> Claude code does some of this by handing off the "explore" agent work to haiku.

That is not handing off to a specialized model, its just handing off to a lighter and interior model (compared to the parent model). That by itself can create issues like the lighter model not capturing all the data that the parent needs.

The idea is that we get specialized models that are better then general purpose models. But its rare for a specialized model to beat a strong general model.

There is a reason why we hear less about this idea of smaller expert models, because large strong models to the tasks just as good.

And if the tasks is repetitive to the point that specialization is useful, you can get into a situation that your better off having a program written for that reputative nature, then delegating to other models. And then have the main strong model, deal with the (semi)cleaned up data.

There are! Chroma has Context1, SID has SID-1, and you'd actually be surprised at how easy it is to post-train your own with pretty good pass@ recall@ ndcg@ etc.

There's also Hornet who have shared some interesting talks & blogs lately. I don't know that I'd exclusively use agents for retrieval the way Neon outlines here as well. I think distillation similar to what ZeroEntropy has done for bespoke retrieval & reranking with _some_ agent manipulation on top-k results works better (IME).

> Claude code does some of this by handing off the "explore" agent work to haiku

This is no longer necessarily true. As of 2.1.198 [0] (released July 1st): "The built-in Explore agent now inherits the main session’s model (capped at opus) instead of running on haiku"

[0] https://code.claude.com/docs/en/changelog#2-1-198

I feel like the future is people building applications with tightly integrated LLMs that work hand in hand with the application's own lifecycle and code.

I also didn't realize that people were using agentic harnesses for search, it's an interesting idea. If the context length is short enough it should be fairly cheap compared to running "normal" agentic coding workloads where you have O(100k) context length for doing almost anything.

Models keep on improving though, so doesn't fine tuning become an ongoing task with ongoing maintenance burden?

> There is so much opportunity for purpose built models like this.

OpenAI etc could themselves do this, and maybe they already do? Where the public-facing interface delegates to multiple little goblins behinds the scenes

Considering the state of the field ( RAG/retrieval/evaluation) I have 0 trust in it, even more if it's closed source with bullshit claim like that.

Everything is vibe sloped to death, and dead after a few months to a couple of years (and not hard to be 100 cheaper than GPT-5.6 sol ... DS is basically free and I guess already 100 times cheaper or more, and here another slope ).

I use detailed project files. It has data regarding the project and subtasks as well as task status. It doesn’t depend on agent context and it’s managed to keep the agent on track. Feature creep with the new models is a very real issue. Capturing principles and how to reconcile tasks helps too. Even today it brought up a source of truth issue it had detected. There were multiple authorities born out of a patch and it used that principle to highlight and resolve the problem.

Anecdotally, it feels like Opus, Fable, and Sol "get distracted" when you use them for writing code. Great at reasoning and coordination but they will go off on a tangent and refactor half the code base. I only use them for reasoning (of course) and coordinating subagents.

Any data or public links you can share? That surprises me

Keeping track of any AI progress is becoming harder by the day, because there's ambiguity around common/clear/consistent benchmarks. Everything is constantly skewed into favourable directions.

Deepseek flash (especially after the recent update) has to be one of the most slept-on models. Price-performance is ridiculous, and its available on a number of cheap coding subscriptions