license: cc-by-sa-4.0
language:
- eng
language_details: en
tags:
- task-completion
- agents
- llm-agents
- dialogue-system
- task-oriented-dialogue-system
- user-persona
- user-simulator
- role-playing
task_categories:
- text-generation
configs:
- config_name: all
data_files:
- split: test
path: output_jsonl/*.jsonl
- config_name: multi_8domain
data_files:
- split: test
path: output_jsonl/multi_8domain.jsonl
- config_name: multi_11domain
data_files:
- split: test
path: output_jsonl/multi_11domain.jsonl
- config_name: attraction
data_files:
- split: test
path: output_jsonl/attraction.jsonl
- config_name: bar
data_files:
- split: test
path: output_jsonl/bar.jsonl
- config_name: cafe
data_files:
- split: test
path: output_jsonl/cafe.jsonl
- config_name: cruise
data_files:
- split: test
path: output_jsonl/cruise.jsonl
- config_name: dessert
data_files:
- split: test
path: output_jsonl/dessert.jsonl
- config_name: flight
data_files:
- split: test
path: output_jsonl/flight.jsonl
- config_name: food&dining
data_files:
- split: test
path: output_jsonl/food&dining.jsonl
- config_name: hotel
data_files:
- split: test
path: output_jsonl/hotel.jsonl
- config_name: kayak
data_files:
- split: test
path: output_jsonl/kayak.jsonl
- config_name: liveshow
data_files:
- split: test
path: output_jsonl/liveshow.jsonl
- config_name: vehicle
data_files:
- split: test
path: output_jsonl/vehicle.jsonl
- config_name: multi_2domain_attraction_cafe
data_files:
- split: test
path: output_jsonl/multi_2domain_attraction_cafe.jsonl
- config_name: multi_2domain_attraction_dessert
data_files:
- split: test
path: output_jsonl/multi_2domain_attraction_dessert.jsonl
- config_name: multi_2domain_food&dining_hotel
data_files:
- split: test
path: output_jsonl/multi_2domain_food&dining_hotel.jsonl
- config_name: multi_2domain_hotel_vehicle
data_files:
- split: test
path: output_jsonl/multi_2domain_hotel_vehicle.jsonl
- config_name: multi_2domain_liveshow_food&dining
data_files:
- split: test
path: output_jsonl/multi_2domain_liveshow_food&dining.jsonl
- config_name: multi_2domain_vehicle_attraction
data_files:
- split: test
path: output_jsonl/multi_2domain_vehicle_attraction.jsonl
- config_name: multi_3domain_attraction_food&dining_cafe
data_files:
- split: test
path: output_jsonl/multi_3domain_attraction_food&dining_cafe.jsonl
- config_name: multi_3domain_flight_hotel_bar
data_files:
- split: test
path: output_jsonl/multi_3domain_flight_hotel_bar.jsonl
- config_name: multi_3domain_hotel_attraction_vehicle
data_files:
- split: test
path: output_jsonl/multi_3domain_hotel_attraction_vehicle.jsonl
- config_name: multi_3domain_vehicle_food&dining_hotel
data_files:
- split: test
path: output_jsonl/multi_3domain_vehicle_food&dining_hotel.jsonl
- config_name: multi_4domain_flight_hotel_attraction_vehicle
data_files:
- split: test
path: output_jsonl/multi_4domain_flight_hotel_attraction_vehicle.jsonl
- config_name: multi_4domain_hotel_vehicle_food&dining_bar
data_files:
- split: test
path: output_jsonl/multi_4domain_hotel_vehicle_food&dining_bar.jsonl
T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains
T1-Bench is a high-fidelity benchmark for evaluating task-completion and role-playing agents across 25 domains, including 11 single-domain and 14 multi-domain settings. It provides 76 tools and extensive human annotations, enabling systematic evaluation of agents in realistic, policy-grounded multi-domain interactions with natural user–assistant role-playing.
T1-Bench is a fully automated benchmark for evaluating the tool-calling capabilities of conversational AI agents across diverse service domains in task-oriented settings. The framework simulates end-to-end user–agent interactions without requiring human intervention at inference time, where a User Agent generates realistic customer utterances conditioned on predefined task goals and a tool-augmented Assistant Agent responds by invoking domain-specific tools/APIs and producing outputs grounded in tool results, prior conversational context, and domain-specific policies. Designed to capture the sequential and interactive nature of real-world service workflows across multi-domain scenarios, T1-Bench requires agents to maintain conversational state, reason over prior tool outputs, and execute multi-step operations such as search, filtering, booking, modification, and cancellation. Because all interactions are grounded in deterministic datasets and executable tools, the benchmark enables reproducible and fine-grained evaluation of agent behavior, tool-use decisions, and task completion performance.
The paper is accepted at EMNLP Findings 2026. Check our ArXiv.
Dataset structure
Each row has the following columns:
domain_id: the domain name concatenated with a row index (e.g.attraction-0).domain: the domain name, matching the source folder name.user_template: the persona, user profile, and goal used to drive the user simulator.benchmark_success_criteria: a JSON-stringified object describing the exact-match tool calls and LLM-as-judge metrics used to score the conversation.
Domains
11 single-domain configs: attraction, bar, cafe, cruise, dessert, flight, food&dining, hotel, kayak, liveshow, vehicle.
14 multi-domain configs, prefixed with multi_Xdomain_ where X is the number of entities combined: multi_2domain_attraction_cafe, multi_2domain_attraction_dessert, multi_2domain_food&dining_hotel, multi_2domain_hotel_vehicle, multi_2domain_liveshow_food&dining, multi_2domain_vehicle_attraction, multi_3domain_attraction_food&dining_cafe, multi_3domain_flight_hotel_bar, multi_3domain_hotel_attraction_vehicle, multi_3domain_vehicle_food&dining_hotel, multi_4domain_flight_hotel_attraction_vehicle, multi_4domain_hotel_vehicle_food&dining_bar, multi_8domain, multi_11domain.
Results
Code
Please refer to the official implementation repository: https://github.com/CapitalOne-Research/t1-bench
Contact
E-mail: Genta Indra Winata or Amartya Chakraborty.
License
The dataset is licensed under CC-BY-SA 4.0.
Citation
If you find this dataset useful, please cite the following work
@article{winata2026t1,
title={T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains},
author={Winata, Genta Indra and Chakraborty, Amartya and Lin, Yuzhen and Rao, Swasthi P and Siingh, Shikhhar and Lu, Houhan and Bathaee, Nadia and Hatwar, Sriharsha and Dashore, Paresh and Jain, Anmol and others},
journal={arXiv preprint arXiv:2606.11070},
year={2026}
}