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