Is it agentic enough? Benchmarking open models on your own tooling

Is it agentic enough? Benchmarking open models on your own tooling Benchmarking open models on your own tooling Published June 18, 2026 Update on GitHub Upvote 23 Lysandre lysandre Nathan Habib SaylorTwift Pedro Cuenca pcuenq Benchmarking transformers revisions across different metrics This is a human-made, agent-focused blogpost. Coding agents increasingly work with our software instead of us: describe a task, and the agent picks the library, writes the calls, runs them, and debugs its own mistakes.

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• Benchmarking open models on your own tooling Published June 18, 2026 Update on GitHub Upvote 23 Lysandre lysandre Nathan Habib SaylorTwift Pedro Cuenca pcuenq Benchmarking transformers revisions across different metrics This is a human-made, agent-focused blogpost.
• Coding agents increasingly work with our software instead of us: describe a task, and the agent picks the library, writes the calls, runs them, and debugs its own mistakes.
• When the library gets in the way, it will happily bypass it and rewrite the logic from scratch.
• This introduces a new concept in library development: the code should not only be correct and fast, but should be designed so that an agent can drive it effectively.
• A clunky API or stale docs annoy us developers, but it now also sends the agent down a longer, more expensive path.
• Most benchmarks just look at the final answer.