T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains

2026-06-09

AI Summary

Existing agent evaluation benchmarks are limited in task complexity, realism, and domain diversity, making them inadequate for assessing cross-domain, multi-step reasoning and coordination capabilities. This work proposes a high-fidelity, multi-domain customer interaction benchmark comprising 25 progressively challenging real-world scenarios, introducing for the first time highly complex, interwoven cross-domain tasks that substantially enhance compositional depth, interaction richness, and evaluation rigor. Leveraging both automated metrics and human judgment, the study systematically evaluates 12 leading large language models across dimensions including tool use, multi-step reasoning, and dialogue coherence. The project releases open-source data and code, establishing a reproducible and standardized agent evaluation framework that lays the groundwork for future research on agents operating across diverse real-world settings.

Abstract

Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems. However, existing benchmarks remain limited in task complexity, realism, and domain diversity, and often fail to capture interactions that span multiple domains, limiting their ability to evaluate agents in realistic multi-step settings that require sustained reasoning and coordination. To address these limitations, we introduce T1-Bench, a high-fidelity, comprehensive benchmark for evaluating agentic systems in realistic customer-facing, multi-domain environments, featuring interleaved scenarios that require structured reasoning across multi-turn user-assistant interactions and substantially increasing both compositional complexity and evaluative rigor across 25 domains of varying difficulty. We evaluate T1-Bench using 12 proprietary and open-weight models, providing a reproducible and standardized framework for assessing agent behavior, tool utilization, and conversational quality in complex, multi-step environments. We further complement automatic evaluation with human judgments to strengthen the assessment of qualitative performance. Overall, T1-Bench substantially advances prior benchmarks by increasing task complexity, interaction depth, and domain coverage in simulated multi-domain environments. To facilitate future research on agentic systems, we will publicly release data and evaluation code as open source.

Problem

• benchmarking
• multi-domain agents
• task complexity
• realistic evaluation
• agentic systems

Innovation

• multi-domain agents
• T1-Bench
• tool-calling
• structured reasoning
• agent evaluation