Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows
📰 ArXiv cs.AI
Learn how to evaluate LLM agents with Claw-Eval-Live, a live benchmark for evolving real-world workflows, and improve their performance in end-to-end tasks
Action Steps
- Implement Claw-Eval-Live in your workflow to evaluate LLM agents
- Separate the signal layer from the task layer to enable refreshable benchmarks
- Use Claw-Eval-Live to assess agent performance in end-to-end tasks across software tools and services
- Compare the results of different LLM agents to identify areas for improvement
- Integrate Claw-Eval-Live into your development pipeline to continuously evaluate and refine agent performance
Who Needs to Know This
AI engineers and researchers can use Claw-Eval-Live to assess and enhance the capabilities of LLM agents in real-world workflows, while product managers can utilize it to inform product development and strategy
Key Insight
💡 Claw-Eval-Live enables the evaluation of LLM agents in real-world workflows, allowing for more accurate assessment of their capabilities and identification of areas for improvement
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🚀 Introducing Claw-Eval-Live: a live benchmark for evaluating LLM agents in evolving real-world workflows 🤖
Key Takeaways
Learn how to evaluate LLM agents with Claw-Eval-Live, a live benchmark for evolving real-world workflows, and improve their performance in end-to-end tasks
Full Article
Title: Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows
Abstract:
arXiv:2604.28139v1 Announce Type: cross Abstract: LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grade mainly the final response, making it difficult to evaluate agents against evolving workflow demand or verify whether a task was executed. We introduce Claw-Eval-Live, a live benchmark for workflow agents that separates a refreshable signal layer,
Abstract:
arXiv:2604.28139v1 Announce Type: cross Abstract: LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grade mainly the final response, making it difficult to evaluate agents against evolving workflow demand or verify whether a task was executed. We introduce Claw-Eval-Live, a live benchmark for workflow agents that separates a refreshable signal layer,
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