README.md

metadata

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_and_dining
    data_files:
      - split: test
        path: output_jsonl/food_and_dining.jsonl
  - config_name: hotel
    data_files:
      - split: test
        path: output_jsonl/hotel.jsonl
  - config_name: kayak_rental
    data_files:
      - split: test
        path: output_jsonl/kayak_rental.jsonl
  - config_name: live_show
    data_files:
      - split: test
        path: output_jsonl/live_show.jsonl
  - config_name: vehicle_rental
    data_files:
      - split: test
        path: output_jsonl/vehicle_rental.jsonl
  - config_name: attraction_cafe
    data_files:
      - split: test
        path: output_jsonl/attraction_cafe.jsonl
  - config_name: attraction_dessert
    data_files:
      - split: test
        path: output_jsonl/attraction_dessert.jsonl
  - config_name: attraction_food_dining_cafe
    data_files:
      - split: test
        path: output_jsonl/attraction_food_dining_cafe.jsonl
  - config_name: flight_hotel_attraction_rental
    data_files:
      - split: test
        path: output_jsonl/flight_hotel_attraction_rental.jsonl
  - config_name: flight_hotel_bar
    data_files:
      - split: test
        path: output_jsonl/flight_hotel_bar.jsonl
  - config_name: hotel_attraction_rental
    data_files:
      - split: test
        path: output_jsonl/hotel_attraction_rental.jsonl
  - config_name: hotel_rental_food_bar
    data_files:
      - split: test
        path: output_jsonl/hotel_rental_food_bar.jsonl
  - config_name: hotel_rental
    data_files:
      - split: test
        path: output_jsonl/hotel_rental.jsonl
  - config_name: live_show_food_dining
    data_files:
      - split: test
        path: output_jsonl/live_show_food_dining.jsonl
  - config_name: vehicle_rental_attraction
    data_files:
      - split: test
        path: output_jsonl/vehicle_rental_attraction.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.

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

bibtex