TraceTrial-Bench measures AI agent harness reliability and failures
Original title: Trial by Trace: Benchmarking AI Agent Harnesses for Reliability and Failure Attribution
Across 3,420 runs, harness scores span 59.8% to 72.0%, and 31.1% succeed despite a failed criterion.
TraceTrial-Bench evaluates agent harnesses across context, execution, governance, lifecycle, observability, tools, and verification. It includes 95 expert tasks with executable or state-based ground truth and a trajectory-tracing module. Separate scoring covers task success and harness reliability, while evidence and failure-attribution modules support run-level diagnosis. Tests of four harnesses and nine models produced 3,420 runs.
- The benchmark defines seven harness components and 95 mechanism-oriented tasks.
- Macro-averaged harness scores range from 59.8% to 72.0%.
- 31.1% of runs succeed while failing at least one harness criterion.
- The evaluation covers four harnesses and nine models.
From the original article
1 min read at huggingface.co
LLM agents are increasingly used to solve real-world problems, and their effectiveness depends on both the underlying models and the harnesses that support their operation. However, most existing benchmarks focus on model capabilities and overall agent performance, with limited attention to the evaluation of agent harnesses.
By Zhiyong Cao, Hengjie Zhu, Yongxin Wang · huggingface.co
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.