Researchers introduced EVOHARNESSBENCH, a benchmark for evaluating LLM-based agents as their tool harnesses evolve over time, addressing the gap in current agent benchmarks that assume static environments. The benchmark tests agents' ability to adapt to new capabilities and changing tools, which is critical for real-world deployment.
Modern LLM-based agents operate through a harness of tools, reusable skills, and specialist agents that shapes what they observe and what they can do. In practice, this harness continually evolves as new capabilities are added. We introduce EVOHARNESSBENCH, a benchmark for evaluating agents under co
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