Is it agentic enough? Benchmarking open models on your own tooling
The article discusses the importance of designing software tools for agents, emphasizing that optimization should focus on efficiency and performance. It introduces a benchmarking tool that evaluates not only the final result but also the effort required, including metrics like token usage, latency, and error rates. The tool tests how changes in the library, such as CLI improvements and better error messages, affect agent performance, revealing that the skill tier reduces task time and tokens but increases consumption due to CLI implementation. The study highlights the trade-offs between performance benefits for larger models and the need for smaller models to rely on memorized API patterns.
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