.03748/) (2021)
- [ToolTalk: Evaluating Tool-Usage in a Conversational Setting](https://mlsanity.com/paper/2311.10775/) (2023)
- [Reward Models in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2506.15421/) (2025)
- [Simple LLM Prompting is State-of-the-Art for Robust and Multilingual Dialogue Evaluation](https://mlsanity.com/paper/2308.16797/) (2023)
- [Automated test generation to evaluate tool-augmented LLMs as conversational AI agents](https://mlsanity.com/paper/2409.15934/) (2024)
- [On the Benchmarking of LLMs for Open-Domain Dialogue Evaluation](https://mlsanity.com/paper/2407.03841/) (2024)
- [Neural Models and Language Model Prompting for the Multidimensional Evaluation of Open-Ended Conversations](https://mlsanity.com/paper/2509.00841/) (2025)

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

- [CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments](https://mlsanity.com/paper/2603.28569/) (2026)

## Abstract (arXiv)

Evaluating conversational AI systems that use external tools is challenging, as errors can arise from complex interactions among user, agent, and tools. While existing evaluation methods assess either user satisfaction or agents' tool-calling capabilities, they fail to capture critical errors in multi-turn tool-augmented dialogues-such as when agents misinterpret tool results yet appear satisfactory to users. We introduce TRACE, a benchmark of systematically synthesized tool-augmented conversations covering diverse error cases. Evaluation with state-of-the-art conversation evaluation frameworks reveals that all approaches remain far from ideal performance, demonstrating the fundamental difficulty of this benchmark.