bkotrys

That’s a good example, I believe it could be similar case like with RAG. I didn’t hear for a while about any recommendations to use RAG.

Thanks, this looks useful. The subagent and tool-use breakdown is exactly the kind of visibility I was looking for. I will give it a try.

I don’t think subagents are the problem. Uncontrolled fan-out is. Separate contexts and independent analysis are extremely useful for larger tasks. In my experience, Pro is far too limited for sustained programming, although that probably depends a lot on the kind of engineering work you do.

Fable is great, but overkill for most tasks. Sonnet and Opus are already good enough for the execution work. I find Fable much more useful for planning, orchestration, advising, and judgment.

The audit point is especially interesting. Is your Claude token-audit tool available anywhere?

Very similar experience. The review UI might actually be the most important part of the whole pipeline. Does the confidence ranking match what you end up rejecting?

At the shell level, the agent does not need to know about Bash. The contents of "$OUT" are passed to echo as one argument, then sent to claude-mem-save over stdin. Bash does not re-evaluate them as shell code. printf '%s\n' "$OUT" would be more robust than echo. Whether claude-mem-save can use arbitrary output is a separate question.

That can work, especially for privacy or repetitive tasks. My experiment focused on subscriptions I already had, but local models are a natural addition to the routing layer.

Agreed. My goal is not fewer dead ends, but fewer repeated dead ends.

Thanks for sharing. That’s a very interesting direction. I will take a look.

Good point, thanks. Of course I was not able to fully eliminate hallucinations, but significantly reduce

looking promising, will give it a try! thanks for sharing

Try to mix both subscribtions and take the best from both providers. In my opinion, we should not just one over another but understand the power of both and mix them to reach the better results.

definitely something that will come sooner than later. In my view, adaption of model routing / model orchestration and mixing providers is just natural next step.

Yeah, that was pretty much my experience. The models weren’t lacking knowledge as much as discipline. Without a good workflow, they will most likely spend thousands of tokens exploring dead ends.

Thank you, glad you found the article useful!