# ATOD: An Evaluation Framework and Benchmark for Agentic Task-Oriented Dialogue Systems

arXiv: https://arxiv.org/abs/2601.11854 · HTML: https://mlsanity.com/paper/2601.11854/

## Key facts

- No citing papers in the map yet (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 31 of its 57 references that are in the map.
- First posted on arXiv 17 Jan 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, 30 of 31)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
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- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025)
- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://mlsanity.com/paper/2404.04475/) (2024)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://mlsanity.com/paper/2506.07982/) (2025)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation](https://mlsanity.com/paper/2308.08239/) (2023)
- [Towards Lifelong Dialogue Agents via Timeline-based Memory Management](https://mlsanity.com/paper/2406.10996/) (2024)
- [Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems](https://mlsanity.com/paper/2105.03748/) (2021)
- [RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems](https://mlsanity.com/paper/2012.14666/) (2020)
- [Large Language Models as Zero-shot Dialogue State Tracker through Function Calling](https://mlsanity.com/paper/2402.10466/) (2024)
- [Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation](https://mlsanity.com/paper/2310.07397/) (2023)
- [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022)
- [SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking](https://mlsanity.com/paper/2402.02285/) (2024)
- [Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator](https://mlsanity.com/paper/2210.14529/) (2022)
- [T1: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planning](https://mlsanity.com/paper/2505.16986/) (2025)
- [Large Language Models as User-Agents for Evaluating Task-Oriented-Dialogue Systems](https://mlsanity.com/paper/2411.09972/) (2024)
- [TD-EVAL: Revisiting Task-Oriented Dialogue Evaluation by Combining Turn-Level Precision with Dialogue-Level Comparisons](https://mlsanity.com/paper/2504.19982/) (2025)
- [LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues](https://mlsanity.com/paper/2403.00462/) (2024)
- [HierTOD: A Task-Oriented Dialogue System Driven by Hierarchical Goals](https://mlsanity.com/paper/2411.07152/) (2024)
- [AgentChangeBench: A Multi-Dimensional Evaluation Framework for Goal-Shift Robustness in Conversational AI](https://mlsanity.com/paper/2510.18170/) (2025)
- [Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction Estimation](https://mlsanity.com/paper/1908.07064/) (2019)
- [Toward More Accurate and Generalizable Evaluation Metrics for Task-Oriented Dialogs](https://mlsanity.com/paper/2306.03984/) (2023)

## Abstract (arXiv)

Agentic task-oriented dialogue (TOD) requires systems to track concurrent goals, dependencies, and long-horizon state. We examine goal-lifecycle recovery from fixed dialogue trajectories. ATOD contains 1,000 synthetic dialogues annotated for six advanced-TOD properties, and ATOD-Eval defines metrics for dependency-sensitive completion, memory recall, and proactivity. We implement a symbolic-vector memory evaluator for ATOD-Eval. Among five prompt-only predictors and six matched-backbone memory baselines, our evaluator is the only configuration above 90% in both goal detection F1 and conditional status accuracy on medium dialogues. On complex dialogues, no baseline exceeds it on both metrics; it has the highest conditional status accuracy within the memory-based block and the lowest measured per-turn latency. These experiments assess lifecycle tracking rather than interactive agent task success. A cross-family judge swap and a manual audit with 94.0% agreement provide initial checks on measurement reliability. Code and data will be released at https://github.com/amazon-science/ATOD.