Benchmark RadarT1-Bench
T1-Bench evaluates agentic systems through realistic customer-facing, multi-domain tasks. Scenarios involve multi-turn user-assistant interactions across 25 domains with varying difficulty, measuring structured reasoning, tool utilization, and conversational quality. Automatic evaluation is complemented by human judgments.
- Released
- 2026-06-09
- Readiness
- Paper only
- Primary field
- General AI
Why it matters
Existing agent benchmarks lack realism and domain diversity, limiting assessment of sustained multi-step reasoning. T1-Bench addresses this gap by providing interleaved, complex scenarios across multiple domains, enabling more accurate evaluation of agents' practical capabilities in real-world customer service settings.
Motivation
Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.