Is it agentic enough? Benchmarking open models on your own tooling
We're on a journey to advance and democratize artificial intelligence through open source and open science. Back to Articles a]:hidden"> Is it agentic enough? Benchmarking open models on your own tooling Published June 18, 2026 Update on GitHub Upvote 2 Lysandre lysandre Follow Nathan Habib SaylorTwift Follow Pedro Cuenca pcuenq Follow Benchmarking transformers revisions across different metrics This is a human-made, agent-focused blogpost.
Key Takeaways
- 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.
- We measured exactly that, using as our case study.
- They need to be structured in a way that the agent has rapid access to the useful files and examples.
If you want your tool to work for an agent, then you should test it for agentic-use.
- We wanted to know whether that kind of win generalizes, and whether it could be useful for transformers as well.
Intuition is a powerful tool, but we wanted more evidence before we opened PRs that add several thousand lines of code to such a widely used codebase as .
- Our goal with this harness is to evaluate how much work an agent has to do to perform a given task, and whether changes to the library improve performance.
Stats & Key Facts
- #Benchmarking open models on your own tooling Published June 18, 2026 Update on GitHub Upvote 2 Lysandre lysandre Follow Nathan Habib SaylorTwift Follow Pedro Cuenca pcuenq Follow 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. We wanted the whole process instead: not just whether the agent got it right, but how much work it took to get there, and how that shifts across models, library revisions, and tasks.
We measured exactly that, using as our case study. Here, we will introduce a tool specific benchmark focusing on how the answer was found, and provide a simple implementation of one such harness, running entirely on open models driven by the pi coding agent, with the full sweep of models × revisions × tasks fanned out across Hugging Face Jobs so every run sees identical hardware. But, how do you optimize software for agents?
For more details please read the original article at Hugging Face.
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