> It appears newer models are better at narive harness tool calls and worse at custom tools that look similar to default tools.

This is true. For example, the other day I was doing some testing with DeepSeek-V4-Flash-0731, and I gave it a tool called "EditFile" with "old_content" argument. It ignored the tool definition and called it with "old_string" instead, presumably because it was trained on a bunch of Claude Code traces.

So either make sure your tools look nothing like the tools in the popular harnesses, or make them similar.

Doesn’t that have more to do with the templating of tool-calls and how using them are presented to the models?

Or is that just why my model likes to break out of the sandbox, going strait to exec shell command and editing files using python on the cli?

I’m not really a dev, so hefty pinch of salt with this take, but doesn’t this feel like we’re just inventing new “fuzzy” regex with much more required compute?

This is correct. People seem to get the wrong idea about why agentic coding is even a thing in 2026. The naive AI techno optimist which has basically displaced the vast majority of opinions on HN, thinks that the models got "smarter" [0]. No, the training distribution shifted towards training on agentic sessions which made certain forms of agentic coding "in-distribution".

We are still witnessing the same underlying problems of transformers.

[0] Think back to all the publicity stunts like the Hugging Face. They are meant to convince you that the agents have somehow progressed past the transformer limitations when those publicity stunts are actually expressions of transformer limitations.